FM / transformers

/usr/local/lib/python3.6/site-packages/transformers/

/usr/local/lib/python3.6/site-packages/transformers

Name	Size	Mode	Actions
benchmark/	-	0755	rm
commands/	-	0755	rm
data/	-	0755	rm
models/	-	0755	rm
onnx/	-	0755	rm
pipelines/	-	0755	rm
sagemaker/	-	0755	rm
utils/	-	0755	rm
__pycache__/	-	0755	rm
activations.py	6612	0644	edit dl rm
activations_tf.py	4268	0644	edit dl rm
configuration_utils.py	46765	0644	edit dl rm
convert_graph_to_onnx.py	19575	0644	edit dl rm
convert_pytorch_checkpoint_to_tf2.py	16640	0644	edit dl rm
convert_slow_tokenizer.py	37626	0644	edit dl rm
convert_slow_tokenizers_checkpoints_to_fast.py	4954	0644	edit dl rm
convert_tf_hub_seq_to_seq_bert_to_pytorch.py	2899	0644	edit dl rm
debug_utils.py	12887	0644	edit dl rm
deepspeed.py	18883	0644	edit dl rm
dependency_versions_check.py	1767	0644	edit dl rm
dependency_versions_table.py	2390	0644	edit dl rm
dynamic_module_utils.py	19306	0644	edit dl rm
feature_extraction_sequence_utils.py	18074	0644	edit dl rm
feature_extraction_utils.py	26768	0644	edit dl rm
file_utils.py	3930	0644	edit dl rm
generation_beam_constraints.py	19078	0644	edit dl rm
generation_beam_search.py	40145	0644	edit dl rm
generation_flax_logits_process.py	10879	0644	edit dl rm
generation_flax_utils.py	38708	0644	edit dl rm
generation_logits_process.py	30336	0644	edit dl rm
generation_stopping_criteria.py	5509	0644	edit dl rm
generation_tf_logits_process.py	17503	0644	edit dl rm
generation_tf_utils.py	130671	0644	edit dl rm
generation_utils.py	177118	0644	edit dl rm
hf_argparser.py	11901	0644	edit dl rm
image_utils.py	12860	0644	edit dl rm
integrations.py	40203	0644	edit dl rm
keras_callbacks.py	18902	0644	edit dl rm
modelcard.py	35278	0644	edit dl rm
modeling_flax_outputs.py	35514	0644	edit dl rm
modeling_flax_pytorch_utils.py	12296	0644	edit dl rm
modeling_flax_utils.py	37661	0644	edit dl rm
modeling_outputs.py	60327	0644	edit dl rm
modeling_tf_outputs.py	46107	0644	edit dl rm
modeling_tf_pytorch_utils.py	19916	0644	edit dl rm
modeling_tf_utils.py	100832	0644	edit dl rm
modeling_utils.py	137800	0644	edit dl rm
optimization.py	27756	0644	edit dl rm
optimization_tf.py	15722	0644	edit dl rm
processing_utils.py	10559	0644	edit dl rm
py.typed	1	0644	edit dl rm
pytorch_utils.py	1649	0644	edit dl rm
testing_utils.py	48292	0644	edit dl rm
tf_utils.py	1568	0644	edit dl rm
tokenization_utils.py	39867	0644	edit dl rm
tokenization_utils_base.py	170159	0644	edit dl rm
tokenization_utils_fast.py	32909	0644	edit dl rm
trainer.py	143103	0644	edit dl rm
trainer_callback.py	23199	0644	edit dl rm
trainer_pt_utils.py	44764	0644	edit dl rm
trainer_seq2seq.py	10396	0644	edit dl rm
trainer_tf.py	34564	0644	edit dl rm
trainer_utils.py	18509	0644	edit dl rm
training_args.py	68042	0644	edit dl rm
training_args_seq2seq.py	2829	0644	edit dl rm
training_args_tf.py	14267	0644	edit dl rm
__init__.py	171952	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/modeling_tf_outputs.py (46107B)

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from dataclasses import dataclass
from typing import List, Optional, Tuple

import tensorflow as tf

from .utils import ModelOutput

@dataclass
class TFBaseModelOutput(ModelOutput):
    """
    Base class for model's outputs, with potential hidden states and attentions.

Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (`tuple(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

last_hidden_state: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFBaseModelOutputWithPooling(ModelOutput):
    """
    Base class for model's outputs that also contains a pooling of the last hidden states.

Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`):
            Last layer hidden-state of the first token of the sequence (classification token) further processed by a
            Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
            prediction (classification) objective during pretraining.

This output is usually *not* a good summary of the semantic content of the input, you're often better with
            averaging or pooling the sequence of hidden-states for the whole input sequence.
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

last_hidden_state: tf.Tensor = None
    pooler_output: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
    """
    Base class for model's outputs that also contains a pooling of the last hidden states.

This output is usually *not* a good summary of the semantic content of the input, you're often better with
            averaging or pooling the sequence of hidden-states for the whole input sequence.
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
    """

last_hidden_state: tf.Tensor = None
    pooler_output: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    cross_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFBaseModelOutputWithPast(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.

If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
            hidden_size)` is output.
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

last_hidden_state: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFBaseModelOutputWithCrossAttentions(ModelOutput):
    """
    Base class for model's outputs, with potential hidden states and attentions.

Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
    """

last_hidden_state: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    cross_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.

Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
    """

last_hidden_state: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
    cross_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSeq2SeqModelOutput(ModelOutput):
    """
    Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
    decoding.

Args:
        last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the decoder of the model.

Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
            used (see `past_key_values` input) to speed up sequential decoding.
        decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
        decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
        encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the model.
        encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
        encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

last_hidden_state: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    decoder_attentions: Optional[Tuple[tf.Tensor]] = None
    cross_attentions: Optional[Tuple[tf.Tensor]] = None
    encoder_last_hidden_state: Optional[tf.Tensor] = None
    encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    encoder_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFCausalLMOutput(ModelOutput):
    """
    Base class for causal language model (or autoregressive) outputs.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFCausalLMOutputWithPast(ModelOutput):
    """
    Base class for causal language model (or autoregressive) outputs.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFCausalLMOutputWithCrossAttentions(ModelOutput):
    """
    Base class for causal language model (or autoregressive) outputs.

Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
    """

@dataclass
class TFMaskedLMOutput(ModelOutput):
    """
    Base class for masked language models outputs.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
            Masked language modeling (MLM) loss.
        logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSeq2SeqLMOutput(ModelOutput):
    """
    Base class for sequence-to-sequence language models outputs.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
            Language modeling loss.
        logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    decoder_attentions: Optional[Tuple[tf.Tensor]] = None
    cross_attentions: Optional[Tuple[tf.Tensor]] = None
    encoder_last_hidden_state: Optional[tf.Tensor] = None
    encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    encoder_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFNextSentencePredictorOutput(ModelOutput):
    """
    Base class for outputs of models predicting if two sentences are consecutive or not.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `next_sentence_label` is provided):
            Next sentence prediction loss.
        logits (`tf.Tensor` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSequenceClassifierOutput(ModelOutput):
    """
    Base class for outputs of sentence classification models.

Args:
        loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
    """
    Base class for outputs of sequence-to-sequence sentence classification models.

Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the model.
        encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    decoder_attentions: Optional[Tuple[tf.Tensor]] = None
    encoder_last_hidden_state: Optional[tf.Tensor] = None
    encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    encoder_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFMultipleChoiceModelOutput(ModelOutput):
    """
    Base class for outputs of multiple choice models.

Args:
        loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided):
            Classification loss.
        logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
            *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

Classification scores (before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFTokenClassifierOutput(ModelOutput):
    """
    Base class for outputs of token classification models.

Args:
        loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided) :
            Classification loss.
        logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`):
            Classification scores (before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFQuestionAnsweringModelOutput(ModelOutput):
    """
    Base class for outputs of question answering models.

Args:
        loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided):
            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
        start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
            Span-start scores (before SoftMax).
        end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
            Span-end scores (before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

loss: Optional[tf.Tensor] = None
    start_logits: tf.Tensor = None
    end_logits: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
    """
    Base class for outputs of sequence-to-sequence question answering models.

Args:
        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
        start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
            Span-start scores (before SoftMax).
        end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
            Span-end scores (before SoftMax).
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

loss: Optional[tf.Tensor] = None
    start_logits: tf.Tensor = None
    end_logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    decoder_attentions: Optional[Tuple[tf.Tensor]] = None
    encoder_last_hidden_state: Optional[tf.Tensor] = None
    encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
    encoder_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFSequenceClassifierOutputWithPast(ModelOutput):
    """
    Base class for outputs of sentence classification models.

Args:
        loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
            sequence_length, embed_size_per_head)`).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

cmd: