






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)



@require_torch
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						3
							UpperCAmelCase_   :				str						=						2_5_0

							UpperCAmelCase_   :				List[str]						=						ids_tensor((batch_size, length)    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						torch.ones((batch_size, length)    ,		device=_UpperCamelCase    ,		dtype=torch.float       ) / length
							return input_ids, scores



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						self._get_tensors(5       )

							UpperCAmelCase_   :				Optional[int]						=						StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=1_0       ),
							        MaxTimeCriteria(max_time=0.1       ),
							    ]       )

							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						self._get_tensors(9       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self._get_tensors(1_0       )
							self.assertTrue(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				List[Any]						=						MaxLengthCriteria(max_length=1_0       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						self._get_tensors(5       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						self._get_tensors(9       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self._get_tensors(1_0       )
							self.assertTrue(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				int						=						MaxNewTokensCriteria(start_length=5    ,		max_new_tokens=5       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						self._get_tensors(5       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						self._get_tensors(9       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						self._get_tensors(1_0       )
							self.assertTrue(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_   :				Optional[int]						=						StoppingCriteriaList([criteria]       )
							self.assertEqual(criteria_list.max_length    ,		1_0       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						self._get_tensors(5       )

							UpperCAmelCase_   :				Union[str, Any]						=						MaxTimeCriteria(max_time=0.1       )
							self.assertFalse(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )

							UpperCAmelCase_   :				List[str]						=						MaxTimeCriteria(max_time=0.1    ,		initial_timestamp=time.time() - 0.2       )
							self.assertTrue(criteria(_UpperCamelCase    ,		_UpperCamelCase       )       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0       )]       )    ,		1_0       )

							with self.assertWarns(_UpperCamelCase       ):
												validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0       )]       )    ,		1_1       )

							UpperCAmelCase_   :				Tuple						=						validate_stopping_criteria(StoppingCriteriaList()    ,		1_1       )

							self.assertEqual(len(_UpperCamelCase       )    ,		1       )














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)






