

"""simple docstring"""




import os
from typing import Optional

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







class   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							""""""
  snake_case_												=							(
      None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
  )
  snake_case_												=							None  # compression type in fsspec. ex: "gzip"
  snake_case_												=							None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz

  def __init__(	self		:      Optional[int]       ,A		:      str = ""       ,A		:      Optional[str] = None       ,A		:      Optional[dict] = None       ,**A		:      int     ):






   '''simple docstring'''
   super().__init__(self       ,**a__     )
   # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
   UpperCAmelCase__       :	str        =       fsspec.open(
       a__       ,mode="""rb"""       ,protocol=a__       ,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 {})       ,)
   UpperCAmelCase__       :	List[str]        =       os.path.basename(self.file.path.split("""::"""     )[0]     )
   UpperCAmelCase__       :	Union[str, Any]        =       (
       self.compressed_name[: self.compressed_name.rindex("""."""     )]
       if "." in self.compressed_name
       else self.compressed_name
   )
   UpperCAmelCase__       :	Tuple        =       None

  @classmethod
  def  __lowercase							(	cls		:      List[str]       ,A		:      Dict     ):






   '''simple docstring'''
   return super()._strip_protocol(a__     ).lstrip("""/"""     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''

   if self.dir_cache is None:
    UpperCAmelCase__       :	List[str]        =       {**self.file.fs.info(self.file.path     ), "name": self.uncompressed_name}
    UpperCAmelCase__       :	Tuple        =       {f["name"]: f}

  def  __lowercase							(	self		:      Optional[Any]       ,A		:      str     ):






   '''simple docstring'''
   return self.file.open().read()




  def  __lowercase							(	self		:      Dict       ,A		:      str       ,A		:      str = "rb"       ,A		:      Union[str, Any]=None       ,A		:      str=True       ,A		:      int=None       ,**A		:      Tuple       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self._strip_protocol(a__     )
   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   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							"""bz2"""
  snake_case_												=							"""bz2"""
  snake_case_												=							""".bz2"""







class   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							"""gzip"""
  snake_case_												=							"""gzip"""
  snake_case_												=							""".gz"""







class   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							"""lz4"""
  snake_case_												=							"""lz4"""
  snake_case_												=							""".lz4"""







class   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							"""xz"""
  snake_case_												=							"""xz"""
  snake_case_												=							""".xz"""







class   __lowercase      (     UpperCamelCase_       ):
  snake_case_												=							"""zstd"""
  snake_case_												=							"""zstd"""
  snake_case_												=							""".zst"""

  def __init__(	self		:      Optional[int]       ,A		:      str       ,A		:      str = "rb"       ,A		:      Optional[str] = None       ,A		:      Optional[dict] = None       ,A		:      int = DEFAULT_BLOCK_SIZE       ,**A		:      Tuple       ,):






   '''simple docstring'''
   super().__init__(
       fo=a__       ,mode=a__       ,target_protocol=a__       ,target_options=a__       ,block_size=a__       ,**a__       ,)
   # 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
   UpperCAmelCase__       :	Optional[int]        =       self.file.__enter__







   class   __lowercase      :

     def __init__(	self		:      Dict       ,A		:      List[str]     ):






      '''simple docstring'''
      UpperCAmelCase__       :	Optional[Any]        =       file_

     def __enter__(	self		:      Optional[Any]     ):






      '''simple docstring'''
      self._file.__enter__()
      return self

     def __exit__(	self		:      Tuple       ,*A		:      Any       ,**A		:      Any     ):






      '''simple docstring'''
      self._file.__exit__(*a__       ,**a__     )

     def __iter__(	self		:      List[Any]     ):






      '''simple docstring'''
      return iter(self._file     )

     def  __lowercase							(	self		:      str     ):






      '''simple docstring'''
      return next(self._file     )




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






      '''simple docstring'''
      return getattr(self._file       ,a__     )

   def fixed_enter(*A		:      List[str]       ,**A		:      Optional[int]     ):
    return WrappedFile(_enter(*a__       ,**a__     )     )

   UpperCAmelCase__       :	List[Any]        =       fixed_enter







'''simple docstring'''

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    lowerCAmelCase       :


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

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[str]     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Optional[int]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	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    )
  lowerCAmelCase__     :   Union[str, Any]     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	IFWatermarker()

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


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

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

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[Any]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Dict     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Dict     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Tuple     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   str     		=	inputs["prompt"]
  lowerCAmelCase__     :   Optional[Any]     		=	inputs["generator"]
  lowerCAmelCase__     :   str     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   int     		=	inputs["output_type"]

  if "image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "mask_image" in inputs:
   lowerCAmelCase__     :   List[str]     		=	inputs["mask_image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "original_image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["original_image"]
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	None

  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	pipe.encode_prompt(a__    )

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   Dict     		=	{
      "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:
   lowerCAmelCase__     :   Optional[Any]     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   Optional[int]     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   Optional[Any]     		=	original_image

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

  lowerCAmelCase__     :   Optional[int]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

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

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   Any     		=	inputs["generator"]
  lowerCAmelCase__     :   Union[str, Any]     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   str     		=	inputs["output_type"]

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   int     		=	{
      "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:
   lowerCAmelCase__     :   Tuple     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   int     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   List[Any]     		=	original_image

  lowerCAmelCase__     :   Dict     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   int     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )


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

  lowerCAmelCase__     :   str     		=	self.get_dummy_components()
  lowerCAmelCase__     :   int     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   str     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )





