
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase__		:Dict   =    {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					if got_ver is None or want_ver is None:
										raise ValueError(
										    f'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
										    f' reinstalling {pkg}.'		)
					if not ops[op](version.parse(lowerCAmelCase__		) ,			version.parse(lowerCAmelCase__		)		):
										raise ImportError(
										    f'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ = None		):
					'''simple docstring'''
					lowercase							=						f'\n{hint}' if hint is not None else ''''''

					# non-versioned check
					if re.match(R'''^[\w_\-\d]+$''' ,			lowerCAmelCase__		):
										lowercase	,						lowercase	,						lowercase							=						requirement, None, None
					else:
										lowercase							=						re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''' ,			lowerCAmelCase__		)
										if not match:
															raise ValueError(
															    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
															    f' got {requirement}'		)
										lowercase	,						lowercase							=						match[0]
										lowercase							=						want_full.split(''','''		)  # there could be multiple requirements
										lowercase							=						{}
										for w in want_range:
															lowercase							=						re.findall(R'''^([\s!=<>]{1,2})(.+)''' ,			lowerCAmelCase__		)
															if not match:
																				raise ValueError(
																				    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																				    f' but got {requirement}'		)
															lowercase	,						lowercase							=						match[0]
															lowercase							=						want_ver
															if op not in ops:
																				raise ValueError(f'{requirement}: need one of {list(ops.keys()		)}, but got {op}'		)

    # special case
					if pkg == "python":
										lowercase							=						'''.'''.join([str(lowerCAmelCase__		) for x in sys.version_info[:3]]		)
										for op, want_ver in wanted.items():
															_compare_versions(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										return

					# check if any version is installed
					try:
										lowercase							=						importlib.metadata.version(lowerCAmelCase__		)
					except importlib.metadata.PackageNotFoundError:
										raise importlib.metadata.PackageNotFoundError(
										    f'The \'{requirement}\' distribution was not found and is required by this application. {hint}'		)

					# check that the right version is installed if version number or a range was provided
					if want_ver is not None:
										for op, want_ver in wanted.items():
															_compare_versions(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
					return require_version(lowerCAmelCase__ ,			lowerCAmelCase__		)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_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):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





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




