






'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__lowerCamelCase   :					Tuple   							=						logging.get_logger(__name__)







class    A_	(_UpperCamelCase							):
							"""simple docstring"""




							a__   		=	['''pixel_values''']

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:List[str] = True       ,							lowerCAmelCase__			:List[str] = None       ,							lowerCAmelCase__			:Optional[Any] = None       ,							lowerCAmelCase__			:Union[str, Any] = PILImageResampling.BILINEAR       ,							lowerCAmelCase__			:List[str] = True       ,							lowerCAmelCase__			:int = 1 / 255       ,							lowerCAmelCase__			:Optional[Any] = True       ,							lowerCAmelCase__			:Tuple = None       ,							lowerCAmelCase__			:Union[str, Any] = None       ,							**lowerCAmelCase__			:List[str]       ,							)				->  List[Any]:


										'''simple docstring'''




										super().__init__(**_UpperCAmelCase				)
										snake_case_				:					str   =       size if size is not None else {'''shortest_edge''': 384}
										snake_case_				:					str   =       get_size_dict(_UpperCAmelCase       ,							default_to_square=_UpperCAmelCase				)

										snake_case_				:					Optional[Any]   =       do_resize
										snake_case_				:					Optional[int]   =       size
										# Default value set here for backwards compatibility where the value in config is None
										snake_case_				:					str   =       crop_pct if crop_pct is not None else 224 / 256
										snake_case_				:					Any   =       resample
										snake_case_				:					int   =       do_rescale
										snake_case_				:					int   =       rescale_factor
										snake_case_				:					int   =       do_normalize
										snake_case_				:					Dict   =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case_				:					int   =       image_std if image_std is not None else IMAGENET_STANDARD_STD

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[int] = PILImageResampling.BICUBIC       ,							lowerCAmelCase__			:int = None       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_size_dict(_UpperCAmelCase       ,							default_to_square=_UpperCAmelCase				)
										if "shortest_edge" not in size:
													raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''				)
										snake_case_				:					Any   =       size['''shortest_edge''']

										if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													snake_case_				:					Optional[int]   =       int(shortest_edge / crop_pct				)
													snake_case_				:					Optional[Any]   =       get_resize_output_image_size(_UpperCAmelCase       ,							size=_UpperCAmelCase       ,							default_to_square=_UpperCAmelCase				)
													snake_case_				:					Optional[Any]   =       resize(image=_UpperCAmelCase       ,							size=_UpperCAmelCase       ,							resample=_UpperCAmelCase       ,							data_format=_UpperCAmelCase       ,							**_UpperCAmelCase				)
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=_UpperCAmelCase       ,							size=(shortest_edge, shortest_edge)       ,							data_format=_UpperCAmelCase       ,							**_UpperCAmelCase				)
										else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    _UpperCAmelCase       ,							size=(shortest_edge, shortest_edge)       ,							resample=_UpperCAmelCase       ,							data_format=_UpperCAmelCase       ,							**_UpperCAmelCase				)

							def 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[Any] = None       ,							**lowerCAmelCase__			:List[Any]       ,							)				->  int:


										'''simple docstring'''




										return rescale(_UpperCAmelCase       ,							scale=_UpperCAmelCase       ,							data_format=_UpperCAmelCase       ,							**_UpperCAmelCase				)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Dict = None       ,							**lowerCAmelCase__			:Union[str, Any]       ,							)				->  Tuple:


										'''simple docstring'''




										return normalize(_UpperCAmelCase       ,							mean=_UpperCAmelCase       ,							std=_UpperCAmelCase       ,							data_format=_UpperCAmelCase       ,							**_UpperCAmelCase				)






							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Optional[Any] = None       ,							lowerCAmelCase__			:Tuple = None       ,							lowerCAmelCase__			:int = None       ,							lowerCAmelCase__			:int = None       ,							lowerCAmelCase__			:Optional[int] = None       ,							lowerCAmelCase__			:int = None       ,							lowerCAmelCase__			:Any = None       ,							lowerCAmelCase__			:Dict = None       ,							lowerCAmelCase__			:str = None       ,							lowerCAmelCase__			:Optional[Any] = None       ,							lowerCAmelCase__			:int = ChannelDimension.FIRST       ,							**lowerCAmelCase__			:List[str]       ,							)				->  Any:


										'''simple docstring'''




										snake_case_				:					str   =       do_resize if do_resize is not None else self.do_resize
										snake_case_				:					List[Any]   =       crop_pct if crop_pct is not None else self.crop_pct
										snake_case_				:					Tuple   =       resample if resample is not None else self.resample
										snake_case_				:					List[str]   =       do_rescale if do_rescale is not None else self.do_rescale
										snake_case_				:					List[Any]   =       rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case_				:					Dict   =       do_normalize if do_normalize is not None else self.do_normalize
										snake_case_				:					Any   =       image_mean if image_mean is not None else self.image_mean
										snake_case_				:					int   =       image_std if image_std is not None else self.image_std

										snake_case_				:					List[str]   =       size if size is not None else self.size
										snake_case_				:					Optional[int]   =       get_size_dict(_UpperCAmelCase       ,							default_to_square=_UpperCAmelCase				)

										snake_case_				:					Optional[int]   =       make_list_of_images(_UpperCAmelCase				)

										if not valid_images(_UpperCAmelCase				):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."				)

										if do_resize and size is None or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."				)

										if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError("crop_pct must be specified if size < 384."				)

										if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."				)

										if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."				)

										# All transformations expect numpy arrays.
										snake_case_				:					Tuple   =       [to_numpy_array(_UpperCAmelCase				) for image in images]

										if do_resize:
													snake_case_				:					Dict   =       [self.resize(image=_UpperCAmelCase       ,							size=_UpperCAmelCase       ,							crop_pct=_UpperCAmelCase       ,							resample=_UpperCAmelCase				) for image in images]

										if do_rescale:
													snake_case_				:					str   =       [self.rescale(image=_UpperCAmelCase       ,							scale=_UpperCAmelCase				) for image in images]

										if do_normalize:
													snake_case_				:					Dict   =       [self.normalize(image=_UpperCAmelCase       ,							mean=_UpperCAmelCase       ,							std=_UpperCAmelCase				) for image in images]

										snake_case_				:					str   =       [to_channel_dimension_format(_UpperCAmelCase       ,							_UpperCAmelCase				) for image in images]

										snake_case_				:					Dict   =       {'''pixel_values''': images}
										return BatchFeature(data=_UpperCAmelCase       ,							tensor_type=_UpperCAmelCase				)












'''simple docstring'''




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
__lowerCamelCase   :					str   							=					'''.'''

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

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

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

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

			snake_case_				:					Tuple   =       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_				:					str   =       sorted(__magic_name__						)
			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(__magic_name__						)

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


if __name__ == "__main__":
		__lowerCamelCase   :					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)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

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




