






'''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()

__lowerCamelCase   :					Optional[int]   							=						logging.get_logger(__name__)


__lowerCamelCase   :					Union[str, Any]   							=						{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    str:



			"""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_				:					Union[str, Any]   =       TOKENIZER_CLASSES
			else:
						snake_case_				:					Optional[Any]   =       {tokenizer_name: getattr(__UpperCAmelCase     ,tokenizer_name + "Fast"						)}

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

			for tokenizer_name in tokenizer_names:
						snake_case_				:					Union[str, Any]   =       TOKENIZER_CLASSES[tokenizer_name]

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

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

									snake_case_				:					str   =       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   :					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.''',
		)
		__lowerCamelCase   :					List[Any]   							=						parser.parse_args()

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












'''simple docstring'''




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)




