






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  __UpperCAmelCase						(_UpperCAmelCase			,unittest.TestCase       ):
			__snake_case							: Union[str, Any]							=				KandinskyPipeline
			__snake_case							: Tuple							=				[
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			__snake_case							: Union[str, Any]							=				["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			__snake_case							: Union[str, Any]							=				[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			__snake_case							: Union[str, Any]							=				False







			@property
			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									return 32







			@property
			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									return 32







			@property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return self.time_input_dim







			@property
			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									return self.time_input_dim * 4







			@property
			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									return 100







			@property
			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
									return tokenizer







			@property
			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									torch.manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    MCLIPConfig(
									    numDims=self.cross_attention_dim ,      transformerDimensions=self.text_embedder_hidden_size ,      hidden_size=self.text_embedder_hidden_size ,      intermediate_size=37 ,      num_attention_heads=4 ,      num_hidden_layers=5 ,      vocab_size=1_005 ,      )

									_SCREAMING_SNAKE_CASE       =    MultilingualCLIP(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    text_encoder.eval()

									return text_encoder







			@property
			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									torch.manual_seed(0	)

									_SCREAMING_SNAKE_CASE       =    {
									    """in_channels""": 4,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """text_image""",
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """encoder_hid_dim""": self.text_embedder_hidden_size,
									    """encoder_hid_dim_type""": """text_image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									_SCREAMING_SNAKE_CASE       =    UNetaDConditionModel(**UpperCAmelCase_	)
									return model







			@property
			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}







			@property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									torch.manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    VQModel(**self.dummy_movq_kwargs	)
									return model







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE       =    self.dummy_tokenizer
									_SCREAMING_SNAKE_CASE       =    self.dummy_unet
									_SCREAMING_SNAKE_CASE       =    self.dummy_movq

									_SCREAMING_SNAKE_CASE       =    DDIMScheduler(
									    num_train_timesteps=1_000 ,      beta_schedule="""linear""" ,      beta_start=0.0_00_85 ,      beta_end=0.0_12 ,      clip_sample=UpperCAmelCase_ ,      set_alpha_to_one=UpperCAmelCase_ ,      steps_offset=1 ,      prediction_type="""epsilon""" ,      thresholding=UpperCAmelCase_ ,      )

									_SCREAMING_SNAKE_CASE       =    {
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}
									return components







			def 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    List[Any]=0	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    floats_tensor((1, self.cross_attention_dim) ,      rng=random.Random(UpperCAmelCase_	)	).to(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    floats_tensor((1, self.cross_attention_dim) ,      rng=random.Random(seed + 1	)	).to(UpperCAmelCase_	)
									if str(UpperCAmelCase_	).startswith("""mps"""	):
															_SCREAMING_SNAKE_CASE       =    torch.manual_seed(UpperCAmelCase_	)
									else:
															_SCREAMING_SNAKE_CASE       =    torch.Generator(device=UpperCAmelCase_	).manual_seed(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    {
									    """prompt""": """horse""",
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    """cpu"""

									_SCREAMING_SNAKE_CASE       =    self.get_dummy_components()

									_SCREAMING_SNAKE_CASE       =    self.pipeline_class(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    pipe.to(UpperCAmelCase_	)

									pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    pipe(**self.get_dummy_inputs(UpperCAmelCase_	)	)
									_SCREAMING_SNAKE_CASE       =    output.images

									_SCREAMING_SNAKE_CASE       =    pipe(
									    **self.get_dummy_inputs(UpperCAmelCase_	) ,      return_dict=UpperCAmelCase_ ,      )[0]

									_SCREAMING_SNAKE_CASE       =    image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE       =    image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE       =    np.array(
									    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]	)

									assert (
									    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
									), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
									), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



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







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""	)

									_SCREAMING_SNAKE_CASE       =    KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior""" ,      torch_dtype=torch.floataa	)
									pipe_prior.to(UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1""" ,      torch_dtype=torch.floataa	)
									_SCREAMING_SNAKE_CASE       =    pipeline.to(UpperCAmelCase_	)
									pipeline.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    """red cat, 4k photo"""

									_SCREAMING_SNAKE_CASE       =    torch.Generator(device="""cuda"""	).manual_seed(0	)
									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    pipe_prior(
									    UpperCAmelCase_ ,      generator=UpperCAmelCase_ ,      num_inference_steps=5 ,      negative_prompt="""""" ,      ).to_tuple()

									_SCREAMING_SNAKE_CASE       =    torch.Generator(device="""cuda"""	).manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    pipeline(
									    UpperCAmelCase_ ,      image_embeds=UpperCAmelCase_ ,      negative_image_embeds=UpperCAmelCase_ ,      generator=UpperCAmelCase_ ,      num_inference_steps=100 ,      output_type="""np""" ,      )

									_SCREAMING_SNAKE_CASE       =    output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(UpperCAmelCase_ ,      UpperCAmelCase_	)









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  __UpperCAmelCase						(_UpperCAmelCase       ):







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







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




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

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



class  __UpperCAmelCase						:







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







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    dataset
									_SCREAMING_SNAKE_CASE       =    path_or_buf
									_SCREAMING_SNAKE_CASE       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_SCREAMING_SNAKE_CASE       =    num_proc
									_SCREAMING_SNAKE_CASE       =    """utf-8"""
									_SCREAMING_SNAKE_CASE       =    to_csv_kwargs







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""path_or_buf""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""header""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""index""" ,      UpperCAmelCase_	)

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







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    args

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







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    BinaryIO ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    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""" ,      ):
																					_SCREAMING_SNAKE_CASE       =    self._batch_csv((offset, header, index, to_csv_kwargs)	)
																					written += file_obj.write(UpperCAmelCase_	)

									else:
															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    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 ,      UpperCAmelCase_ ,      UpperCAmelCase_	)] ,      ) ,      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(UpperCAmelCase_	)

									return written

