




"""simple docstring"""



import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						)




class      UpperCAmelCase_  (						unittest.TestCase							):




				def __init__(   self						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int=13     ,			snake_case_						:	Optional[int]=7     ,			snake_case_						:	str=True     ,			snake_case_						:	Any=True     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	Union[str, Any]=99     ,			snake_case_						:	Any=32     ,			snake_case_						:	Tuple=5     ,			snake_case_						:	Optional[Any]=4     ,			snake_case_						:	int=37     ,			snake_case_						:	str="gelu"     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	Optional[int]=0.1     ,			snake_case_						:	int=512     ,			snake_case_						:	str=16     ,			snake_case_						:	str=2     ,			snake_case_						:	int=0.02     ,			snake_case_						:	Optional[Any]=4     ,			)				->							Any:




					'''simple docstring'''

					A__	=    parent
					A__	=    batch_size
					A__	=    seq_length
					A__	=    is_training
					A__	=    use_attention_mask
					A__	=    use_token_type_ids
					A__	=    use_labels
					A__	=    vocab_size
					A__	=    hidden_size
					A__	=    num_hidden_layers
					A__	=    num_attention_heads
					A__	=    intermediate_size
					A__	=    hidden_act
					A__	=    hidden_dropout_prob
					A__	=    attention_probs_dropout_prob
					A__	=    max_position_embeddings
					A__	=    type_vocab_size
					A__	=    type_sequence_label_size
					A__	=    initializer_range
					A__	=    num_choices




				def 							__magic_name__							(   self						:	Any				)				->							List[Any]:




					'''simple docstring'''

					A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size				)

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

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

					A__	=    RoFormerConfig(
					    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=snake_case_     ,			initializer_range=self.initializer_range     ,			)

					return config, input_ids, token_type_ids, attention_mask




				def 							__magic_name__							(   self						:	List[Any]				)				->							str:




					'''simple docstring'''

					A__	=    self.prepare_config_and_inputs()
					A__,				A__,				A__,				A__	=    config_and_inputs
					A__	=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






@require_flax
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
				lowercase__						 =				True

				lowercase__						 =				(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




					'''simple docstring'''

					A__	=    FlaxRoFormerModelTester(self				)




				@slow
				def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




					'''simple docstring'''

					for model_class_name in self.all_model_classes:
						A__	=    model_class_name.from_pretrained("junnyu/roformer_chinese_small"     ,			from_pt=snake_case_				)
						A__	=    model(np.ones((1, 1)				)				)
						self.assertIsNotNone(snake_case_				)






@require_flax
class      UpperCAmelCase_  (						unittest.TestCase							):




				@slow
				def 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




					'''simple docstring'''

					A__	=    FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"				)
					A__	=    jnp.array([[0, 1, 2, 3, 4, 5]]				)
					A__	=    model(snake_case_				)[0]

					A__	=    50_000

					A__	=    (1, 6, vocab_size)
					self.assertEqual(output.shape     ,			snake_case_				)

					A__	=    jnp.array(
					    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]				)

					self.assertTrue(jnp.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)











"""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()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 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:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

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

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [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
   A__	=    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:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    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]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

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

   A__	=    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__":
      SCREAMING_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.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

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





