






'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class    A_	(a_						,					a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	IFImgaImgSuperResolutionPipeline
							a__   		=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							a__   		=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}							)
							a__   		=	PipelineTesterMixin.required_optional_params - {'''latents'''}

							def 							_A	(		self			:Tuple				)				->  List[Any]:


										'''simple docstring'''




										return self._get_superresolution_dummy_components()

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:List[Any]=0				)				->  str:


										'''simple docstring'''




										if str(lowerCAmelCase__				).startswith("mps"				):
													snake_case_				:					Union[str, Any]   =       torch.manual_seed(lowerCAmelCase__				)
										else:
													snake_case_				:					Optional[Any]   =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)

										snake_case_				:					Tuple   =       floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       floats_tensor((1, 3, 16, 16)       ,							rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)

										snake_case_				:					str   =       {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "original_image": original_image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()       ,							reason="XFormers attention is only available with CUDA and `xformers` installed"       ,							)
							def 							_A	(		self			:Tuple				)				->  List[Any]:


										'''simple docstring'''




										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

							def 							_A	(		self			:str				)				->  Tuple:


										'''simple docstring'''




										self._test_save_load_optional_components()

							@unittest.skipIf(torch_device != "cuda"       ,							reason="float16 requires CUDA"				)
							def 							_A	(		self			:Optional[Any]				)				->  Optional[Any]:


										'''simple docstring'''




										super().test_save_load_floataa(expected_max_diff=1E-1				)

							def 							_A	(		self			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

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


										'''simple docstring'''




										self._test_save_load_local()






							def 							_A	(		self			:Dict				)				->  Tuple:


										'''simple docstring'''




										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2       ,							)












'''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)




