






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)

class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				def __init__( self     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :List[str]=13  ,     lowerCamelCase__     :Any=7  ,     lowerCamelCase__     :Dict=True  ,     lowerCamelCase__     :Union[str, Any]=True  ,     lowerCamelCase__     :Union[str, Any]=True  ,     lowerCamelCase__     :Optional[int]=True  ,     lowerCamelCase__     :List[str]=99  ,     lowerCamelCase__     :Optional[Any]=32  ,     lowerCamelCase__     :Any=5  ,     lowerCamelCase__     :Any=4  ,     lowerCamelCase__     :List[Any]=37  ,     lowerCamelCase__     :Optional[int]="gelu"  ,     lowerCamelCase__     :Tuple=0.1  ,     lowerCamelCase__     :List[Any]=0.1  ,     lowerCamelCase__     :Tuple=5_12  ,     lowerCamelCase__     :int=16  ,     lowerCamelCase__     :Optional[Any]=2  ,     lowerCamelCase__     :Optional[int]=0.02  ,     lowerCamelCase__     :Optional[Any]=4  ,     ):
					UpperCamelCase__ :Union[str, Any]						=      parent
					UpperCamelCase__ :Optional[Any]						=      batch_size
					UpperCamelCase__ :Optional[Any]						=      seq_length
					UpperCamelCase__ :str						=      is_training
					UpperCamelCase__ :int						=      use_attention_mask
					UpperCamelCase__ :Dict						=      use_token_type_ids
					UpperCamelCase__ :int						=      use_labels
					UpperCamelCase__ :List[str]						=      vocab_size
					UpperCamelCase__ :Optional[int]						=      hidden_size
					UpperCamelCase__ :int						=      num_hidden_layers
					UpperCamelCase__ :Optional[int]						=      num_attention_heads
					UpperCamelCase__ :Dict						=      intermediate_size
					UpperCamelCase__ :str						=      hidden_act
					UpperCamelCase__ :List[Any]						=      hidden_dropout_prob
					UpperCamelCase__ :List[str]						=      attention_probs_dropout_prob
					UpperCamelCase__ :Optional[int]						=      max_position_embeddings
					UpperCamelCase__ :Dict						=      type_vocab_size
					UpperCamelCase__ :Dict						=      type_sequence_label_size
					UpperCamelCase__ :Dict						=      initializer_range
					UpperCamelCase__ :Union[str, Any]						=      num_choices







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Any						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )

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

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

					UpperCamelCase__ :Optional[int]						=      RobertaConfig(
					    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  ,     )

					return config, input_ids, token_type_ids, attention_mask







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Optional[Any]						=      self.prepare_config_and_inputs()
					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      config_and_inputs
					UpperCamelCase__ :Union[str, Any]						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
					return config, inputs_dict







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Dict						=      self.prepare_config_and_inputs()
					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      config_and_inputs

					UpperCamelCase__ :Optional[Any]						=      True
					UpperCamelCase__ :Any						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
					UpperCamelCase__ :List[str]						=      ids_tensor([self.batch_size, self.seq_length]  ,     vocab_size=2     )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

@require_flax
class        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Any        =			True

				_snake_case				:   Union[str, Any]        =			(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Optional[int]						=      FlaxRobertaModelTester(self     )







				@slow
				def 				__a   ( self     :Any     ):
					for model_class_name in self.all_model_classes:
						UpperCamelCase__ :int						=      model_class_name.from_pretrained("""roberta-base"""  ,     from_pt=lowerCamelCase__     )
						UpperCamelCase__ :Optional[Any]						=      model(np.ones((1, 1)     )     )
						self.assertIsNotNone(lowerCamelCase__     )





'''simple docstring'''



import argparse
import os
import re


UpperCAmelCase_							:	List[str]	     =		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_							:	Tuple	     =		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCAmelCase_							:	Dict	     =		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  bool = False						)-> str:




     '''simple docstring'''





     with open(_lowerCamelCase ,						'''r''' ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read()

     __snake_case            =	content.split('''\n'''						)
     __snake_case            =	[]
     __snake_case            =	0
     while line_idx < len(_lowerCamelCase						):
          if _re_intro_mapping.search(lines[line_idx]						) is not None:
               __snake_case            =	len(re.search(R'''^(\s*)\S''' ,						lines[line_idx]						).groups()[0]						) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(''' ''' * indent + '''('''						):
                    new_lines.append(lines[line_idx]						)
                    line_idx += 1

               __snake_case            =	[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         __snake_case            =	line_idx
                         while not lines[line_idx].startswith(''' ''' * indent + ''')'''						):
                              line_idx += 1
                         blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]						)						)
                    else:
                         blocks.append(lines[line_idx]						)
                    line_idx += 1

               # Sort blocks by their identifiers
               __snake_case            =	sorted(_lowerCamelCase ,						key=lambda _lowerCamelCase						: _re_identifier.search(_lowerCamelCase						).groups()[0]						)
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]						)
               line_idx += 1

     if overwrite:
          with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as f:
               f.write('''\n'''.join(_lowerCamelCase						)						)
     elif "\n".join(_lowerCamelCase						) != content:
          return True







def       _UpperCamelCase			(_lowerCamelCase				:  bool = False						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	[os.path.join(_lowerCamelCase ,						_lowerCamelCase						) for f in os.listdir(_lowerCamelCase						) if f.endswith('''.py'''						)]
     __snake_case            =	[sort_auto_mapping(_lowerCamelCase ,						overwrite=_lowerCamelCase						) for fname in fnames]

     if not overwrite and any(_lowerCamelCase						):
          __snake_case            =	[f for f, d in zip(_lowerCamelCase ,						_lowerCamelCase						) if d]
          raise ValueError(
              f'''The following files have auto mappings that need sorting: {", ".join(_lowerCamelCase						)}. Run `make style` to fix'''
              ''' this.'''						)


if __name__ == "__main__":
     UpperCAmelCase_							:	str	     =		argparse.ArgumentParser()
     parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
     UpperCAmelCase_							:	List[Any]	     =		parser.parse_args()

     sort_all_auto_mappings(not args.check_only)






