



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
SCREAMING_SNAKE_CASE__       =      '''.'''

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			A__       =    SavedModel()
			A__       =    []

			with open(os.path.join(_lowercase ,	'utils' ,	'tf_ops' ,	'onnx.json'					)					) as f:
						A__       =    json.load(_lowercase					)["opsets"]

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

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

			A__       =    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
			A__       =    sorted(_lowercase					)
			A__       =    []

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()
							parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
							parser.add_argument(
							    '''--opset''', default=1_2, 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)'''
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




