
'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_snake_case				:   Tuple     =				logging.get_logger(__name__)


_snake_case				:   int     =				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
_snake_case				:   List[str]     =				{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_snake_case				:   Dict     =				{
    """allenai/led-base-16384""": 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= (
						    list(range(ord("!"  )	,			ord("~"  ) + 1  )  ) + list(range(ord("¡"  )	,			ord("¬"  ) + 1  )  ) + list(range(ord("®"  )	,			ord("ÿ"  ) + 1  )  )
						)
						_SCREAMING_SNAKE_CASE				= bs[:]
						_SCREAMING_SNAKE_CASE				= 0
						for b in range(2**8  ):
												if b not in bs:
																		bs.append(_SCREAMING_SNAKE_CASE  )
																		cs.append(2**8 + n  )
																		n += 1
						_SCREAMING_SNAKE_CASE				= [chr(_SCREAMING_SNAKE_CASE  ) for n in cs]
						return dict(zip(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= set()
						_SCREAMING_SNAKE_CASE				= word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)  )
												_SCREAMING_SNAKE_CASE				= char
						return pairs




class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[Any]       =					VOCAB_FILES_NAMES
		a							:					str       =					PRETRAINED_VOCAB_FILES_MAP
		a							:					Tuple       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a							:					List[str]       =					["""input_ids""", """attention_mask"""]




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase="replace"					,						UpperCamelCase="<s>"					,						UpperCamelCase="</s>"					,						UpperCamelCase="</s>"					,						UpperCamelCase="<s>"					,						UpperCamelCase="<unk>"					,						UpperCamelCase="<pad>"					,						UpperCamelCase="<mask>"					,						UpperCamelCase=False					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else bos_token
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else eos_token
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else sep_token
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else cls_token
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else unk_token
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								_SCREAMING_SNAKE_CASE				= AddedToken(UpperCamelCase					,						lstrip=UpperCamelCase					,						rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase					,						UpperCamelCase      ) else mask_token

								super().__init__(
								    errors=UpperCamelCase					,						bos_token=UpperCamelCase					,						eos_token=UpperCamelCase					,						unk_token=UpperCamelCase					,						sep_token=UpperCamelCase					,						cls_token=UpperCamelCase					,						pad_token=UpperCamelCase					,						mask_token=UpperCamelCase					,						add_prefix_space=UpperCamelCase					,						**UpperCamelCase					,						)

								with open(UpperCamelCase					,						encoding="utf-8"      ) as vocab_handle:
														_SCREAMING_SNAKE_CASE				= json.load(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {v: k for k, v in self.encoder.items()}
								_SCREAMING_SNAKE_CASE				= errors  # how to handle errors in decoding
								_SCREAMING_SNAKE_CASE				= bytes_to_unicode()
								_SCREAMING_SNAKE_CASE				= {v: k for k, v in self.byte_encoder.items()}
								with open(UpperCamelCase					,						encoding="utf-8"      ) as merges_handle:
														_SCREAMING_SNAKE_CASE				= merges_handle.read().split("\n"      )[1:-1]
								_SCREAMING_SNAKE_CASE				= [tuple(merge.split()      ) for merge in bpe_merges]
								_SCREAMING_SNAKE_CASE				= dict(zip(UpperCamelCase					,						range(len(UpperCamelCase      )      )      )      )
								_SCREAMING_SNAKE_CASE				= {}
								_SCREAMING_SNAKE_CASE				= add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								_SCREAMING_SNAKE_CASE				= re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )




		@property
		# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
		def     lowercase       (				self      ):
								return len(self.encoder      )




		def     lowercase       (				self      ):
								return dict(self.encoder					,						**self.added_tokens_encoder      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								if token in self.cache:
														return self.cache[token]
								_SCREAMING_SNAKE_CASE				= tuple(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= get_pairs(UpperCamelCase      )

								if not pairs:
														return token

								while True:
														_SCREAMING_SNAKE_CASE				= min(UpperCamelCase					,						key=lambda UpperCamelCase      : self.bpe_ranks.get(UpperCamelCase					,						float("inf"      )      )      )
														if bigram not in self.bpe_ranks:
																				break
														_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= bigram
														_SCREAMING_SNAKE_CASE				= []
														_SCREAMING_SNAKE_CASE				= 0
														while i < len(UpperCamelCase      ):
																				try:
																										_SCREAMING_SNAKE_CASE				= word.index(UpperCamelCase					,						UpperCamelCase      )
																				except ValueError:
																										new_word.extend(word[i:]      )
																										break
																				else:
																										new_word.extend(word[i:j]      )
																										_SCREAMING_SNAKE_CASE				= j

																				if word[i] == first and i < len(UpperCamelCase      ) - 1 and word[i + 1] == second:
																										new_word.append(first + second      )
																										i += 2
																				else:
																										new_word.append(word[i]      )
																										i += 1
														_SCREAMING_SNAKE_CASE				= tuple(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= new_word
														if len(UpperCamelCase      ) == 1:
																				break
														else:
																				_SCREAMING_SNAKE_CASE				= get_pairs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= " ".join(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= word
								return word




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= []
								for token in re.findall(self.pat					,						UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= "".join(
														    self.byte_encoder[b] for b in token.encode("utf-8"      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
														bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase      ).split(" "      )      )
								return bpe_tokens




		def     lowercase       (				self					,						UpperCamelCase      ):
								return self.encoder.get(UpperCamelCase					,						self.encoder.get(self.unk_token      )      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								return self.decoder.get(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "".join(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"					,						errors=self.errors      )
								return text




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if not os.path.isdir(UpperCamelCase      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
														return
								_SCREAMING_SNAKE_CASE				= os.path.join(
								    UpperCamelCase					,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
								_SCREAMING_SNAKE_CASE				= os.path.join(
								    UpperCamelCase					,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

								with open(UpperCamelCase					,						"w"					,						encoding="utf-8"      ) as f:
														f.write(json.dumps(self.encoder					,						indent=2					,						sort_keys=UpperCamelCase					,						ensure_ascii=UpperCamelCase      ) + "\n"      )

								_SCREAMING_SNAKE_CASE				= 0
								with open(UpperCamelCase					,						"w"					,						encoding="utf-8"      ) as writer:
														writer.write("#version: 0.2\n"      )
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,						key=lambda UpperCamelCase      : kv[1]      ):
																				if index != token_index:
																										logger.warning(
																										    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																										    " Please check that the tokenizer is not corrupted!"      )
																										_SCREAMING_SNAKE_CASE				= token_index
																				writer.write(" ".join(UpperCamelCase      ) + "\n"      )
																				index += 1

								return vocab_file, merge_file




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_SCREAMING_SNAKE_CASE				= [self.cls_token_id]
								_SCREAMING_SNAKE_CASE				= [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = False      ):
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase					,						token_ids_a=UpperCamelCase					,						already_has_special_tokens=UpperCamelCase      )

								if token_ids_a is None:
														return [1] + ([0] * len(UpperCamelCase      )) + [1]
								return [1] + ([0] * len(UpperCamelCase      )) + [1, 1] + ([0] * len(UpperCamelCase      )) + [1]




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								_SCREAMING_SNAKE_CASE				= [self.sep_token_id]
								_SCREAMING_SNAKE_CASE				= [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep      ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=False					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= kwargs.pop("add_prefix_space"					,						self.add_prefix_space      )
								if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase      ) > 0 and not text[0].isspace()):
														_SCREAMING_SNAKE_CASE				= " " + text
								return (text, kwargs)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = PaddingStrategy.DO_NOT_PAD					,						UpperCamelCase = None					,						UpperCamelCase = None					,						):
								_SCREAMING_SNAKE_CASE				= super()._pad(
								    encoded_inputs=UpperCamelCase					,						max_length=UpperCamelCase					,						padding_strategy=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						)

								# Load from model defaults
								if return_attention_mask is None:
														_SCREAMING_SNAKE_CASE				= "attention_mask" in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
														_SCREAMING_SNAKE_CASE				= encoded_inputs[self.model_input_names[0]]
														# `global_attention_mask` need to have the same length as other (sequential) inputs.
														_SCREAMING_SNAKE_CASE				= len(encoded_inputs["global_attention_mask"]      ) != len(UpperCamelCase      )

														if needs_to_be_padded:
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      ) - len(encoded_inputs["global_attention_mask"]      )

																				if self.padding_side == "right":
																										# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																										_SCREAMING_SNAKE_CASE				= (
																										    encoded_inputs["global_attention_mask"] + [-1] * difference
																										)
																				elif self.padding_side == "left":
																										_SCREAMING_SNAKE_CASE				= [-1] * difference + encoded_inputs[
																										    "global_attention_mask"
																										]
																				else:
																										raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

								return encoded_inputs

'''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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim * 4




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								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     lowercase       (				self      ):
								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     lowercase       (				self      ):
								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     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_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				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

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

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_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      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								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": 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     lowercase       (				self      ):
								_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(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								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     lowercase       (				self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




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

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

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

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.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				= torch.Generator(device="cpu"      ).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				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

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

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )