





import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					KandinskyInpaintPipeline
							lowerCamelCase_           =					["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							lowerCamelCase_           =					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							lowerCamelCase_           =					[
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							lowerCamelCase_           =					False


							@property
							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									return 32


							@property
							def      _snake_case  (							self    :Optional[int]		)   ->   Union[str, Any]:



									"""simple docstring"""



									return 32


							@property
							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



									return self.time_input_dim


							@property
							def      _snake_case  (							self    :Optional[int]		)   ->   List[str]:



									"""simple docstring"""



									return self.time_input_dim * 4


							@property
							def      _snake_case  (							self    :Optional[int]		)   ->   int:



									"""simple docstring"""



									return 100


							@property
							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



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


							@property
							def      _snake_case  (							self    :List[str]		)   ->   Tuple:



									"""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=1005							,				)

									SCREAMING_SNAKE_CASE__			=      MultilingualCLIP(__A		)
									SCREAMING_SNAKE_CASE__			=      text_encoder.eval()

									return text_encoder


							@property
							def      _snake_case  (							self    :List[str]		)   ->   Tuple:



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      {
									    """in_channels""": 9,
									    # 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(**__A		)
									return model


							@property
							def      _snake_case  (							self    :Any		)   ->   Optional[int]:



									"""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      _snake_case  (							self    :str		)   ->   Optional[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Any		)   ->   Optional[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=1000							,				beta_schedule="""linear"""							,				beta_start=0.0_0_0_8_5							,				beta_end=0.0_1_2							,				clip_sample=__A							,				set_alpha_to_one=__A							,				steps_offset=1							,				prediction_type="""epsilon"""							,				thresholding=__A							,				)

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

									return components


							def      _snake_case  (							self    :Dict							,				__A    :Optional[int]							,				__A    :Union[str, Any]=0		)   ->   Tuple:



									"""simple docstring"""



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

									if str(__A		).startswith("""mps"""		):
											SCREAMING_SNAKE_CASE__			=      torch.manual_seed(__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      torch.Generator(device=__A		).manual_seed(__A		)
									SCREAMING_SNAKE_CASE__			=      {
									    """prompt""": """horse""",
									    """image""": init_image,
									    """mask_image""": mask,
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 4.0,
									    """output_type""": """np""",
									}
									return inputs


							def      _snake_case  (							self    :int		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """cpu"""

									SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()

									SCREAMING_SNAKE_CASE__			=      self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__			=      pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__			=      pipe(**self.get_dummy_inputs(__A		)		)
									SCREAMING_SNAKE_CASE__			=      output.images

									SCREAMING_SNAKE_CASE__			=      pipe(
									    **self.get_dummy_inputs(__A		)							,				return_dict=__A							,				)[0]

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

									print(f'''image.shape {image.shape}'''		)

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

									SCREAMING_SNAKE_CASE__			=      np.array(
									    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]		)

									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()}'''


							def      _snake_case  (							self    :Dict		)   ->   str:



									"""simple docstring"""



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




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


							def      _snake_case  (							self    :Any		)   ->   Optional[int]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Any		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""		)

									SCREAMING_SNAKE_CASE__			=      load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
									SCREAMING_SNAKE_CASE__			=      np.ones((768, 768)							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__			=      0

									SCREAMING_SNAKE_CASE__			=      """a hat"""

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

									SCREAMING_SNAKE_CASE__			=      KandinskyInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-inpaint"""							,				torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__			=      pipeline.to(__A		)
									pipeline.set_progress_bar_config(disable=__A		)

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

									SCREAMING_SNAKE_CASE__			=      pipeline(
									    __A							,				image=__A							,				mask_image=__A							,				image_embeds=__A							,				negative_image_embeds=__A							,				generator=__A							,				num_inference_steps=100							,				height=768							,				width=768							,				output_type="""np"""							,				)

									SCREAMING_SNAKE_CASE__			=      output.images[0]

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

									assert_mean_pixel_difference(__A							,				__A		)






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_lowerCamelCase   				=			'\\n    Text data.\n    Second line of data.'

_lowerCamelCase   				=			'file'






@pytest.fixture(scope="""session"""    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / (FILE_PATH + """.zstd""")
		SCREAMING_SNAKE_CASE__			=      bytes(UpperCamelCase__					,   """utf-8"""    )
		with zstd.open(UpperCamelCase__					,   """wb"""    ) as f:
				f.write(UpperCamelCase__    )
		return path






@pytest.fixture
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		with open(os.path.join(tmpfs.local_root_dir					,   UpperCamelCase__    )					,   """w"""    ) as f:
				f.write(UpperCamelCase__    )
		return FILE_PATH






@pytest.mark.parametrize("""compression_format"""					,   ["""gzip""", """xz""", """zstd"""]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int					,   UpperCamelCase__:							Dict					,   UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
		SCREAMING_SNAKE_CASE__			=      input_paths[compression_format]
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      DownloadConfig(cache_dir=UpperCamelCase__					,   extract_compressed_file=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      cached_path(UpperCamelCase__					,   download_config=UpperCamelCase__    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		assert extracted_file_content == expected_file_content






@pytest.mark.parametrize("""default_extracted"""					,   [True, False]    )
@pytest.mark.parametrize("""default_cache_dir"""					,   [True, False]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      """custom_cache"""
		SCREAMING_SNAKE_CASE__			=      """custom_extracted_dir"""
		SCREAMING_SNAKE_CASE__			=      tmp_path / """custom_extracted_path"""
		if default_extracted:
				SCREAMING_SNAKE_CASE__			=      ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
		else:
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""					,   UpperCamelCase__    )
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""					,   str(UpperCamelCase__    )    )
				SCREAMING_SNAKE_CASE__			=      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

		SCREAMING_SNAKE_CASE__			=      xz_file
		SCREAMING_SNAKE_CASE__			=      (
		    DownloadConfig(extract_compressed_file=UpperCamelCase__    )
		    if default_cache_dir
		    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,   extract_compressed_file=UpperCamelCase__    )
		)
		SCREAMING_SNAKE_CASE__			=      cached_path(UpperCamelCase__					,   download_config=UpperCamelCase__    )
		assert Path(UpperCamelCase__    ).parent.parts[-2:] == expected






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[int]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve()    )
		assert cached_path(UpperCamelCase__    ) == text_file
		# relative path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve().relative_to(Path(os.getcwd()    )    )    )
		assert cached_path(UpperCamelCase__    ) == text_file






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(tmp_path.resolve() / """__missing_file__.txt"""    )
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )
		# relative path
		SCREAMING_SNAKE_CASE__			=      """./__missing_file__.txt"""
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      get_from_cache(f'''tmp://{tmpfs_file}'''    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		assert output_file_content == FILE_CONTENT






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       ):
		with pytest.raises(UpperCamelCase__    ):
				cached_path("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				http_get("""https://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				http_head("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				ftp_get("""ftp://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				ftp_head("""ftp://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				fsspec_get("""s3://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				fsspec_head("""s3://huggingface.co"""    )