






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   List[str]						=					PhobertTokenizer
						A     :   Union[str, Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				str:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								snake_case_				:							Any						 =       ["T@@", "i", "I", "R@@", "r", "e@@"]
								snake_case_				:							Union[str, Any]						 =       dict(zip(_SCREAMING_SNAKE_CASE		,				range(len(_SCREAMING_SNAKE_CASE				)				)				)				)
								snake_case_				:							str						 =       ["#version: 0.2", "l à</w>"]
								snake_case_				:							Tuple						 =       {"unk_token": "<unk>"}

								snake_case_				:							Union[str, Any]						 =       os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["vocab_file"]				)
								snake_case_				:							str						 =       os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["merges_file"]				)

								with open(self.vocab_file		,				"w"		,				encoding="utf-8"				) as fp:
										for token in vocab_tokens:
												fp.write(f'''{token} {vocab_tokens[token]}\n'''				)
								with open(self.merges_file		,				"w"		,				encoding="utf-8"				) as fp:
										fp.write("\n".join(_SCREAMING_SNAKE_CASE				)				)






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Any:
								kwargs.update(self.special_tokens_map				)
								return PhobertTokenizer.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       "Tôi là VinAI Research"
								snake_case_				:							Any						 =       "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
								return input_text, output_text






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Union[str, Any]						 =       PhobertTokenizer(self.vocab_file		,				self.merges_file		,				**self.special_tokens_map				)
								snake_case_				:							Optional[Any]						 =       "Tôi là VinAI Research"
								snake_case_				:							Dict						 =       "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
								snake_case_				:							Tuple						 =       tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
								print(_SCREAMING_SNAKE_CASE				)
								self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       tokens + [tokenizer.unk_token]

								snake_case_				:							Optional[Any]						 =       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)		,				_SCREAMING_SNAKE_CASE				)








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
lowercase  :							Tuple			  =    '''.'''

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

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Dict  ,      _a       :			List[str]    ):
		snake_case_				:							Optional[int]						 =       SavedModel()
		snake_case_				:							Any						 =       []

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

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

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

		snake_case_				:							str						 =       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_				:							int						 =       sorted(_a    )
		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(_a    )

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


if __name__ == "__main__":
							lowercase  :							List[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)'''
							)
							lowercase  :							int			  =    parser.parse_args()

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