

'''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


lowerCAmelCase__      		=			logging.get_logger(__name__)

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

lowerCAmelCase__      		=			{
    '''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'''
        ),
    },
}
lowerCAmelCase__      		=			{
    '''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'''
        ),
    },
}
lowerCAmelCase__      		=			{
    '''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'''
        ),
    },
}

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


lowerCAmelCase__      		=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase__      		=			{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase__      		=			{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Dict           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Dict           =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						List[Any]           =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						str           =    DPRContextEncoderTokenizer






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						int           =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Tuple           =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						Any           =    DPRQuestionEncoderTokenizer
lowerCAmelCase__      		=			collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


lowerCAmelCase__      		=			R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''






@add_start_docstrings(lowerCamelCase__   )
class     lowercase_			:




  """simple docstring"""




  def __call__(   self     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[str] = None      ,lowercase__     :  Optional[str] = None      ,lowercase__     :  Union[bool, str] = False      ,lowercase__     :  Union[bool, str] = False      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  Optional[Union[str, TensorType]] = None      ,lowercase__     :  Optional[bool] = None      ,**lowercase__     :  str      ,):
   if titles is None and texts is None:
    return super().__call__(
        lowercase__      ,padding=lowercase__      ,truncation=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__      ,return_attention_mask=lowercase__      ,**lowercase__      ,)
   elif titles is None or texts is None:
    __lowercase													=				titles if texts is None else texts
    return super().__call__(
        lowercase__      ,lowercase__      ,padding=lowercase__      ,truncation=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__      ,return_attention_mask=lowercase__      ,**lowercase__      ,)
   __lowercase													=				titles if not isinstance(lowercase__      ,lowercase__    ) else [titles]
   __lowercase													=				texts if not isinstance(lowercase__      ,lowercase__    ) else [texts]
   __lowercase													=				len(lowercase__    )
   __lowercase													=				questions if not isinstance(lowercase__      ,lowercase__    ) else [questions] * n_passages
   assert len(lowercase__    ) == len(
       lowercase__    ), F"There should be as many titles than texts but got {len(lowercase__    )} titles and {len(lowercase__    )} texts."
   __lowercase													=				super().__call__(lowercase__      ,lowercase__      ,padding=lowercase__      ,truncation=lowercase__    )['''input_ids''']
   __lowercase													=				super().__call__(lowercase__      ,add_special_tokens=lowercase__      ,padding=lowercase__      ,truncation=lowercase__    )['''input_ids''']
   __lowercase													=				{
       '''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(lowercase__      ,lowercase__    )
       ]
   }
   if return_attention_mask is not False:
    __lowercase													=				[]
    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													=				attention_mask
   return self.pad(lowercase__      ,padding=lowercase__      ,max_length=lowercase__      ,return_tensors=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  BatchEncoding      ,lowercase__     :  DPRReaderOutput      ,lowercase__     :  int = 1_6      ,lowercase__     :  int = 6_4      ,lowercase__     :  int = 4      ,):
   __lowercase													=				reader_input['''input_ids''']
   __lowercase						,			__lowercase						,			__lowercase													=				reader_output[:3]
   __lowercase													=				len(lowercase__    )
   __lowercase													=				sorted(range(lowercase__    )      ,reverse=lowercase__      ,key=relevance_logits.__getitem__    )
   __lowercase													=				[]
   for doc_id in sorted_docs:
    __lowercase													=				list(input_ids[doc_id]    )
    # assuming question & title information is at the beginning of the sequence
    __lowercase													=				sequence_ids.index(self.sep_token_id      ,2    ) + 1  # second sep id
    if sequence_ids[-1] == self.pad_token_id:
     __lowercase													=				sequence_ids.index(self.pad_token_id    )
    else:
     __lowercase													=				len(lowercase__    )

    __lowercase													=				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=lowercase__      ,top_spans=lowercase__      ,)
    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=lowercase__      ,start_index=lowercase__      ,end_index=lowercase__      ,text=self.decode(sequence_ids[start_index : end_index + 1]    )      ,)    )
    if len(lowercase__    ) >= num_spans:
     break
   return nbest_spans_predictions[:num_spans]






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[int]      ,lowercase__     :  List[int]      ,lowercase__     :  int      ,lowercase__     :  int      ,):
   __lowercase													=				[]
   for start_index, start_score in enumerate(lowercase__    ):
    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													=				sorted(lowercase__      ,key=lambda lowercase__    : x[1]      ,reverse=lowercase__    )
   __lowercase													=				[]
   for (start_index, end_index), score in scores:
    assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
    __lowercase													=				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(lowercase__    ) == top_spans:
     break
   return chosen_span_intervals






@add_end_docstrings(lowerCamelCase__   )
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Any           =    READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE					:						Tuple           =    ['input_ids', 'attention_mask']
  SCREAMING_SNAKE_CASE					:						Dict           =    DPRReaderTokenizer




'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

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 import (
     TFFunnelBaseModel,
     TFFunnelForMaskedLM,
     TFFunnelForMultipleChoice,
     TFFunnelForPreTraining,
     TFFunnelForQuestionAnswering,
     TFFunnelForSequenceClassification,
     TFFunnelForTokenClassification,
     TFFunnelModel,
 )






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  List[Any]=True      ,lowercase__     :  str=True      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Tuple=9_9      ,lowercase__     :  List[str]=[1, 1, 2]      ,lowercase__     :  List[Any]=1      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  int=4      ,lowercase__     :  Tuple=8      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  str="gelu_new"      ,lowercase__     :  Dict=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  Tuple=5_1_2      ,lowercase__     :  Any=3      ,lowercase__     :  List[str]=0.0_2      ,lowercase__     :  List[Any]=3      ,lowercase__     :  List[Any]=4      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  Tuple=False      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				block_sizes
   __lowercase													=				num_decoder_layers
   __lowercase													=				d_model
   __lowercase													=				n_head
   __lowercase													=				d_head
   __lowercase													=				d_inner
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				2
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope
   __lowercase													=				initializer_std

   # Used in the tests to check the size of the first attention layer
   __lowercase													=				n_head
   # Used in the tests to check the size of the first hidden state
   __lowercase													=				self.d_model
   # Used in the tests to check the number of output hidden states/attentions
   __lowercase													=				sum(self.block_sizes    ) + (0 if base else self.num_decoder_layers)
   # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
   # the last hidden state of the first block (which is the first hidden state of the decoder).
   if not base:
    __lowercase													=				self.num_hidden_layers + 2






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

   __lowercase													=				None
   if self.use_input_mask:
    __lowercase													=				random_attention_mask([self.batch_size, self.seq_length]    )

   __lowercase													=				None
   if self.use_token_type_ids:
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size    )

   __lowercase													=				None
   __lowercase													=				None
   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
    __lowercase													=				ids_tensor([self.batch_size]      ,self.num_choices    )

   __lowercase													=				FunnelConfig(
       vocab_size=self.vocab_size      ,block_sizes=self.block_sizes      ,num_decoder_layers=self.num_decoder_layers      ,d_model=self.d_model      ,n_head=self.n_head      ,d_head=self.d_head      ,d_inner=self.d_inner      ,hidden_act=self.hidden_act      ,hidden_dropout=self.hidden_dropout      ,attention_dropout=self.attention_dropout      ,activation_dropout=self.activation_dropout      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_std=self.initializer_std      ,)

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
   )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,):
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 3, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelForPreTraining(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				TFFunnelForMaskedLM(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForSequenceClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_choices
   __lowercase													=				TFFunnelForMultipleChoice(config=lowercase__    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				{
       '''input_ids''': multiple_choice_inputs_ids,
       '''attention_mask''': multiple_choice_input_mask,
       '''token_type_ids''': multiple_choice_token_type_ids,
   }
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForTokenClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Any      ,):
   __lowercase													=				TFFunnelForQuestionAnswering(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_tf
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    (
      (
          TFFunnelModel,
          TFFunnelForMaskedLM,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      {
          'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
          'fill-mask': TFFunnelForMaskedLM,
          'question-answering': TFFunnelForQuestionAnswering,
          'text-classification': TFFunnelForSequenceClassification,
          'token-classification': TFFunnelForTokenClassification,
          'zero-shot': TFFunnelForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__    )






@require_tf
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
  )
  SCREAMING_SNAKE_CASE					:						Dict           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self      ,base=lowercase__    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_base_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase__    )

