






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=7    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_8    ,		_UpperCamelCase=3_0    ,		_UpperCamelCase=4_0_0    ,		_UpperCamelCase=True    ,		_UpperCamelCase=None    ,		_UpperCamelCase=True    ,		_UpperCamelCase=None    ,		_UpperCamelCase=True    ,		_UpperCamelCase=[0.5, 0.5, 0.5]    ,		_UpperCamelCase=[0.5, 0.5, 0.5]    ,		_UpperCamelCase=False    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=						size if size is not None else {'height': 2_0, 'width': 2_0}
							UpperCAmelCase_   :				Optional[Any]						=						crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
							UpperCAmelCase_   :				List[str]						=						parent
							UpperCAmelCase_   :				List[str]						=						batch_size
							UpperCAmelCase_   :				int						=						num_channels
							UpperCAmelCase_   :				Dict						=						image_size
							UpperCAmelCase_   :				List[str]						=						min_resolution
							UpperCAmelCase_   :				str						=						max_resolution
							UpperCAmelCase_   :				List[Any]						=						do_resize
							UpperCAmelCase_   :				int						=						size
							UpperCAmelCase_   :				Dict						=						do_center_crop
							UpperCAmelCase_   :				int						=						crop_size
							UpperCAmelCase_   :				Dict						=						do_normalize
							UpperCAmelCase_   :				int						=						image_mean
							UpperCAmelCase_   :				Optional[int]						=						image_std
							UpperCAmelCase_   :				str						=						do_reduce_labels



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						load_dataset('hf-internal-testing/fixtures_ade20k'					,	split='test'  )

					UpperCAmelCase_   :				List[Any]						=						Image.open(dataset[0]['file']  )
					UpperCAmelCase_   :				Dict						=						Image.open(dataset[1]['file']  )

					return image, map




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						load_dataset('hf-internal-testing/fixtures_ade20k'					,	split='test'  )

					UpperCAmelCase_   :				Tuple						=						Image.open(ds[0]['file']  )
					UpperCAmelCase_   :				List[Any]						=						Image.open(ds[1]['file']  )
					UpperCAmelCase_   :				Tuple						=						Image.open(ds[2]['file']  )
					UpperCAmelCase_   :				Dict						=						Image.open(ds[3]['file']  )

					return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Any							=  BeitImageProcessor if is_vision_available() else None



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Optional[Any]						=						BeitImageProcessingTester(self       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[int]						=						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_resize'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'size'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_center_crop'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'center_crop'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_normalize'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'image_mean'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'image_std'       )       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Any						=						self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size    ,		{'height': 2_0, 'width': 2_0}       )
							self.assertEqual(image_processor.crop_size    ,		{'height': 1_8, 'width': 1_8}       )
							self.assertEqual(image_processor.do_reduce_labels    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						self.image_processing_class.from_dict(
							    self.image_processor_dict    ,		size=4_2    ,		crop_size=8_4    ,		reduce_labels=_UpperCamelCase       )
							self.assertEqual(image_processor.size    ,		{'height': 4_2, 'width': 4_2}       )
							self.assertEqual(image_processor.crop_size    ,		{'height': 8_4, 'width': 8_4}       )
							self.assertEqual(image_processor.do_reduce_labels    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							pass



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							# Initialize image_processing
							UpperCAmelCase_   :				Tuple						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							UpperCAmelCase_   :				Union[str, Any]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		Image.Image       )

							# Test not batched input
							UpperCAmelCase_   :				List[Any]						=						image_processing(image_inputs[0]    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)

							# Test batched
							UpperCAmelCase_   :				Any						=						image_processing(_UpperCamelCase    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							# Initialize image_processing
							UpperCAmelCase_   :				Any						=						self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							UpperCAmelCase_   :				Dict						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase    ,		numpify=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		np.ndarray       )

							# Test not batched input
							UpperCAmelCase_   :				List[Any]						=						image_processing(image_inputs[0]    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)

							# Test batched
							UpperCAmelCase_   :				Optional[Any]						=						image_processing(_UpperCamelCase    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							# Initialize image_processing
							UpperCAmelCase_   :				Optional[Any]						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							UpperCAmelCase_   :				Tuple						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase    ,		torchify=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		torch.Tensor       )

							# Test not batched input
							UpperCAmelCase_   :				str						=						image_processing(image_inputs[0]    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)

							# Test batched
							UpperCAmelCase_   :				Optional[Any]						=						image_processing(_UpperCamelCase    ,		return_tensors='pt'       ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							# Initialize image_processing
							UpperCAmelCase_   :				Union[str, Any]						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							UpperCAmelCase_   :				Optional[int]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase    ,		torchify=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						[]
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		torch.Tensor       )
												maps.append(torch.zeros(image.shape[-2:]       ).long()       )

							# Test not batched input
							UpperCAmelCase_   :				Tuple						=						image_processing(image_inputs[0]    ,		maps[0]    ,		return_tensors='pt'       )
							self.assertEqual(
							    encoding['pixel_values'].shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(
							    encoding['labels'].shape    ,		(
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(encoding['labels'].dtype    ,		torch.long       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 2_5_5       )

							# Test batched
							UpperCAmelCase_   :				Optional[int]						=						image_processing(_UpperCamelCase    ,		_UpperCamelCase    ,		return_tensors='pt'       )
							self.assertEqual(
							    encoding['pixel_values'].shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(
							    encoding['labels'].shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(encoding['labels'].dtype    ,		torch.long       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 2_5_5       )

							# Test not batched input (PIL images)
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						prepare_semantic_single_inputs()

							UpperCAmelCase_   :				Optional[int]						=						image_processing(_UpperCamelCase    ,		_UpperCamelCase    ,		return_tensors='pt'       )
							self.assertEqual(
							    encoding['pixel_values'].shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(
							    encoding['labels'].shape    ,		(
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(encoding['labels'].dtype    ,		torch.long       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 2_5_5       )

							# Test batched input (PIL images)
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						prepare_semantic_batch_inputs()

							UpperCAmelCase_   :				List[str]						=						image_processing(_UpperCamelCase    ,		_UpperCamelCase    ,		return_tensors='pt'       )
							self.assertEqual(
							    encoding['pixel_values'].shape    ,		(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(
							    encoding['labels'].shape    ,		(
							        2,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )    ,		)
							self.assertEqual(encoding['labels'].dtype    ,		torch.long       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 2_5_5       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							# Initialize image_processing
							UpperCAmelCase_   :				Any						=						self.image_processing_class(**self.image_processor_dict       )

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						prepare_semantic_single_inputs()
							UpperCAmelCase_   :				Any						=						image_processing(_UpperCamelCase    ,		_UpperCamelCase    ,		return_tensors='pt'       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 1_5_0       )

							UpperCAmelCase_   :				List[str]						=						True
							UpperCAmelCase_   :				List[Any]						=						image_processing(_UpperCamelCase    ,		_UpperCamelCase    ,		return_tensors='pt'       )
							self.assertTrue(encoding['labels'].min().item() >= 0       )
							self.assertTrue(encoding['labels'].max().item() <= 2_5_5       )














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)






