






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		__UpperCAmelCase     							=						None


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCAmelCase     							=						{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Optional[int]							=  VOCAB_FILES_NAMES
		_snake_case   :   Dict							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Union[str, Any]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Optional[Any]							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   int							=  TaTokenizer

		_snake_case   :   List[int]							=  []



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase=1_0_0    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							int:
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
												UpperCAmelCase_   :				List[str]						=						[f"<extra_id_{i}>" for i in range(_UpperCamelCase       )]
							elif extra_ids > 0 and additional_special_tokens is not None:
												# Check that we have the right number of extra special tokens
												UpperCAmelCase_   :				Union[str, Any]						=						len(set(filter(lambda _UpperCamelCase       : bool('extra_id_' in str(_UpperCamelCase       )       )    ,		_UpperCamelCase       )       )       )
												if extra_tokens != extra_ids:
																	raise ValueError(
																	    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																	    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																	    ' tokens'       )

							super().__init__(
							    _UpperCamelCase    ,		tokenizer_file=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		extra_ids=_UpperCamelCase    ,		additional_special_tokens=_UpperCamelCase    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				str						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True
							UpperCAmelCase_   :				List[str]						=						extra_ids



		@staticmethod
		def  __UpperCAmelCase      (							_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
												UpperCAmelCase_   :				Union[str, Any]						=						TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
												if init_max_model_length is not None and init_max_model_length != max_model_length:
																	return init_max_model_length
												elif init_max_model_length is None:
																	warnings.warn(
																	    'This tokenizer was incorrectly instantiated with a model max length of'
																	    f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																	    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																	    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																	    f" {pretrained_model_name_or_path} automatically truncating your input to"
																	    f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																	    f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																	    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																	    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'    ,		_UpperCamelCase    ,		)

							return max_model_length



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
												    'tokenizer.'       )

							if not os.path.isdir(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				Tuple						=						os.path.join(
							    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCamelCase       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )
												logger.info(f"Copy vocab file to {out_vocab_file}"       )

							return (out_vocab_file,)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Dict						=						token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a
							else:
												UpperCAmelCase_   :				Dict						=						token_ids_a + [self.eos_token_id]
												return self.prefix_tokens + token_ids_a + token_ids_a



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				int						=						[self.eos_token_id]

							if token_ids_a is None:
												return len(token_ids_a + eos       ) * [0]
							return len(token_ids_a + eos + token_ids_a + eos       ) * [0]



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return list(
							    set(filter(lambda _UpperCamelCase       : bool(re.search(r'<extra_id_\d+>'    ,		_UpperCamelCase       )       ) is not None    ,		self.additional_special_tokens       )       )       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return [self.convert_tokens_to_ids(_UpperCamelCase       ) for token in self.get_sentinel_tokens()]














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
__UpperCAmelCase     							=						'.'

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




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

					with open(os.path.join(__snake_case					,	'utils'					,	'tf_ops'					,	'onnx.json'  )  ) as f:
										UpperCAmelCase_   :				Optional[Any]						=						json.load(__snake_case  )['opsets']

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

					with open(__snake_case					,	'rb'  ) as f:
										saved_model.ParseFromString(f.read()  )

					UpperCAmelCase_   :				List[Any]						=						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
					UpperCAmelCase_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

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

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


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

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






