import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
		import torch



@is_pipeline_test
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		_snake_case   :   List[Any]							=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				int						=						TextaTextGenerationPipeline(model=_lowerCamelCase    ,		tokenizer=_lowerCamelCase       )
							return generator, ["Something to write", "Something else"]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						generator('Something there'       )
							self.assertEqual(_lowerCamelCase    ,		[{'generated_text': ANY(_lowerCamelCase       )}]       )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]['generated_text'].startswith('Something there'       )       )

							UpperCAmelCase_   :				Union[str, Any]						=						generator(['This is great !', 'Something else']    ,		num_return_sequences=2    ,		do_sample=_lowerCamelCase       )
							self.assertEqual(
							    _lowerCamelCase    ,		[
							        [{'generated_text': ANY(_lowerCamelCase       )}, {'generated_text': ANY(_lowerCamelCase       )}],
							        [{'generated_text': ANY(_lowerCamelCase       )}, {'generated_text': ANY(_lowerCamelCase       )}],
							    ]    ,		)

							UpperCAmelCase_   :				str						=						generator(
							    ['This is great !', 'Something else']    ,		num_return_sequences=2    ,		batch_size=2    ,		do_sample=_lowerCamelCase       )
							self.assertEqual(
							    _lowerCamelCase    ,		[
							        [{'generated_text': ANY(_lowerCamelCase       )}, {'generated_text': ANY(_lowerCamelCase       )}],
							        [{'generated_text': ANY(_lowerCamelCase       )}, {'generated_text': ANY(_lowerCamelCase       )}],
							    ]    ,		)

							with self.assertRaises(_lowerCamelCase       ):
												generator(4       )



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				str						=						pipeline('text2text-generation'    ,		model='patrickvonplaten/t5-tiny-random'    ,		framework='pt'       )
							# do_sample=False necessary for reproducibility
							UpperCAmelCase_   :				Union[str, Any]						=						generator('Something there'    ,		do_sample=_lowerCamelCase       )
							self.assertEqual(_lowerCamelCase    ,		[{'generated_text': ''}]       )

							UpperCAmelCase_   :				Optional[Any]						=						3
							UpperCAmelCase_   :				str						=						generator(
							    'Something there'    ,		num_return_sequences=_lowerCamelCase    ,		num_beams=_lowerCamelCase    ,		)
							UpperCAmelCase_   :				Any						=						[
							    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
							    {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
							    {'''generated_text''': ''''''},
							]
							self.assertEqual(_lowerCamelCase    ,		_lowerCamelCase       )

							UpperCAmelCase_   :				Dict						=						generator('This is a test'    ,		do_sample=_lowerCamelCase    ,		num_return_sequences=2    ,		return_tensors=_lowerCamelCase       )
							self.assertEqual(
							    _lowerCamelCase    ,		[
							        {'generated_token_ids': ANY(torch.Tensor       )},
							        {'generated_token_ids': ANY(torch.Tensor       )},
							    ]    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						generator.model.config.eos_token_id
							UpperCAmelCase_   :				List[Any]						=						'''<pad>'''
							UpperCAmelCase_   :				str						=						generator(
							    ['This is a test', 'This is a second test']    ,		do_sample=_lowerCamelCase    ,		num_return_sequences=2    ,		batch_size=2    ,		return_tensors=_lowerCamelCase    ,		)
							self.assertEqual(
							    _lowerCamelCase    ,		[
							        [
							            {'generated_token_ids': ANY(torch.Tensor       )},
							            {'generated_token_ids': ANY(torch.Tensor       )},
							        ],
							        [
							            {'generated_token_ids': ANY(torch.Tensor       )},
							            {'generated_token_ids': ANY(torch.Tensor       )},
							        ],
							    ]    ,		)



		@require_tf
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[Any]						=						pipeline('text2text-generation'    ,		model='patrickvonplaten/t5-tiny-random'    ,		framework='tf'       )
							# do_sample=False necessary for reproducibility
							UpperCAmelCase_   :				Any						=						generator('Something there'    ,		do_sample=_lowerCamelCase       )
							self.assertEqual(_lowerCamelCase    ,		[{'generated_text': ''}]       )














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)






