



from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
	import torch

A__       =			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
	from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


	def 						_UpperCAmelCase							( cls     ):





						"""simple docstring"""

						return cls


def 						_UpperCAmelCase							( snake_case      ,		snake_case = None      ,		snake_case = None      ,		snake_case = None      ,		snake_case = None      ,		):





					"""simple docstring"""

					_lowerCAmelCase         =       device
					_lowerCAmelCase         =       shape[0]

					_lowerCAmelCase         =       layout or torch.strided
					_lowerCAmelCase         =       device or torch.device("""cpu"""     )

					if generator is not None:
										_lowerCAmelCase         =       generator.device.type if not isinstance(snake_case      ,		snake_case     ) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															_lowerCAmelCase         =       """cpu"""
															if device != "mps":
																				logger.info(
																				    F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
																				    F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
																				    F' slighly speed up this function by passing a generator that was created on the {device} device.'     )
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'     )

					if isinstance(snake_case      ,		snake_case     ):
										_lowerCAmelCase         =       (1,) + shape[1:]
										_lowerCAmelCase         =       [
										    torch.randn(snake_case      ,		generator=generator[i]      ,		device=snake_case      ,		dtype=snake_case      ,		layout=snake_case     )
										    for i in range(snake_case     )
										]
										_lowerCAmelCase         =       torch.cat(snake_case      ,		dim=0     ).to(snake_case     )
					else:
										_lowerCAmelCase         =       torch.randn(snake_case      ,		generator=snake_case      ,		device=snake_case      ,		dtype=snake_case      ,		layout=snake_case     ).to(snake_case     )

					return latents


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					if is_torch_version("""<"""      ,		"""2.0.0"""     ) or not hasattr(snake_case      ,		"""_dynamo"""     ):
										return False
					return isinstance(snake_case      ,		torch._dynamo.eval_frame.OptimizedModule     )





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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 __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': 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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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



