




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  ''''''
							UpperCamelCase__					     =  (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							UpperCamelCase__					     =  None  # compression type in fsspec. ex: "gzip"
							UpperCamelCase__					     =  None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
							def __init__(						self						:Any		,      __magic_name__						:str = ""		,      __magic_name__						:Optional[str] = None		,      __magic_name__						:Optional[dict] = None		,      **__magic_name__						:str				):







								'''simple docstring'''







								super().__init__(self		,      **__magic_name__				)
								# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
								a										=							fsspec.open(
								    __magic_name__		,      mode="""rb"""		,      protocol=__magic_name__		,      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 {})		,      )
								a										=							os.path.basename(self.file.path.split("""::"""				)[0]				)
								a										=							(
								    self.compressed_name[: self.compressed_name.rindex("""."""				)]
								    if """.""" in self.compressed_name
								    else self.compressed_name
								)
								a										=							None
							@classmethod
							def       lowerCamelCase__							(						cls						:Optional[int]		,      __magic_name__						:int				):







								'''simple docstring'''







								return super()._strip_protocol(__magic_name__				).lstrip("""/"""				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								if self.dir_cache is None:
									a										=							{**self.file.fs.info(self.file.path				), """name""": self.uncompressed_name}
									a										=							{f["""name"""]: f}
							def       lowerCamelCase__							(						self						:Any		,      __magic_name__						:str				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:str		,      __magic_name__						:str = "rb"		,      __magic_name__						:str=None		,      __magic_name__						:Dict=True		,      __magic_name__						:Tuple=None		,      **__magic_name__						:Union[str, Any]		,      ):







								'''simple docstring'''







								a										=							self._strip_protocol(__magic_name__				)
								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 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''bz2'''
							UpperCamelCase__					     =  '''bz2'''
							UpperCamelCase__					     =  '''.bz2'''







class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''gzip'''
							UpperCamelCase__					     =  '''gzip'''
							UpperCamelCase__					     =  '''.gz'''







class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''lz4'''
							UpperCamelCase__					     =  '''lz4'''
							UpperCamelCase__					     =  '''.lz4'''







class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''xz'''
							UpperCamelCase__					     =  '''xz'''
							UpperCamelCase__					     =  '''.xz'''







class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''zstd'''
							UpperCamelCase__					     =  '''zstd'''
							UpperCamelCase__					     =  '''.zst'''
							def __init__(						self						:List[str]		,      __magic_name__						:str		,      __magic_name__						:str = "rb"		,      __magic_name__						:Optional[str] = None		,      __magic_name__						:Optional[dict] = None		,      __magic_name__						:int = DEFAULT_BLOCK_SIZE		,      **__magic_name__						:Optional[Any]		,      ):







								'''simple docstring'''







								super().__init__(
								    fo=__magic_name__		,      mode=__magic_name__		,      target_protocol=__magic_name__		,      target_options=__magic_name__		,      block_size=__magic_name__		,      **__magic_name__		,      )
								# 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
								a										=							self.file.__enter__

								class 		__lowerCAmelCase	:
															def __init__(						self						:Tuple		,      __magic_name__						:str				):







																'''simple docstring'''







																a										=							file_
															def __enter__(						self						:Dict				):







																'''simple docstring'''







																self._file.__enter__()
																return self
															def __exit__(						self						:List[str]		,      *__magic_name__						:str		,      **__magic_name__						:Any				):







																'''simple docstring'''







																self._file.__exit__(*__magic_name__		,      **__magic_name__				)
															def __iter__(						self						:Optional[int]				):







																'''simple docstring'''







																return iter(self._file				)
															def       lowerCamelCase__							(						self						:List[Any]				):







																'''simple docstring'''







																return next(self._file				)





															def __getattr__(						self						:List[str]		,      __magic_name__						:List[Any]				):







																'''simple docstring'''







																return getattr(self._file		,      __magic_name__				)

								def fixed_enter(*__magic_name__						:Optional[Any]		,      **__magic_name__						:List[str]				):
									return WrappedFile(_enter(*__magic_name__		,      **__magic_name__				)				)

								a										=							fixed_enter







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

