




import os
from typing import Optional

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




class 		__A   (      UpperCamelCase__      ):
						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     ,			__snake_case :str = ""     ,			__snake_case :Optional[str] = None     ,			__snake_case :Optional[dict] = None     ,			**__snake_case :Dict			):
										'''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
										__magic_name__						:						Any										=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 {})     ,			)
										__magic_name__						:						Union[str, Any]										=os.path.basename(self.file.path.split("""::"""			)[0]			)
										__magic_name__						:						Optional[int]										=(
										    self.compressed_name[: self.compressed_name.rindex("""."""			)]
										    if """.""" in self.compressed_name
										    else self.compressed_name
										)
										__magic_name__						:						Optional[Any]										=None

						@classmethod
						def      A__    (		cls :Tuple     ,			__snake_case :str			):
										'''simple docstring'''

										return super()._strip_protocol(__snake_case			).lstrip("""/"""			)

						def      A__    (		self :Any			):
										'''simple docstring'''

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

						def      A__    (		self :Any     ,			__snake_case :str			):
										'''simple docstring'''

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


						def      A__    (		self :List[Any]     ,			__snake_case :str     ,			__snake_case :str = "rb"     ,			__snake_case :str=None     ,			__snake_case :List[Any]=True     ,			__snake_case :Any=None     ,			**__snake_case :Any     ,			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=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   (      UpperCamelCase__      ):
						UpperCamelCase      =       """bz2"""
						UpperCamelCase      =       """bz2"""
						UpperCamelCase      =       """.bz2"""




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """gzip"""
						UpperCamelCase      =       """gzip"""
						UpperCamelCase      =       """.gz"""




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """lz4"""
						UpperCamelCase      =       """lz4"""
						UpperCamelCase      =       """.lz4"""




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xz"""
						UpperCamelCase      =       """xz"""
						UpperCamelCase      =       """.xz"""




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """zstd"""
						UpperCamelCase      =       """zstd"""
						UpperCamelCase      =       """.zst"""

						def __init__(		self :Optional[Any]     ,			__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[Any]     ,			):
										'''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
										__magic_name__						:						Dict										=self.file.__enter__




										class 		__A   :

																def __init__(		self :List[Any]     ,			__snake_case :List[Any]			):
																				'''simple docstring'''

																				__magic_name__						:						int										=file_

																def __enter__(		self :Dict			):
																				'''simple docstring'''

																				self._file.__enter__()
																				return self

																def __exit__(		self :Dict     ,			*__snake_case :str     ,			**__snake_case :Any			):
																				'''simple docstring'''

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

																def __iter__(		self :Tuple			):
																				'''simple docstring'''

																				return iter(self._file			)

																def      A__    (		self :Union[str, Any]			):
																				'''simple docstring'''

																				return next(self._file			)


																def __getattr__(		self :Any     ,			__snake_case :Optional[Any]			):
																				'''simple docstring'''

																				return getattr(self._file     ,			__snake_case			)

										def fixed_enter(*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
														return WrappedFile(_enter(*__snake_case     ,			**__snake_case			)			)

										__magic_name__						:						List[str]										=fixed_enter










import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 						UpperCAmelCase  :







						'''simple docstring'''





						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=1			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=3			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=[1, 2]			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=6			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      class_embed_type="timestep"			,      mid_block_scale_factor=1.414			,      time_embedding_act_fn="gelu"			,      time_embedding_dim=32			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["prompt"]
							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							if "image" in inputs:
								snake_case_						       =	inputs["image"]
							else:
								snake_case_						       =	None

							if "mask_image" in inputs:
								snake_case_						       =	inputs["mask_image"]
							else:
								snake_case_						       =	None

							if "original_image" in inputs:
								snake_case_						       =	inputs["original_image"]
							else:
								snake_case_						       =	None

							snake_case_       , snake_case_						       =	pipe.encode_prompt(__lowercase		)

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(__lowercase			,      __lowercase			,      __lowercase		)

							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(__lowercase			,      __lowercase		) is None			,      f"`{optional_component}` did not stay set to None after loading."			,      )

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)
