"""simple docstring"""


import unittest

from transformers import BigBirdConfig, 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

    from transformers.models.big_bird.modeling_flax_big_bird import (
        FlaxBigBirdForCausalLM,
        FlaxBigBirdForMaskedLM,
        FlaxBigBirdForMultipleChoice,
        FlaxBigBirdForPreTraining,
        FlaxBigBirdForQuestionAnswering,
        FlaxBigBirdForSequenceClassification,
        FlaxBigBirdForTokenClassification,
        FlaxBigBirdModel,
    )


class        a				(		unittest.TestCase   ):


    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Any=2   ,				__lowerCAmelCase	:  Tuple=56   ,				__lowerCAmelCase	:  Any=True   ,				__lowerCAmelCase	:  Dict=True   ,				__lowerCAmelCase	:  Dict=True   ,				__lowerCAmelCase	:  Dict=True   ,				__lowerCAmelCase	:  Union[str, Any]=99   ,				__lowerCAmelCase	:  str=32   ,				__lowerCAmelCase	:  str=2   ,				__lowerCAmelCase	:  List[Any]=2   ,				__lowerCAmelCase	:  Dict=7   ,				__lowerCAmelCase	:  Optional[int]="gelu_new"   ,				__lowerCAmelCase	:  Tuple=0.1   ,				__lowerCAmelCase	:  Union[str, Any]=0.1   ,				__lowerCAmelCase	:  Optional[Any]=512   ,				__lowerCAmelCase	:  int=16   ,				__lowerCAmelCase	:  List[Any]=2   ,				__lowerCAmelCase	:  Union[str, Any]=0.02   ,				__lowerCAmelCase	:  Tuple=4   ,				__lowerCAmelCase	:  Any="block_sparse"   ,				__lowerCAmelCase	:  int=True   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  Tuple=2   ,				__lowerCAmelCase	:  int=3   ,				):
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      seq_length
      _UpperCAmelCase						 =      is_training
      _UpperCAmelCase						 =      use_attention_mask
      _UpperCAmelCase						 =      use_token_type_ids
      _UpperCAmelCase						 =      use_labels
      _UpperCAmelCase						 =      vocab_size
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_attention_heads
      _UpperCAmelCase						 =      intermediate_size
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob
      _UpperCAmelCase						 =      max_position_embeddings
      _UpperCAmelCase						 =      type_vocab_size
      _UpperCAmelCase						 =      type_sequence_label_size
      _UpperCAmelCase						 =      initializer_range
      _UpperCAmelCase						 =      num_choices

      _UpperCAmelCase						 =      rescale_embeddings
      _UpperCAmelCase						 =      attention_type
      _UpperCAmelCase						 =      use_bias
      _UpperCAmelCase						 =      block_size
      _UpperCAmelCase						 =      num_random_blocks


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)

      _UpperCAmelCase						 =      None
      if self.use_attention_mask:
        _UpperCAmelCase						 =      random_attention_mask([self.batch_size, self.seq_length]					)

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

      _UpperCAmelCase						 =      BigBirdConfig(
          vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=__lowerCAmelCase   ,				initializer_range=self.initializer_range   ,				attention_type=self.attention_type   ,				block_size=self.block_size   ,				num_random_blocks=self.num_random_blocks   ,				use_bias=self.use_bias   ,				rescale_embeddings=self.rescale_embeddings   ,				)

      return config, input_ids, token_type_ids, attention_mask


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      config_and_inputs
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """token_type_ids""": token_type_ids,
          """attention_mask""": attention_mask,
      }
      return config, inputs_dict



@require_flax
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[Any]     							=							(
        (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdModel,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
        )
        if is_flax_available()
        else ()
    )

    _snake_case     :  Optional[Any]     							=							False
    _snake_case     :  Tuple     							=							False


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      FlaxBigBirdModelTester(self					)


    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def      lowerCAmelCase_  (					self	:  List[Any]					):
      super().test_from_pretrained_save_pretrained()


    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      super().test_from_pretrained_with_no_automatic_init()


    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def      lowerCAmelCase_  (					self	:  List[Any]					):
      super().test_no_automatic_init()


    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def      lowerCAmelCase_  (					self	:  Any					):
      super().test_hidden_states_output()


    @slow
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      for model_class_name in self.all_model_classes:
        _UpperCAmelCase						 =      model_class_name.from_pretrained("""google/bigbird-roberta-base"""					)
        self.assertIsNotNone(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      if self.test_attn_probs:
        super().test_attention_outputs()


    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__					):
          _UpperCAmelCase						 =      self._prepare_for_class(__lowerCAmelCase   ,				__lowerCAmelCase					)
          _UpperCAmelCase						 =      model_class(__lowerCAmelCase					)

          @jax.jit
          def model_jitted(__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				**__lowerCAmelCase	:  Tuple					):
            return model(input_ids=__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				**__lowerCAmelCase					)

          with self.subTest("""JIT Enabled"""					):
            _UpperCAmelCase						 =      model_jitted(**__lowerCAmelCase					).to_tuple()

          with self.subTest("""JIT Disabled"""					):
            with jax.disable_jit():
              _UpperCAmelCase						 =      model_jitted(**__lowerCAmelCase					).to_tuple()

          self.assertEqual(len(__lowerCAmelCase					)   ,				len(__lowerCAmelCase					)					)
          for jitted_output, output in zip(__lowerCAmelCase   ,				__lowerCAmelCase					):
            self.assertEqual(jitted_output.shape   ,				output.shape					)


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Any=1e-5   ,				__lowerCAmelCase	:  List[str]="outputs"   ,				__lowerCAmelCase	:  Union[str, Any]=None					):
      # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
      # an effort was done to return `attention_probs` (yet to be verified).
      if name.startswith("""outputs.attentions"""					):
        return
      else:
        super().check_pt_flax_outputs(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)

"""simple docstring"""


# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  		=						re.compile(r"""([A-Z]+)([A-Z][a-z])""")
UpperCAmelCase__  		=						re.compile(r"""([a-z\d])([A-Z])""")

UpperCAmelCase__  		=						re.compile(r"""(?<!_)_(?!_)""")
UpperCAmelCase__  		=						re.compile(r"""(_{2,})""")

UpperCAmelCase__  		=						r"""^\w+(\.\w+)*$"""

UpperCAmelCase__  		=						r"""<>:/\|?*"""






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _uppercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  _UpperCAmelCase						 =      _lowercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  return name.lower()






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _single_underscore_re.split(lowercase		)
  _UpperCAmelCase						 =      [_multiple_underscores_re.split(lowercase		) for n in name]
  return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase		) if n != """"""		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  return camelcase_to_snakecase(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  if not re.match(_split_re   ,lowercase		):
    raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''		)
  return f'''{filename_prefix_for_name(lowercase		)}-{split}'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  if filetype_suffix:
    prefix += f'''.{filetype_suffix}'''
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
  return f'''{filepath}*'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)

  if shard_lengths:
    _UpperCAmelCase						 =      len(lowercase		)
    _UpperCAmelCase						 =      [f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase		)]
    if filetype_suffix:
      _UpperCAmelCase						 =      [filename + f'''.{filetype_suffix}''' for filename in filenames]
    return filenames
  else:
    _UpperCAmelCase						 =      prefix
    if filetype_suffix:
      filename += f'''.{filetype_suffix}'''
    return [filename]
