




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class     A(		datasets.BuilderConfig  ):



 '''simple docstring'''


 UpperCamelCase		  =		None







class     A(		datasets.ArrowBasedBuilder  ):



 '''simple docstring'''


 UpperCamelCase		  =		PandasConfig

 def 							a__					(							self   :    Optional[int]    )  ->      str:

       """simple docstring"""






       return datasets.DatasetInfo(features=self.config.features    )

 def 							a__					(							self   :    Dict		,				A_   :    int    )  ->      str:

       """simple docstring"""






       if not self.config.data_files:
             raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""    )
       lowerCamelCase_							=       dl_manager.download_and_extract(self.config.data_files    )
       if isinstance(A_		,				(str, list, tuple)    ):
             lowerCamelCase_							=       data_files
             if isinstance(A_		,				A_    ):
                   lowerCamelCase_							=       [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_							=       [dl_manager.iter_files(A_    ) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,				gen_kwargs={'files': files}    )]
       lowerCamelCase_							=       []
       for split_name, files in data_files.items():
             if isinstance(A_		,				A_    ):
                   lowerCamelCase_							=       [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_							=       [dl_manager.iter_files(A_    ) for file in files]
             splits.append(datasets.SplitGenerator(name=A_		,				gen_kwargs={'files': files}    )    )
       return splits

 def 							a__					(							self   :    int		,				A_   :    pa.Table    )  ->      pa.Table:

       """simple docstring"""






       if self.config.features is not None:
             # more expensive cast to support nested features with keys in a different order
             # allows str <-> int/float or str to Audio for example
             lowerCamelCase_							=       table_cast(A_		,				self.config.features.arrow_schema    )
       return pa_table





 def 							a__					(							self   :    str		,				A_   :    Optional[Any]    )  ->      str:

       """simple docstring"""






       for i, file in enumerate(itertools.chain.from_iterable(A_    )    ):
             with open(A_		,				'rb'    ) as f:
                   lowerCamelCase_							=       pa.Table.from_pandas(pd.read_pickle(A_    )    )
                   yield i, self._cast_table(A_    )




'''simple docstring'''




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 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			KandinskyImgaImgPipeline
			UpperCAmelCase_     :	Union[str, Any]						=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_     :	List[str]						=			[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			UpperCAmelCase_     :	Dict						=			[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCAmelCase_     :	str						=			False



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 100



			@property
			def 				a_			(  self):




					"""simple docstring"""


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



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    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=1005			,       )

					lowerCAmelCase     =    MultilingualCLIP(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder.eval()

					return text_encoder



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase     =    {
					    """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,
					}

					lowerCAmelCase     =    UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 				a_			(  self):




					"""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 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_text_encoder
					lowerCAmelCase     =    self.dummy_tokenizer
					lowerCAmelCase     =    self.dummy_unet
					lowerCAmelCase     =    self.dummy_movq

					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.00085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					lowerCAmelCase     =    DDIMScheduler(**__lowerCAmelCase)

					lowerCAmelCase     =    {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(seed + 1)).to(__lowerCAmelCase)
					# create init_image
					lowerCAmelCase     =    floats_tensor((1, 3, 64, 64)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    image.cpu().permute(0			,       2			,       3			,       1)[0]
					lowerCAmelCase     =    Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))

					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase     =    torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """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 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""

					lowerCAmelCase     =    self.get_dummy_components()

					lowerCAmelCase     =    self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase     =    pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    pipe(**self.get_dummy_inputs(__lowerCAmelCase))
					lowerCAmelCase     =    output.images

					lowerCAmelCase     =    pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase)			,       return_dict=__lowerCAmelCase			,       )[0]

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

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

					lowerCAmelCase     =    np.array(
					    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
					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 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


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







			def 				a_			(  self):




					"""simple docstring"""


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

					lowerCAmelCase     =    load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase     =    """A red cartoon frog, 4k"""

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

					lowerCAmelCase     =    KandinskyImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1"""			,       torch_dtype=torch.floataa)
					lowerCAmelCase     =    pipeline.to(__lowerCAmelCase)

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase   ,	lowerCAmelCase     =    pipe_prior(
					    __lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=5			,       negative_prompt=""""""			,       ).to_tuple()

					lowerCAmelCase     =    pipeline(
					    __lowerCAmelCase			,       image=__lowerCAmelCase			,       image_embeds=__lowerCAmelCase			,       negative_image_embeds=__lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=100			,       height=768			,       width=768			,       strength=0.2			,       output_type="""np"""			,       )

					lowerCAmelCase     =    output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(__lowerCAmelCase			,       __lowerCAmelCase)




