



"""simple docstring"""







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[Any]   	=				""
		lowerCAmelCase			:					str   	=				(
		    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
		)
		lowerCAmelCase			:					str   	=				None  # compression type in fsspec. ex: "gzip"
		lowerCAmelCase			:					str   	=				None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







		def __init__(	self						:  Union[str, Any]			,_snake_case						:  str = ""			,_snake_case						:  Optional[str] = None			,_snake_case						:  Optional[dict] = None			,**_snake_case						:  int						)					->		Any:






				"""simple docstring"""



				super().__init__(self			,**_snake_case						)
				# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
				lowercase__   :    Dict				=			fsspec.open(
				    _snake_case			,mode='''rb'''			,protocol=_snake_case			,compression=self.compression			,client_kwargs={
				        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
				        '''trust_env''': True,  # Enable reading proxy env variables.
				        **(target_options or {}).pop('''client_kwargs'''			,{}						),  # To avoid issues if it was already passed.
				    }			,**(target_options or {})			,)
				lowercase__   :    Optional[Any]				=			os.path.basename(self.file.path.split('''::'''						)[0]						)
				lowercase__   :    List[Any]				=			(
				    self.compressed_name[: self.compressed_name.rindex('''.'''						)]
				    if '''.''' in self.compressed_name
				    else self.compressed_name
				)
				lowercase__   :    int				=			None







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[Any]			,_snake_case						:  str						)					->		List[Any]:






				"""simple docstring"""



				return super()._strip_protocol(_snake_case						).lstrip('''/'''						)







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






				"""simple docstring"""



				if self.dir_cache is None:
						lowercase__   :    Any				=			{**self.file.fs.info(self.file.path						), '''name''': self.uncompressed_name}
						lowercase__   :    int				=			{f['''name''']: f}







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






				"""simple docstring"""



				return self.file.open().read()







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  str			,_snake_case						:  str = "rb"			,_snake_case						:  Any=None			,_snake_case						:  Tuple=True			,_snake_case						:  str=None			,**_snake_case						:  Optional[int]			,)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self._strip_protocol(_snake_case						)
				if mode != "rb":
						raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""						)
				return self.file.open()





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Dict   	=				"bz2"
		lowerCAmelCase			:					List[Any]   	=				"bz2"
		lowerCAmelCase			:					Union[str, Any]   	=				".bz2"





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"gzip"
		lowerCAmelCase			:					Any   	=				"gzip"
		lowerCAmelCase			:					Optional[Any]   	=				".gz"





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Dict   	=				"lz4"
		lowerCAmelCase			:					int   	=				"lz4"
		lowerCAmelCase			:					Optional[int]   	=				".lz4"





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"xz"
		lowerCAmelCase			:					Any   	=				"xz"
		lowerCAmelCase			:					Any   	=				".xz"





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				"zstd"
		lowerCAmelCase			:					str   	=				"zstd"
		lowerCAmelCase			:					Tuple   	=				".zst"







		def __init__(	self						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  str = "rb"			,_snake_case						:  Optional[str] = None			,_snake_case						:  Optional[dict] = None			,_snake_case						:  int = DEFAULT_BLOCK_SIZE			,**_snake_case						:  List[str]			,)					->		List[str]:






				"""simple docstring"""



				super().__init__(
				    fo=_snake_case			,mode=_snake_case			,target_protocol=_snake_case			,target_options=_snake_case			,block_size=_snake_case			,**_snake_case			,)
				# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
				#
				# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
				#     out.close = close
				# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
				#
				# see https://github.com/intake/filesystem_spec/issues/725
				lowercase__   :    Optional[Any]				=			self.file.__enter__
				class        __A       :







						'''simple docstring'''





						def __init__(	self						:  List[Any]			,_snake_case						:  List[str]						)					->		List[str]:






								"""simple docstring"""



								lowercase__   :    List[str]				=			file_







						def __enter__(	self						:  Optional[Any]						)					->		List[Any]:






								"""simple docstring"""



								self._file.__enter__()
								return self







						def __exit__(	self						:  Any			,*_snake_case						:  Any			,**_snake_case						:  Optional[int]						)					->		Union[str, Any]:






								"""simple docstring"""



								self._file.__exit__(*_snake_case			,**_snake_case						)







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






								"""simple docstring"""



								return iter(self._file						)







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






								"""simple docstring"""



								return next(self._file						)







						def __getattr__(	self						:  Any			,_snake_case						:  Optional[Any]						)					->		Optional[Any]:






								"""simple docstring"""



								return getattr(self._file			,_snake_case						)

				def fixed_enter(*_snake_case						:  Dict			,**_snake_case						:  str						):
						return WrappedFile(_enter(*_snake_case			,**_snake_case						)						)

				lowercase__   :    Union[str, Any]				=			fixed_enter







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







@nightly
@require_torch_gpu
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCAmelCase__							)
          a__	:					str										=VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

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

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

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






