





"""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()

_UpperCamelCase					      =   logging.get_logger(__name__)


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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Dict ):


							'''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:
														__lowerCamelCase   :   int       =TOKENIZER_CLASSES
							else:
														__lowerCamelCase   :   Union[str, Any]       ={tokenizer_name: getattr(SCREAMING_SNAKE_CASE     ,    tokenizer_name + '''Fast''' )}

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

							for tokenizer_name in tokenizer_names:
														__lowerCamelCase   :   List[Any]       =TOKENIZER_CLASSES[tokenizer_name]

														__lowerCamelCase   :   Union[str, Any]       =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   :   Optional[Any]       =tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE     ,    force_download=SCREAMING_SNAKE_CASE )

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

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

																					__lowerCamelCase   :   Optional[Any]       =tokenizer.save_pretrained(
																					    SCREAMING_SNAKE_CASE     ,    legacy_format=SCREAMING_SNAKE_CASE     ,    filename_prefix=SCREAMING_SNAKE_CASE )
																					logger.info(F'=> File names {file_names}' )

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


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

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











"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




