






"""simple docstring"""


import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.distilbert.modeling_flax_distilbert import (
     FlaxDistilBertForMaskedLM,
     FlaxDistilBertForMultipleChoice,
     FlaxDistilBertForQuestionAnswering,
     FlaxDistilBertForSequenceClassification,
     FlaxDistilBertForTokenClassification,
     FlaxDistilBertModel,
 )






class 						snake_case  (  unittest.TestCase	):





    def __init__(							self     : List[Any]    ,		A     : Dict    ,		A     : Optional[int]=1_3    ,		A     : Dict=7    ,		A     : Union[str, Any]=True    ,		A     : Union[str, Any]=True    ,		A     : Optional[Any]=True    ,		A     : str=True    ,		A     : Any=9_9    ,		A     : Dict=3_2    ,		A     : Union[str, Any]=5    ,		A     : Tuple=4    ,		A     : List[Any]=3_7    ,		A     : Tuple="gelu"    ,		A     : str=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : Optional[int]=5_1_2    ,		A     : int=1_6    ,		A     : Any=2    ,		A     : List[str]=0.02    ,		A     : int=4    ,		):


      '''simple docstring'''




      a					:      Dict    =       parent
      a					:      List[Any]    =       batch_size
      a					:      Optional[Any]    =       seq_length
      a					:      Tuple    =       is_training
      a					:      int    =       use_attention_mask
      a					:      Optional[int]    =       use_token_type_ids
      a					:      Any    =       use_labels
      a					:      List[Any]    =       vocab_size
      a					:      Optional[int]    =       hidden_size
      a					:      List[Any]    =       num_hidden_layers
      a					:      Union[str, Any]    =       num_attention_heads
      a					:      Any    =       intermediate_size
      a					:      Tuple    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Dict    =       attention_probs_dropout_prob
      a					:      Any    =       max_position_embeddings
      a					:      Tuple    =       type_vocab_size
      a					:      Any    =       type_sequence_label_size
      a					:      str    =       initializer_range
      a					:      Dict    =       num_choices





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Dict    =       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size      )

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

      a					:      Optional[Any]    =       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    ,		tie_weights_=_UpperCAmelCase    ,		)

      return config, input_ids, attention_mask





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




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






@require_flax
class 						snake_case  (  lowerCamelCase_       ,			unittest.TestCase	):
    __magic_name__   					=					(
        (
            FlaxDistilBertModel,
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Any    =       FlaxDistilBertModelTester(self      )





    @slow
    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      for model_class_name in self.all_model_classes:
        a					:      List[Any]    =       model_class_name.from_pretrained('distilbert-base-uncased'      )
        a					:      Optional[Any]    =       model(np.ones((1, 1)      )      )
        self.assertIsNotNone(_UpperCAmelCase      )






@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'      )
      a					:      List[Any]    =       np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]      )
      a					:      Tuple    =       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      a					:      List[str]    =       model(_UpperCAmelCase    ,		attention_mask=_UpperCAmelCase      )[0]
      a					:      List[str]    =       (1, 1_1, 7_6_8)
      self.assertEqual(output.shape    ,		_UpperCAmelCase      )
      a					:      Optional[int]    =       np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]      )

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    ,		_UpperCAmelCase    ,		atol=1E-4      )      )












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




