





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 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     str					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Union[str, Any]=7					,      __lowerCamelCase							:     Optional[int]=3					,      __lowerCamelCase							:     str=18					,      __lowerCamelCase							:     Optional[Any]=30					,      __lowerCamelCase							:     Optional[int]=400					,      __lowerCamelCase							:     Tuple=True					,      __lowerCamelCase							:     Union[str, Any]=None					,      __lowerCamelCase							:     List[Any]=True					,      __lowerCamelCase							:     Dict=None					,      __lowerCamelCase							:     List[str]=True					,      __lowerCamelCase							:     Union[str, Any]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:     List[Any]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:     Tuple=False					,      )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     size if size is not None else {'height': 20, 'width': 20}
			__lowercase											=     crop_size if crop_size is not None else {'height': 18, 'width': 18}
			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     num_channels
			__lowercase											=     image_size
			__lowercase											=     min_resolution
			__lowercase											=     max_resolution
			__lowercase											=     do_resize
			__lowercase											=     size
			__lowercase											=     do_center_crop
			__lowercase											=     crop_size
			__lowercase											=     do_normalize
			__lowercase											=     image_mean
			__lowercase											=     image_std
			__lowercase											=     do_reduce_labels






	def    UpperCAmelCase			(						self							:     Any   )       -> List[Any]:

			'''simple docstring'''




			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  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     load_dataset('hf-internal-testing/fixtures_ade20k'						,   split='test'							)

		__lowercase											=     Image.open(dataset[0]['file']							)
		__lowercase											=     Image.open(dataset[1]['file']							)

		return image, map



def  SCREAMING_SNAKE_CASE				(      )		->    Optional[int]:
		__lowercase											=     load_dataset('hf-internal-testing/fixtures_ade20k'						,   split='test'							)

		__lowercase											=     Image.open(ds[0]['file']							)
		__lowercase											=     Image.open(ds[1]['file']							)
		__lowercase											=     Image.open(ds[2]['file']							)
		__lowercase											=     Image.open(ds[3]['file']							)

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


@require_torch
@require_vision
class 		snake_case_   (							_lowercase     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		BeitImageProcessor if is_vision_available() else None




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> int:

			'''simple docstring'''




			__lowercase											=     BeitImageProcessingTester(self   )




	@property
	def    UpperCAmelCase			(						self							:     Dict   )       -> Any:

			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()




	def    UpperCAmelCase			(						self							:     Any   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'do_resize'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'size'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'do_center_crop'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'center_crop'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'do_normalize'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'image_mean'   )   )
			self.assertTrue(hasattr(__lowerCamelCase					,      'image_std'   )   )




	def    UpperCAmelCase			(						self							:     int   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size					,      {'height': 20, 'width': 20}   )
			self.assertEqual(image_processor.crop_size					,      {'height': 18, 'width': 18}   )
			self.assertEqual(image_processor.do_reduce_labels					,      __lowerCamelCase   )

			__lowercase											=     self.image_processing_class.from_dict(
			    self.image_processor_dict					,      size=42					,      crop_size=84					,      reduce_labels=__lowerCamelCase   )
			self.assertEqual(image_processor.size					,      {'height': 42, 'width': 42}   )
			self.assertEqual(image_processor.crop_size					,      {'height': 84, 'width': 84}   )
			self.assertEqual(image_processor.do_reduce_labels					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Optional[Any]:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     Dict   )       -> int:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      Image.Image   )

			# Test not batched input
			__lowercase											=     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
			__lowercase											=     image_processing(__lowerCamelCase					,      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							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      np.ndarray   )

			# Test not batched input
			__lowercase											=     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
			__lowercase											=     image_processing(__lowerCamelCase					,      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   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase   )
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      torch.Tensor   )

			# Test not batched input
			__lowercase											=     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
			__lowercase											=     image_processing(__lowerCamelCase					,      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							:     int   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__lowercase											=     prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase   )
			__lowercase											=     []
			for image in image_inputs:
					self.assertIsInstance(__lowerCamelCase					,      torch.Tensor   )
					maps.append(torch.zeros(image.shape[-2:]   ).long()   )

			# Test not batched input
			__lowercase											=     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() <= 255   )

			# Test batched
			__lowercase											=     image_processing(__lowerCamelCase					,      __lowerCamelCase					,      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() <= 255   )

			# Test not batched input (PIL images)
			__lowercase						,      __lowercase											=     prepare_semantic_single_inputs()

			__lowercase											=     image_processing(__lowerCamelCase					,      __lowerCamelCase					,      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() <= 255   )

			# Test batched input (PIL images)
			__lowercase						,      __lowercase											=     prepare_semantic_batch_inputs()

			__lowercase											=     image_processing(__lowerCamelCase					,      __lowerCamelCase					,      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() <= 255   )






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     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
			__lowercase						,      __lowercase											=     prepare_semantic_single_inputs()
			__lowercase											=     image_processing(__lowerCamelCase					,      __lowerCamelCase					,      return_tensors='pt'   )
			self.assertTrue(encoding['labels'].min().item() >= 0   )
			self.assertTrue(encoding['labels'].max().item() <= 150   )

			__lowercase											=     True
			__lowercase											=     image_processing(__lowerCamelCase					,      __lowerCamelCase					,      return_tensors='pt'   )
			self.assertTrue(encoding['labels'].min().item() >= 0   )
			self.assertTrue(encoding['labels'].max().item() <= 255   )









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
a_      :      Optional[int]    =     '.'

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






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

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

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

 with open(__UpperCAmelCase							,  '''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(__UpperCAmelCase       )
 a__												= []

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

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


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

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





