



import argparse
from collections import defaultdict

import yaml


lowerCamelCase		       =      """docs/source/en/_toctree.yml"""






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							Optional[int]      						= defaultdict(__UpperCamelCase )
				a__	:							str      						= []
				a__	:							Optional[Any]      						= []
				for doc in doc_list:
								if "local" in doc:
												counts[doc["local"]] += 1

								if doc["title"].lower() == "overview":
												overview_doc.append({"local": doc["local"], "title": doc["title"]} )
								else:
												new_doc_list.append(__UpperCamelCase )

				a__	:							Any      						= new_doc_list
				a__	:							Tuple      						= [key for key, value in counts.items() if value > 1]

				a__	:							Optional[Any]      						= []
				for duplicate_key in duplicates:
								a__	:							int      						= list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
								if len(__UpperCamelCase ) > 1:
												raise ValueError(
												    F'{duplicate_key} is present several times in the documentation table of content at '
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others." )
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]} )

				# Add none duplicate-keys
				new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
				a__	:							List[Any]      						= sorted(__UpperCamelCase	,				key=lambda __UpperCamelCase : s["title"].lower() )

				# "overview" gets special treatment and is always first
				if len(__UpperCamelCase ) > 1:
								raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )

				overview_doc.extend(__UpperCamelCase )

				# Sort
				return overview_doc






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase=False )	->						Any:
				with open(__UpperCamelCase	,				encoding="utf-8" ) as f:
								a__	:							Any      						= yaml.safe_load(f.read() )

				# Get to the API doc
				a__	:							Union[str, Any]      						= 0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				a__	:							Optional[Any]      						= content[api_idx]["sections"]

				# Then to the model doc
				a__	:							Optional[int]      						= 0
				while api_doc[scheduler_idx]["title"] != "Schedulers":
								scheduler_idx += 1

				a__	:							Tuple      						= api_doc[scheduler_idx]["sections"]
				a__	:							List[Any]      						= clean_doc_toc(__UpperCamelCase )

				a__	:							List[Any]      						= False
				if new_scheduler_doc != scheduler_doc:
								a__	:							Dict      						= True
								if overwrite:
												a__	:							List[str]      						= new_scheduler_doc

				if diff:
								if overwrite:
												a__	:							List[Any]      						= api_doc
												with open(__UpperCamelCase	,				"w"	,				encoding="utf-8" ) as f:
																f.write(yaml.dump(__UpperCamelCase	,				allow_unicode=__UpperCamelCase ) )
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this." )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase=False )	->						Union[str, Any]:
				with open(__UpperCamelCase	,				encoding="utf-8" ) as f:
								a__	:							List[Any]      						= yaml.safe_load(f.read() )

				# Get to the API doc
				a__	:							Tuple      						= 0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				a__	:							str      						= content[api_idx]["sections"]

				# Then to the model doc
				a__	:							Tuple      						= 0
				while api_doc[pipeline_idx]["title"] != "Pipelines":
								pipeline_idx += 1

				a__	:							Optional[int]      						= False
				a__	:							Dict      						= api_doc[pipeline_idx]["sections"]
				a__	:							Optional[Any]      						= []

				# sort sub pipeline docs
				for pipeline_doc in pipeline_docs:
								if "section" in pipeline_doc:
												a__	:							List[str]      						= pipeline_doc["section"]
												a__	:							Union[str, Any]      						= clean_doc_toc(__UpperCamelCase )
												if overwrite:
																a__	:							Union[str, Any]      						= new_sub_pipeline_doc
								new_pipeline_docs.append(__UpperCamelCase )

				# sort overall pipeline doc
				a__	:							List[str]      						= clean_doc_toc(__UpperCamelCase )

				if new_pipeline_docs != pipeline_docs:
								a__	:							Optional[int]      						= True
								if overwrite:
												a__	:							Optional[Any]      						= new_pipeline_docs

				if diff:
								if overwrite:
												a__	:							List[str]      						= api_doc
												with open(__UpperCamelCase	,				"w"	,				encoding="utf-8" ) as f:
																f.write(yaml.dump(__UpperCamelCase	,				allow_unicode=__UpperCamelCase ) )
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this." )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						lowerCamelCase		       =      parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)







import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase		       =      logging.get_logger(__name__)


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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Dict:
				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__	:							str      						= TOKENIZER_CLASSES
				else:
								a__	:							int      						= {tokenizer_name: getattr(__UpperCamelCase	,				tokenizer_name + "Fast" )}

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

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

								a__	:							Dict      						= True
								if checkpoint_name is None:
												a__	:							Tuple      						= list(tokenizer_class.max_model_input_sizes.keys() )
								else:
												a__	:							List[str]      						= [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__	:							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:
																a__						,   a__	:							Dict      						= checkpoint.split("/" )
																a__	:							Any      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
												elif add_prefix:
																a__	:							int      						= checkpoint
																a__	:							Optional[Any]      						= dump_path
												else:
																a__	:							Dict      						= None
																a__	:							Optional[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]:
																a__	:							Dict      						= list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
																a__	:							Optional[Any]      						= file_path.split(__UpperCamelCase )[-1][0]
																if next_char == "/":
																				a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
																				a__	:							Any      						= None

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

												a__	:							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__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      parser.parse_args()

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


