






'''simple docstring'''







import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_A	:			Optional[Any]         =logging.get_logger(__name__)


_A	:			int         ={name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Optional[int]:
							if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
														raise ValueError(f'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()      )}.'''      )

							if tokenizer_name is None:
														lowerCamelCase__					:	Optional[Any]         =       TOKENIZER_CLASSES
							else:
														lowerCamelCase__					:	Dict         =       {tokenizer_name: getattr(UpperCamelCase				,			tokenizer_name + """Fast"""      )}

							logger.info(f'''Loading tokenizer classes: {tokenizer_names}'''      )

							for tokenizer_name in tokenizer_names:
														lowerCamelCase__					:	Tuple         =       TOKENIZER_CLASSES[tokenizer_name]

														lowerCamelCase__					:	Optional[int]         =       True
														if checkpoint_name is None:
																					lowerCamelCase__					:	Any         =       list(tokenizer_class.max_model_input_sizes.keys()      )
														else:
																					lowerCamelCase__					:	Any         =       [checkpoint_name]

														logger.info(f'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''      )

														for checkpoint in checkpoint_names:
																					logger.info(f'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''      )

																					# Load tokenizer
																					lowerCamelCase__					:	List[str]         =       tokenizer_class.from_pretrained(UpperCamelCase				,			force_download=UpperCamelCase      )

																					# Save fast tokenizer
																					logger.info(f'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''      )

																					# For organization names we create sub-directories
																					if "/" in checkpoint:
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       checkpoint.split("""/"""      )
																												lowerCamelCase__					:	Union[str, Any]         =       os.path.join(UpperCamelCase				,			UpperCamelCase      )
																					elif add_prefix:
																												lowerCamelCase__					:	Optional[int]         =       checkpoint
																												lowerCamelCase__					:	Optional[Any]         =       dump_path
																					else:
																												lowerCamelCase__					:	str         =       None
																												lowerCamelCase__					:	Union[str, Any]         =       dump_path

																					logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''      )

																					if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()      )[0]:
																												lowerCamelCase__					:	Tuple         =       list(tokenizer.pretrained_vocab_files_map.values()      )[0][checkpoint]
																												lowerCamelCase__					:	Optional[Any]         =       file_path.split(UpperCamelCase      )[-1][0]
																												if next_char == "/":
																																			lowerCamelCase__					:	Optional[Any]         =       os.path.join(UpperCamelCase				,			UpperCamelCase      )
																																			lowerCamelCase__					:	List[Any]         =       None

																												logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''      )

																					lowerCamelCase__					:	Any         =       tokenizer.save_pretrained(
																					    UpperCamelCase				,			legacy_format=UpperCamelCase				,			filename_prefix=UpperCamelCase      )
																					logger.info(f'''=> File names {file_names}'''      )

																					for file_name in file_names:
																												if not file_name.endswith("""tokenizer.json"""      ):
																																			os.remove(UpperCamelCase      )
																																			logger.info(f'''=> removing {file_name}'''      )


if __name__ == "__main__":
			_A	:			Union[str, Any]         =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
			)
			parser.add_argument(
			    '''--tokenizer_name''',
			    default=None,
			    type=str,
			    help=(
			        F'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
			        '''download and convert all the checkpoints from AWS.'''
			    ),
			)
			parser.add_argument(
			    '''--checkpoint_name''',
			    default=None,
			    type=str,
			    help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
			)
			parser.add_argument(
			    '''--force_download''',
			    action='''store_true''',
			    help='''Re-download checkpoints.''',
			)
			_A	:			Optional[int]         =parser.parse_args()

			convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)









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  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

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

								snake_case_				=							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=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							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  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





