


"""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()

__snake_case         =					logging.get_logger(__name__)


__snake_case         =					{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						List[str]				)			->		Optional[Any]:


    """simple docstring"""





    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:
        snake_case					:   Optional[int]         =					TOKENIZER_CLASSES
    else:
        snake_case					:   List[str]         =					{tokenizer_name: getattr(lowercase		,							tokenizer_name + "Fast"				)}

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

    for tokenizer_name in tokenizer_names:
        snake_case					:   Any         =					TOKENIZER_CLASSES[tokenizer_name]

        snake_case					:   Union[str, Any]         =					True
        if checkpoint_name is None:
            snake_case					:   Optional[int]         =					list(tokenizer_class.max_model_input_sizes.keys()				)
        else:
            snake_case					:   Tuple         =					[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
            snake_case					:   str         =					tokenizer_class.from_pretrained(lowercase		,							force_download=lowercase				)

            # 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:
                snake_case      ,snake_case					:   List[Any]         =					checkpoint.split("/"				)
                snake_case					:   List[Any]         =					os.path.join(lowercase		,							lowercase				)
            elif add_prefix:
                snake_case					:   Tuple         =					checkpoint
                snake_case					:   Dict         =					dump_path
            else:
                snake_case					:   Optional[Any]         =					None
                snake_case					:   List[str]         =					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]:
                snake_case					:   int         =					list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                snake_case					:   Any         =					file_path.split(lowercase				)[-1][0]
                if next_char == "/":
                    snake_case					:   List[Any]         =					os.path.join(lowercase		,							lowercase				)
                    snake_case					:   Optional[int]         =					None

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

            snake_case					:   Dict         =					tokenizer.save_pretrained(
                lowercase		,							legacy_format=lowercase		,							filename_prefix=lowercase				)
            logger.info(F'=> File names {file_names}'				)

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


if __name__ == "__main__":
  __snake_case         =					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.""",
  )
  __snake_case         =					parser.parse_args()

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









"""simple docstring"""



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			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=13		,			UpperCamelCase__=7		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=99		,			UpperCamelCase__=32		,			UpperCamelCase__=5		,			UpperCamelCase__=4		,			UpperCamelCase__=37		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=16		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=4		,			)			->	Dict:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

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

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

       snake_case					:   Union[str, Any]         =					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=UpperCamelCase__		,			initializer_range=self.initializer_range		,			)

       return config, input_ids, token_type_ids, attention_mask







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs
       snake_case					:   Tuple         =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs

       snake_case					:   int         =					True
       snake_case					:   Optional[int]         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       snake_case					:   Optional[Any]         =					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       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: int													=						True

   __UpperCAmelCase				: Optional[Any]													=						(
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					FlaxRobertaModelTester(self		)







   @slow
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case					:   Dict         =					model_class_name.from_pretrained("roberta-base"		,			from_pt=UpperCamelCase__		)
           snake_case					:   List[str]         =					model(np.ones((1, 1)		)		)
           self.assertIsNotNone(UpperCamelCase__		)





