






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class _lowercase			(       _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase	):
							a          = StableDiffusionXLImgaImgPipeline
							a          = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
							a          = PipelineTesterMixin.required_optional_params - {"latents"}
							a          = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							a          = IMAGE_TO_IMAGE_IMAGE_PARAMS
							a          = IMAGE_TO_IMAGE_IMAGE_PARAMS




							def 	lowerCamelCase_       (							self:       str  ):
														torch.manual_seed(0  )
														lowerCamelCase__      :			Tuple 						= UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,							attention_head_dim=(2, 4)    ,							use_linear_projection=A_    ,							addition_embed_type="""text_time"""    ,							addition_time_embed_dim=8    ,							transformer_layers_per_block=(1, 2)    ,							projection_class_embeddings_input_dim=80    ,							cross_attention_dim=64    ,							)
														lowerCamelCase__      :			Optional[Any] 						= EulerDiscreteScheduler(
														    beta_start=0.00_085    ,							beta_end=0.012    ,							steps_offset=1    ,							beta_schedule="""scaled_linear"""    ,							timestep_spacing="""leading"""    ,							)
														torch.manual_seed(0  )
														lowerCamelCase__      :			int 						= AutoencoderKL(
														    block_out_channels=[32, 64]    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,							latent_channels=4    ,							sample_size=128    ,							)
														torch.manual_seed(0  )
														lowerCamelCase__      :			Optional[Any] 						= CLIPTextConfig(
														    bos_token_id=0    ,							eos_token_id=2    ,							hidden_size=32    ,							intermediate_size=37    ,							layer_norm_eps=1e-05    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							pad_token_id=1    ,							vocab_size=1_000    ,							hidden_act="""gelu"""    ,							projection_dim=32    ,							)
														lowerCamelCase__      :			List[str] 						= CLIPTextModel(A_  )
														lowerCamelCase__      :			List[str] 						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    ,							local_files_only=A_  )

														lowerCamelCase__      :			str 						= CLIPTextModelWithProjection(A_  )
														lowerCamelCase__      :			str 						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    ,							local_files_only=A_  )

														lowerCamelCase__      :			Union[str, Any] 						= {
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """text_encoder_2""": text_encoder_a,
														    """tokenizer_2""": tokenizer_a,
														    # "safety_checker": None,
														    # "feature_extractor": None,
														}
														return components




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Tuple=0  ):
														lowerCamelCase__      :			str 						= floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_  )  ).to(A_  )
														lowerCamelCase__      :			List[str] 						= image / 2 + 0.5
														if str(A_  ).startswith("""mps"""  ):
																					lowerCamelCase__      :			List[str] 						= torch.manual_seed(A_  )
														else:
																					lowerCamelCase__      :			Any 						= torch.Generator(device=A_  ).manual_seed(A_  )
														lowerCamelCase__      :			Any 						= {
														    """prompt""": """A painting of a squirrel eating a burger""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """guidance_scale""": 5.0,
														    """output_type""": """numpy""",
														    """strength""": 0.75,
														}
														return inputs




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Union[str, Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Tuple 						= self.get_dummy_components()
														lowerCamelCase__      :			Dict 						= StableDiffusionXLImgaImgPipeline(**A_  )
														lowerCamelCase__      :			Dict 						= sd_pipe.to(A_  )
														sd_pipe.set_progress_bar_config(disable=A_  )

														lowerCamelCase__      :			List[str] 						= self.get_dummy_inputs(A_  )
														lowerCamelCase__      :			List[Any] 						= sd_pipe(**A_  ).images
														lowerCamelCase__      :			Optional[int] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)

														lowerCamelCase__      :			Optional[int] 						= np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )




							def 	lowerCamelCase_       (							self:       Any  ):
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Dict 						= self.get_dummy_components()
														lowerCamelCase__      :			List[Any] 						= StableDiffusionXLImgaImgPipeline(**A_  )
														lowerCamelCase__      :			int 						= sd_pipe.to(A_  )
														lowerCamelCase__      :			Optional[Any] 						= sd_pipe.to(A_  )
														sd_pipe.set_progress_bar_config(disable=A_  )

														# forward without prompt embeds
														lowerCamelCase__      :			List[Any] 						= self.get_dummy_inputs(A_  )
														lowerCamelCase__      :			str 						= 3 * ["""this is a negative prompt"""]
														lowerCamelCase__      :			Tuple 						= negative_prompt
														lowerCamelCase__      :			Union[str, Any] 						= 3 * [inputs["""prompt"""]]

														lowerCamelCase__      :			Optional[int] 						= sd_pipe(**A_  )
														lowerCamelCase__      :			List[Any] 						= output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														lowerCamelCase__      :			List[Any] 						= self.get_dummy_inputs(A_  )
														lowerCamelCase__      :			Optional[int] 						= 3 * ["""this is a negative prompt"""]
														lowerCamelCase__      :			Optional[Any] 						= 3 * [inputs.pop("""prompt"""  )]

														(
														    (
														    lowerCamelCase__						
														)						,	(
														    lowerCamelCase__						
														)						,	(
														    lowerCamelCase__						
														)						,	(
														    lowerCamelCase__						
														)						,							
														)      :			str 						= sd_pipe.encode_prompt(A_    ,							negative_prompt=A_  )

														lowerCamelCase__      :			List[str] 						= sd_pipe(
														    **A_    ,							prompt_embeds=A_    ,							negative_prompt_embeds=A_    ,							pooled_prompt_embeds=A_    ,							negative_pooled_prompt_embeds=A_    ,							)
														lowerCamelCase__      :			List[Any] 						= output.images[0, -3:, -3:, -1]

														# make sure that it's equal
														assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()  ).max() < 1e-4



@slow
@require_torch_gpu
class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       str  ):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[int]="cpu"    ,							UpperCamelCase__:       List[Any]=torch.floataa    ,							UpperCamelCase__:       List[Any]=0  ):
														lowerCamelCase__      :			Optional[Any] 						= torch.Generator(device=A_  ).manual_seed(A_  )
														lowerCamelCase__      :			Optional[Any] 						= np.random.RandomState(A_  ).standard_normal((1, 4, 64, 64)  )
														lowerCamelCase__      :			Optional[Any] 						= torch.from_numpy(A_  ).to(device=A_    ,							dtype=A_  )
														lowerCamelCase__      :			Optional[int] 						= {
														    """prompt""": """a photograph of an astronaut riding a horse""",
														    """latents""": latents,
														    """generator""": generator,
														    """num_inference_steps""": 3,
														    """guidance_scale""": 7.5,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			List[str] 						= DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""  )
														pipe.to(A_  )
														pipe.set_progress_bar_config(disable=A_  )

														lowerCamelCase__      :			List[str] 						= self.get_inputs(A_  )
														lowerCamelCase__      :			List[str] 						= pipe(**A_  ).images
														lowerCamelCase__      :			Any 						= image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			Union[str, Any] 						= np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]  )
														assert np.abs(image_slice - expected_slice  ).max() < 7e-3









'''simple docstring'''







import argparse
import os
import re

import packaging.version


_A			:    List[str]					  ='''examples/'''
_A			:    Any					  ={
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
_A			:    int					  ={
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
_A			:    int					  ='''README.md'''





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							with open(UpperCamelCase       ,    """r"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														lowerCamelCase__      :			List[str] 						= f.read()
							lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= REPLACE_PATTERNS[pattern]
							lowerCamelCase__      :			Dict 						= replace.replace("""VERSION"""       ,    UpperCamelCase   )
							lowerCamelCase__      :			str 						= re_pattern.sub(UpperCamelCase       ,    UpperCamelCase   )
							with open(UpperCamelCase       ,    """w"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														f.write(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							for folder, directories, fnames in os.walk(UpperCamelCase   ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove("""research_projects"""   )
														if "legacy" in directories:
																					directories.remove("""legacy"""   )
														for fname in fnames:
																					if fname.endswith(""".py"""   ):
																												update_version_in_file(os.path.join(UpperCamelCase       ,    UpperCamelCase   )       ,    UpperCamelCase       ,    pattern="""examples"""   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> List[Any]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
							if not patch:
														update_version_in_examples(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							lowerCamelCase__      :			Dict 						= """🤗 Transformers currently provides the following architectures"""
							lowerCamelCase__      :			Dict 						= """1. Want to contribute a new model?"""
							with open(UpperCamelCase       ,    """r"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														lowerCamelCase__      :			int 						= f.readlines()

							# Find the start of the list.
							lowerCamelCase__      :			Optional[int] 						= 0
							while not lines[start_index].startswith(_start_prompt   ):
														start_index += 1
							start_index += 1

							lowerCamelCase__      :			Optional[Any] 						= start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt   ):
														if lines[index].startswith("""1."""   ):
																					lowerCamelCase__      :			List[Any] 						= lines[index].replace(
																					    """https://huggingface.co/docs/transformers/main/model_doc"""       ,    """https://huggingface.co/docs/transformers/model_doc"""       ,    )
														index += 1

							with open(UpperCamelCase       ,    """w"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														f.writelines(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							with open(REPLACE_FILES["""init"""]       ,    """r"""   ) as f:
														lowerCamelCase__      :			int 						= f.read()
							lowerCamelCase__      :			Optional[Any] 						= REPLACE_PATTERNS["""init"""][0].search(UpperCamelCase   ).groups()[0]
							return packaging.version.parse(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase=False   )   -> List[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= get_version()
							if patch and default_version.is_devrelease:
														raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""   )
							if default_version.is_devrelease:
														lowerCamelCase__      :			List[str] 						= default_version.base_version
							elif patch:
														lowerCamelCase__      :			Any 						= f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
							else:
														lowerCamelCase__      :			List[Any] 						= f'''{default_version.major}.{default_version.minor + 1}.0'''

							# Now let's ask nicely if that's the right one.
							lowerCamelCase__      :			Any 						= input(f'''Which version are you releasing? [{default_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Optional[int] 						= default_version

							print(f'''Updating version to {version}.'''   )
							global_version_update(UpperCamelCase       ,    patch=UpperCamelCase   )
							if not patch:
														print("""Cleaning main README, don't forget to run `make fix-copies`."""   )
														clean_main_ref_in_model_list()





def 				SCREAMING_SNAKE_CASE_ ()   -> List[str]:
							lowerCamelCase__      :			Optional[int] 						= get_version()
							lowerCamelCase__      :			Any 						= f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
							lowerCamelCase__      :			Any 						= current_version.base_version

							# Check with the user we got that right.
							lowerCamelCase__      :			List[Any] 						= input(f'''Which version are we developing now? [{dev_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Dict 						= dev_version

							print(f'''Updating version to {version}.'''   )
							global_version_update(UpperCamelCase   )
							print("""Cleaning main README, don't forget to run `make fix-copies`."""   )
							clean_main_ref_in_model_list()


if __name__ == "__main__":
			_A			:    List[Any]					  =argparse.ArgumentParser()
			parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
			parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
			_A			:    List[str]					  =parser.parse_args()
			if not args.post_release:
						pre_release_work(patch=args.patch)
			elif args.patch:
						print('''Nothing to do after a patch :-)''')
			else:
						post_release_work()

