




"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					lowercase(     __a       ,			__a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					StableDiffusionDiffEditPipeline
 lowercase__ 					=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
 lowercase__ 					=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
 lowercase__ 					=					frozenset(
     [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase__ 					=					frozenset([] )


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   torch.manual_seed(0		)
   _snake_case							:		Dict								=  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"""),      cross_attention_dim=32,      attention_head_dim=(2, 4),      use_linear_projection=a_,      )
   _snake_case							:		str								=  DDIMScheduler(
       beta_start=0.00_085,      beta_end=0.012,      beta_schedule="""scaled_linear""",      clip_sample=a_,      set_alpha_to_one=a_,      )
   _snake_case							:		str								=  DDIMInverseScheduler(
       beta_start=0.00_085,      beta_end=0.012,      beta_schedule="""scaled_linear""",      clip_sample=a_,      set_alpha_to_zero=a_,      )
   torch.manual_seed(0		)
   _snake_case							:		Optional[Any]								=  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		)
   _snake_case							:		List[str]								=  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=512,      )
   _snake_case							:		Optional[int]								=  CLIPTextModel(a_		)
   _snake_case							:		int								=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

   _snake_case							:		str								=  {
       """unet""": unet,
       """scheduler""": scheduler,
       """inverse_scheduler""": inverse_scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }

   return components


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any,      a_:		Any=0		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  floats_tensor((1, 16, 16),      rng=random.Random(a_		)		).to(a_		)
   _snake_case							:		Dict								=  floats_tensor((1, 2, 4, 16, 16),      rng=random.Random(a_		)		).to(a_		)
   if str(a_		).startswith("""mps"""		):
     _snake_case							:		Optional[int]								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		List[Any]								=  torch.Generator(device=a_		).manual_seed(a_		)
   _snake_case							:		Dict								=  {
       """prompt""": """a dog and a newt""",
       """mask_image""": mask,
       """image_latents""": latents,
       """generator""": generator,
       """num_inference_steps""": 2,
       """inpaint_strength""": 1.0,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }

   return inputs


 def  UpperCamelCase_   (				self:		Any,      a_:		Dict,      a_:		List[Any]=0		):




   '''simple docstring'''



   _snake_case							:		str								=  floats_tensor((1, 3, 32, 32),      rng=random.Random(a_		)		).to(a_		)
   _snake_case							:		str								=  image.cpu().permute(0,      2,      3,      1		)[0]
   _snake_case							:		Dict								=  Image.fromarray(np.uinta(a_		)		).convert("""RGB"""		)
   if str(a_		).startswith("""mps"""		):
     _snake_case							:		str								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		Dict								=  torch.Generator(device=a_		).manual_seed(a_		)
   _snake_case							:		int								=  {
       """image""": image,
       """source_prompt""": """a cat and a frog""",
       """target_prompt""": """a dog and a newt""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """num_maps_per_mask""": 2,
       """mask_encode_strength""": 1.0,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }

   return inputs


 def  UpperCamelCase_   (				self:		Any,      a_:		int,      a_:		Optional[Any]=0		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  floats_tensor((1, 3, 32, 32),      rng=random.Random(a_		)		).to(a_		)
   _snake_case							:		Optional[Any]								=  image.cpu().permute(0,      2,      3,      1		)[0]
   _snake_case							:		List[str]								=  Image.fromarray(np.uinta(a_		)		).convert("""RGB"""		)
   if str(a_		).startswith("""mps"""		):
     _snake_case							:		int								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		Union[str, Any]								=  torch.Generator(device=a_		).manual_seed(a_		)
   _snake_case							:		Dict								=  {
       """image""": image,
       """prompt""": """a cat and a frog""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """inpaint_strength""": 1.0,
       """guidance_scale""": 6.0,
       """decode_latents""": True,
       """output_type""": """numpy""",
   }
   return inputs


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   if not hasattr(self.pipeline_class,      """_optional_components"""		):
     return

   _snake_case							:		Optional[int]								=  self.get_dummy_components()
   _snake_case							:		Optional[int]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   # set all optional components to None and update pipeline config accordingly
   for optional_component in pipe._optional_components:
     setattr(a_,      a_,      a_		)
   pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}		)

   _snake_case							:		Optional[int]								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Optional[Any]								=  pipe(**a_		)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(a_		)
     _snake_case							:		int								=  self.pipeline_class.from_pretrained(a_		)
     pipe_loaded.to(a_		)
     pipe_loaded.set_progress_bar_config(disable=a_		)

   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.",      )

   _snake_case							:		Tuple								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Tuple								=  pipe_loaded(**a_		)[0]

   _snake_case							:		List[str]								=  np.abs(output - output_loaded		).max()
   self.assertLess(a_,      1E-4		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  """cpu"""

   _snake_case							:		Optional[int]								=  self.get_dummy_components()
   _snake_case							:		Dict								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		int								=  self.get_dummy_mask_inputs(a_		)
   _snake_case							:		Union[str, Any]								=  pipe.generate_mask(**a_		)
   _snake_case							:		Union[str, Any]								=  mask[0, -3:, -3:]

   self.assertEqual(mask.shape,      (1, 16, 16)		)
   _snake_case							:		Tuple								=  np.array([0] * 9		)
   _snake_case							:		str								=  np.abs(mask_slice.flatten() - expected_slice		).max()
   self.assertLessEqual(a_,      1E-3		)
   self.assertEqual(mask[0, -3, -4],      0		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		int								=  """cpu"""

   _snake_case							:		Union[str, Any]								=  self.get_dummy_components()
   _snake_case							:		Union[str, Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		List[str]								=  self.get_dummy_inversion_inputs(a_		)
   _snake_case							:		str								=  pipe.invert(**a_		).images
   _snake_case							:		List[str]								=  image[0, -1, -3:, -3:]

   self.assertEqual(image.shape,      (2, 32, 32, 3)		)
   _snake_case							:		List[Any]								=  np.array(
       [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799],      )
   _snake_case							:		Optional[Any]								=  np.abs(image_slice.flatten() - expected_slice		).max()
   self.assertLessEqual(a_,      1E-3		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=5E-3		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Any								=  """cpu"""

   _snake_case							:		List[Any]								=  self.get_dummy_components()

   _snake_case							:		str								=  {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
   _snake_case							:		Dict								=  DPMSolverMultistepScheduler(**a_		)
   _snake_case							:		Any								=  DPMSolverMultistepInverseScheduler(**a_		)

   _snake_case							:		Optional[Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		List[Any]								=  self.get_dummy_inversion_inputs(a_		)
   _snake_case							:		Union[str, Any]								=  pipe.invert(**a_		).images
   _snake_case							:		List[str]								=  image[0, -1, -3:, -3:]

   self.assertEqual(image.shape,      (2, 32, 32, 3)		)
   _snake_case							:		int								=  np.array(
       [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799],      )
   _snake_case							:		Tuple								=  np.abs(image_slice.flatten() - expected_slice		).max()
   self.assertLessEqual(a_,      1E-3		)







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







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


 @classmethod
 def  UpperCamelCase_   (				cls:		Dict		):




   '''simple docstring'''



   _snake_case							:		Dict								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""		)

   _snake_case							:		Optional[Any]								=  raw_image.convert("""RGB"""		).resize((768, 768)		)

   _snake_case							:		Optional[Any]								=  raw_image


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  torch.manual_seed(0		)

   _snake_case							:		Optional[int]								=  StableDiffusionDiffEditPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2-1""",      safety_checker=a_,      torch_dtype=torch.floataa		)
   _snake_case							:		Any								=  DDIMScheduler.from_config(pipe.scheduler.config		)
   _snake_case							:		Optional[Any]								=  DDIMInverseScheduler.from_config(pipe.scheduler.config		)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """a bowl of fruit"""
   _snake_case							:		Union[str, Any]								=  """a bowl of pears"""

   _snake_case							:		List[str]								=  pipe.generate_mask(
       image=self.raw_image,      source_prompt=a_,      target_prompt=a_,      generator=a_,      )

   _snake_case							:		Optional[Any]								=  pipe.invert(
       prompt=a_,      image=self.raw_image,      inpaint_strength=0.7,      generator=a_		).latents

   _snake_case							:		Dict								=  pipe(
       prompt=a_,      mask_image=a_,      image_latents=a_,      generator=a_,      negative_prompt=a_,      inpaint_strength=0.7,      output_type="""numpy""",      ).images[0]

   _snake_case							:		Union[str, Any]								=  (
       np.array(
           load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/diffedit/pears.png"""		).resize((768, 768)		)		)
       / 255
   )
   assert np.abs((expected_image - image).max()		) < 5E-1


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  torch.manual_seed(0		)

   _snake_case							:		List[Any]								=  StableDiffusionDiffEditPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2-1""",      safety_checker=a_,      torch_dtype=torch.floataa		)
   _snake_case							:		Dict								=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
   _snake_case							:		Any								=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config		)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Tuple								=  """a bowl of fruit"""
   _snake_case							:		Optional[Any]								=  """a bowl of pears"""

   _snake_case							:		Any								=  pipe.generate_mask(
       image=self.raw_image,      source_prompt=a_,      target_prompt=a_,      generator=a_,      )

   _snake_case							:		Optional[int]								=  pipe.invert(
       prompt=a_,      image=self.raw_image,      inpaint_strength=0.7,      generator=a_,      num_inference_steps=25,      ).latents

   _snake_case							:		Optional[Any]								=  pipe(
       prompt=a_,      mask_image=a_,      image_latents=a_,      generator=a_,      negative_prompt=a_,      inpaint_strength=0.7,      num_inference_steps=25,      output_type="""numpy""",      ).images[0]

   _snake_case							:		str								=  (
       np.array(
           load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/diffedit/pears.png"""		).resize((768, 768)		)		)
       / 255
   )
   assert np.abs((expected_image - image).max()		) < 5E-1






'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
