


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,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Tuple:




           """simple docstring"""

           super().__init__(
               lowerCamelCase,      split=lowerCamelCase,      features=lowerCamelCase,      cache_dir=lowerCamelCase,      keep_in_memory=lowerCamelCase,      streaming=lowerCamelCase,      num_proc=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Optional[Any]      		=		field
           _lowercase       :							Dict      		=		path_or_paths if isinstance(lowerCamelCase,      lowerCamelCase) else {self.split: path_or_paths}
           _lowercase       :							Tuple      		=		Json(
               cache_dir=lowerCamelCase,      data_files=lowerCamelCase,      features=lowerCamelCase,      field=lowerCamelCase,      **lowerCamelCase,      )




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

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

               self.builder.download_and_prepare(
                   download_config=lowerCamelCase,      download_mode=lowerCamelCase,      verification_mode=lowerCamelCase,      base_path=lowerCamelCase,      num_proc=self.num_proc,      )
               _lowercase       :							Tuple      		=		self.builder.as_dataset(
                   split=self.split,      verification_mode=lowerCamelCase,      in_memory=self.keep_in_memory)
           return dataset






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Optional[int]:




           """simple docstring"""

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

           _lowercase       :							List[Any]      		=		dataset
           _lowercase       :							int      		=		path_or_buf
           _lowercase       :							Optional[Any]      		=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           _lowercase       :							Dict      		=		num_proc
           _lowercase       :							Optional[Any]      		=		'utf-8'
           _lowercase       :							Optional[Any]      		=		to_json_kwargs
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							int      		=		self.to_json_kwargs.pop('path_or_buf',      lowerCamelCase)
           _lowercase       :							List[Any]      		=		self.to_json_kwargs.pop('orient',      'records')
           _lowercase       :							Dict      		=		self.to_json_kwargs.pop('lines',      True if orient == 'records' else False)
           _lowercase       :							Dict      		=		self.to_json_kwargs.pop('index',      False if orient in ['split', 'table'] else True)
           _lowercase       :							List[Any]      		=		self.to_json_kwargs.pop('compression',      lowerCamelCase)

           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=lowerCamelCase) as buffer:
                   _lowercase       :							Dict      		=		self._write(file_obj=lowerCamelCase,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **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.')
               _lowercase       :							Any      		=		self._write(
                   file_obj=self.path_or_buf,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **self.to_json_kwargs)
           return written
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							str      		=		args

           _lowercase       :							Optional[Any]      		=		query_table(
               table=self.dataset.data,      key=slice(lowerCamelCase,      offset + self.batch_size),      indices=self.dataset._indices,      )
           _lowercase       :							List[Any]      		=		batch.to_pandas().to_json(
               path_or_buf=lowerCamelCase,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **lowerCamelCase)
           if not json_str.endswith('\n'):
               json_str += "\n"
           return json_str.encode(self.encoding)




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase,      )       ->   int:




           """simple docstring"""

           _lowercase       :							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',      ):
                   _lowercase       :							Dict      		=		self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(lowerCamelCase)
           else:
               _lowercase		,				_lowercase       :							Dict      		=		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,      lowerCamelCase,      lowerCamelCase)],      ),      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(lowerCamelCase)

           return written






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Any     =			KandinskyImgaImgPipeline
       lowercase_ :	Union[str, Any]     =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowercase_ :	Any     =			[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowercase_ :	List[Any]     =			[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_ :	Union[str, Any]     =			False
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return self.time_input_dim
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return 1_00
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
           return tokenizer
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		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=10_05,      )

           _lowercase       :							Optional[int]      		=		MultilingualCLIP(lowerCamelCase)
           _lowercase       :							List[str]      		=		text_encoder.eval()

           return text_encoder
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase       :							Union[str, Any]      		=		{
               '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,
           }

           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(**lowerCamelCase)
           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)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		VQModel(**self.dummy_movq_kwargs)
           return model
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.dummy_text_encoder
           _lowercase       :							List[Any]      		=		self.dummy_tokenizer
           _lowercase       :							int      		=		self.dummy_unet
           _lowercase       :							int      		=		self.dummy_movq

           _lowercase       :							Optional[int]      		=		{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase       :							List[Any]      		=		DDIMScheduler(**lowerCamelCase)

           _lowercase       :							List[Any]      		=		{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		floats_tensor((1, self.cross_attention_dim),      rng=random.Random(seed + 1)).to(lowerCamelCase)
           # create init_image
           _lowercase       :							Tuple      		=		floats_tensor((1, 3, 64, 64),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		image.cpu().permute(0,      2,      3,      1)[0]
           _lowercase       :							Tuple      		=		Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[str]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               'prompt': 'horse',
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		'cpu'

           _lowercase       :							Tuple      		=		self.get_dummy_components()

           _lowercase       :							str      		=		self.pipeline_class(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							List[Any]      		=		pipe(
               **self.get_dummy_inputs(lowerCamelCase),      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							List[str]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							List[Any]      		=		image_from_tuple[0, -3:, -3:, -1]

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

           _lowercase       :							Tuple      		=		np.array(
               [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
           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 _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_img2img_frog.npy')

           _lowercase       :							str      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
           _lowercase       :							Optional[int]      		=		'A red cartoon frog, 4k'

           _lowercase       :							Union[str, Any]      		=		KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		KandinskyImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1',      torch_dtype=torch.floataa)
           _lowercase       :							List[Any]      		=		pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							str      		=		torch.Generator(device='cpu').manual_seed(0)
           _lowercase		,				_lowercase       :							List[Any]      		=		pipe_prior(
               lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=5,      negative_prompt='',      ).to_tuple()

           _lowercase       :							Union[str, Any]      		=		pipeline(
               lowerCamelCase,      image=lowerCamelCase,      image_embeds=lowerCamelCase,      negative_image_embeds=lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=1_00,      height=7_68,      width=7_68,      strength=0.2,      output_type='np',      )

           _lowercase       :							Dict      		=		output.images[0]

           assert image.shape == (7_68, 7_68, 3)

           assert_mean_pixel_difference(lowerCamelCase,      lowerCamelCase)


