
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> Optional[int]:





													'''simple docstring'''






													super().__init__(
													    SCREAMING_SNAKE_CASE_  ,						split=SCREAMING_SNAKE_CASE_  ,						features=SCREAMING_SNAKE_CASE_  ,						cache_dir=SCREAMING_SNAKE_CASE_  ,						keep_in_memory=SCREAMING_SNAKE_CASE_  ,						streaming=SCREAMING_SNAKE_CASE_  ,						num_proc=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)
													__UpperCamelCase  	=      path_or_paths if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) else {self.split: path_or_paths}
													__UpperCamelCase  	=      Csv(
													    cache_dir=SCREAMING_SNAKE_CASE_  ,						data_files=SCREAMING_SNAKE_CASE_  ,						features=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													if self.streaming:
																			__UpperCamelCase  	=      self.builder.as_streaming_dataset(split=self.split      )
													# Build regular (map-style) dataset
													else:
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      None
																			__UpperCamelCase  	=      None

																			self.builder.download_and_prepare(
																			    download_config=SCREAMING_SNAKE_CASE_  ,						download_mode=SCREAMING_SNAKE_CASE_  ,						verification_mode=SCREAMING_SNAKE_CASE_  ,						base_path=SCREAMING_SNAKE_CASE_  ,						num_proc=self.num_proc  ,						)
																			__UpperCamelCase  	=      self.builder.as_dataset(
																			    split=self.split  ,						verification_mode=SCREAMING_SNAKE_CASE_  ,						in_memory=self.keep_in_memory      )
													return dataset






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> 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  	=      dataset
													__UpperCamelCase  	=      path_or_buf
													__UpperCamelCase  	=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__UpperCamelCase  	=      num_proc
													__UpperCamelCase  	=      '''utf-8'''
													__UpperCamelCase  	=      to_csv_kwargs



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''path_or_buf'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''header'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''index'''  ,						SCREAMING_SNAKE_CASE_      )

													if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)      ):
																			with open(self.path_or_buf  ,						'''wb+'''      ) as buffer:
																									__UpperCamelCase  	=      self._write(file_obj=SCREAMING_SNAKE_CASE_  ,						header=SCREAMING_SNAKE_CASE_  ,						index=SCREAMING_SNAKE_CASE_  ,						**self.to_csv_kwargs      )
													else:
																			__UpperCamelCase  	=      self._write(file_obj=self.path_or_buf  ,						header=SCREAMING_SNAKE_CASE_  ,						index=SCREAMING_SNAKE_CASE_  ,						**self.to_csv_kwargs      )
													return written



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      args

													__UpperCamelCase  	=      query_table(
													    table=self.dataset.data  ,						key=slice(SCREAMING_SNAKE_CASE_  ,						offset + self.batch_size      )  ,						indices=self.dataset._indices  ,						)
													__UpperCamelCase  	=      batch.to_pandas().to_csv(
													    path_or_buf=SCREAMING_SNAKE_CASE_  ,						header=header if (offset == 0) else False  ,						index=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return csv_str.encode(self.encoding      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      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 CSV from Arrow format'''  ,						):
																									__UpperCamelCase  	=      self._batch_csv((offset, header, index, to_csv_kwargs)      )
																									written += file_obj.write(SCREAMING_SNAKE_CASE_      )

													else:
																			__UpperCamelCase	,				__UpperCamelCase  	=      len(self.dataset      ), self.batch_size
																			with multiprocessing.Pool(self.num_proc      ) as pool:
																									for csv_str in logging.tqdm(
																									    pool.imap(
																									        self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )]  ,						)  ,						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 CSV from Arrow format'''  ,						):
																															written += file_obj.write(SCREAMING_SNAKE_CASE_      )

													return written






import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowercase__				:				List[str]						=      False






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

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

													__UpperCamelCase  	=      image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




