






'''simple docstring'''



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE						=	{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_								=			DPRContextEncoderTokenizer





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_								=			DPRQuestionEncoderTokenizer





SCREAMING_SNAKE_CASE						=	collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

SCREAMING_SNAKE_CASE						=	collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


SCREAMING_SNAKE_CASE						=	r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '
@add_start_docstrings(__A				)
class UpperCAmelCase_					:




      """simple docstring"""




      def __call__( self			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Union[bool, str] = False     ,      UpperCAmelCase			:		Union[bool, str] = False     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  BatchEncoding:


       '''simple docstring'''

       if titles is None and texts is None:
        return super().__call__(
            UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      **UpperCAmelCase     ,      )
       elif titles is None or texts is None:
        lowercase					:   Optional[int]				  =titles if texts is None else texts
        return super().__call__(
            UpperCAmelCase     ,      UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      **UpperCAmelCase     ,      )
       lowercase					:   int				  =titles if not isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else [titles]
       lowercase					:   List[str]				  =texts if not isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else [texts]
       lowercase					:   Optional[int]				  =len(UpperCAmelCase		)
       lowercase					:   Dict				  =questions if not isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else [questions] * n_passages
       assert len(UpperCAmelCase		) == len(
           UpperCAmelCase		), f'There should be as many titles than texts but got {len(UpperCAmelCase		)} titles and {len(UpperCAmelCase		)} texts.'
       lowercase					:   Any				  =super().__call__(UpperCAmelCase     ,      UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase		)['''input_ids''']
       lowercase					:   Union[str, Any]				  =super().__call__(UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase		)['''input_ids''']
       lowercase					:   Optional[Any]				  ={
           '''input_ids''': [
               (encoded_question_and_title + encoded_text)[:max_length]
               if max_length is not None and truncation
               else encoded_question_and_title + encoded_text
               for encoded_question_and_title, encoded_text in zip(UpperCAmelCase     ,      UpperCAmelCase		)
           ]
       }
       if return_attention_mask is not False:
        lowercase					:   Any				  =[]
        for input_ids in encoded_inputs["input_ids"]:
         attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
        lowercase					:   Optional[int]				  =attention_mask
       return self.pad(UpperCAmelCase     ,      padding=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		BatchEncoding     ,      UpperCAmelCase			:		DPRReaderOutput     ,      UpperCAmelCase			:		int = 16     ,      UpperCAmelCase			:		int = 64     ,      UpperCAmelCase			:		int = 4     ,      )  ->  List[DPRSpanPrediction]:


       '''simple docstring'''

       lowercase					:   str				  =reader_input['''input_ids''']
       lowercase  ,		lowercase  ,		lowercase					:   str				  =reader_output[:3]
       lowercase					:   Optional[Any]				  =len(UpperCAmelCase		)
       lowercase					:   List[str]				  =sorted(range(UpperCAmelCase		)     ,      reverse=UpperCAmelCase     ,      key=relevance_logits.__getitem__		)
       lowercase					:   List[DPRReaderOutput]				  =[]
       for doc_id in sorted_docs:
        lowercase					:   Optional[int]				  =list(input_ids[doc_id]		)
        # assuming question & title information is at the beginning of the sequence
        lowercase					:   Any				  =sequence_ids.index(self.sep_token_id     ,      2		) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
         lowercase					:   Tuple				  =sequence_ids.index(self.pad_token_id		)
        else:
         lowercase					:   Union[str, Any]				  =len(UpperCAmelCase		)

        lowercase					:   str				  =self._get_best_spans(
            start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,      end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,      max_answer_length=UpperCAmelCase     ,      top_spans=UpperCAmelCase     ,      )
        for start_index, end_index in best_spans:
         start_index += passage_offset
         end_index += passage_offset
         nbest_spans_predictions.append(
             DPRSpanPrediction(
                 span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,      relevance_score=relevance_logits[doc_id]     ,      doc_id=UpperCAmelCase     ,      start_index=UpperCAmelCase     ,      end_index=UpperCAmelCase     ,      text=self.decode(sequence_ids[start_index : end_index + 1]		)     ,      )		)
        if len(UpperCAmelCase		) >= num_spans:
         break
       return nbest_spans_predictions[:num_spans]






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int     ,      )  ->  List[DPRSpanPrediction]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =[]
       for start_index, start_score in enumerate(UpperCAmelCase		):
        for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]		):
         scores.append(((start_index, start_index + answer_length), start_score + end_score)		)
       lowercase					:   List[str]				  =sorted(UpperCAmelCase     ,      key=lambda UpperCAmelCase		: x[1]     ,      reverse=UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =[]
       for (start_index, end_index), score in scores:
        assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
        lowercase					:   Optional[Any]				  =end_index - start_index + 1
        assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
        if any(
            start_index <= prev_start_index <= prev_end_index <= end_index
            or prev_start_index <= start_index <= end_index <= prev_end_index
            for (prev_start_index, prev_end_index) in chosen_span_intervals		):
         continue
        chosen_span_intervals.append((start_index, end_index)		)

        if len(UpperCAmelCase		) == top_spans:
         break
       return chosen_span_intervals





@add_end_docstrings(__A				)
class UpperCAmelCase_					(    __A				,       __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			READER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			READER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_								=			['''input_ids''', '''attention_mask''']
      UpperCamelCase_								=			DPRReaderTokenizer











'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.distilbert.modeling_tf_distilbert import (
          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDistilBertForMaskedLM,
          TFDistilBertForMultipleChoice,
          TFDistilBertForQuestionAnswering,
          TFDistilBertForSequenceClassification,
          TFDistilBertForTokenClassification,
          TFDistilBertModel,
      )
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		List[str]     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =parent
       lowercase					:   Any				  =13
       lowercase					:   Any				  =7
       lowercase					:   Optional[int]				  =True
       lowercase					:   Optional[int]				  =True
       lowercase					:   Tuple				  =False
       lowercase					:   Optional[Any]				  =True
       lowercase					:   Dict				  =99
       lowercase					:   Union[str, Any]				  =32
       lowercase					:   Union[str, Any]				  =2
       lowercase					:   Union[str, Any]				  =4
       lowercase					:   List[str]				  =37
       lowercase					:   str				  ='''gelu'''
       lowercase					:   Dict				  =0.1
       lowercase					:   List[Any]				  =0.1
       lowercase					:   List[str]				  =512
       lowercase					:   Optional[int]				  =16
       lowercase					:   Optional[Any]				  =2
       lowercase					:   List[str]				  =0.0_2
       lowercase					:   Any				  =3
       lowercase					:   Optional[Any]				  =4
       lowercase					:   int				  =None






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Tuple				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

       lowercase					:   Any				  =None
       if self.use_input_mask:
        lowercase					:   List[str]				  =random_attention_mask([self.batch_size, self.seq_length]		)

       lowercase					:   Union[str, Any]				  =None
       lowercase					:   Any				  =None
       lowercase					:   str				  =None
       if self.use_labels:
        lowercase					:   Union[str, Any]				  =ids_tensor([self.batch_size]     ,      self.type_sequence_label_size		)
        lowercase					:   Tuple				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels		)
        lowercase					:   List[Any]				  =ids_tensor([self.batch_size]     ,      self.num_choices		)

       lowercase					:   Dict				  =DistilBertConfig(
           vocab_size=self.vocab_size     ,      dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      hidden_dim=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      )

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =TFDistilBertModel(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}

       lowercase					:   List[str]				  =model(UpperCAmelCase		)

       lowercase					:   str				  =[input_ids, input_mask]

       lowercase					:   Tuple				  =model(UpperCAmelCase		)

       self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[str]				  =TFDistilBertForMaskedLM(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   int				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
       }
       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)		)






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Optional[Any]				  =TFDistilBertForSequenceClassification(UpperCAmelCase		)
       lowercase					:   str				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.num_choices
       lowercase					:   Optional[int]				  =TFDistilBertForMultipleChoice(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   List[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   Tuple				  ={
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
       }
       lowercase					:   Tuple				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Tuple				  =TFDistilBertForTokenClassification(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   str				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)		)






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =self.prepare_config_and_inputs()
       ((lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase))					:   Union[str, Any]				  =config_and_inputs
       lowercase					:   Tuple				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class UpperCAmelCase_					(    __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      UpperCamelCase_								=			(
          {
              '''feature-extraction''': TFDistilBertModel,
              '''fill-mask''': TFDistilBertForMaskedLM,
              '''question-answering''': TFDistilBertForQuestionAnswering,
              '''text-classification''': TFDistilBertForSequenceClassification,
              '''token-classification''': TFDistilBertForTokenClassification,
              '''zero-shot''': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertModelTester(self		)
       lowercase					:   int				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      dim=37		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
        lowercase					:   Union[str, Any]				  =TFDistilBertModel.from_pretrained(UpperCAmelCase		)
        self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		List[str]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
       lowercase					:   Tuple				  =tf.constant([[0, 1, 2, 3, 4, 5]]		)
       lowercase					:   List[Any]				  =model(UpperCAmelCase		)[0]

       lowercase					:   str				  =[1, 6, 768]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

       lowercase					:   Optional[int]				  =tf.constant(
           [
               [
                   [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                   [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                   [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)



