



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			'''▁'''

__UpperCAmelCase			  =			{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

__UpperCAmelCase			  =			{
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
__UpperCAmelCase			  =			{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}



class 							lowerCAmelCase_						(    lowerCAmelCase__				):
       UpperCAmelCase__							:   Optional[Any]  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   Any  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__							:   Any  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   int  	=    ['''input_ids''', '''attention_mask''']

       UpperCAmelCase__							:   List[int]  	=    []
       UpperCAmelCase__							:   List[int]  	=    []
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_="<s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="</s>",     SCREAMING_SNAKE_CASE_="<pad>",     SCREAMING_SNAKE_CASE_="<unk>",     SCREAMING_SNAKE_CASE_="m2m100",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_=8,     **SCREAMING_SNAKE_CASE_,     )	->						Any:
          UpperCamelCase       :    str		      =      {} if sp_model_kwargs is None else sp_model_kwargs

          UpperCamelCase       :    List[Any]		      =      language_codes
          UpperCamelCase       :    Tuple		      =      FAIRSEQ_LANGUAGE_CODES[language_codes]
          UpperCamelCase       :    Union[str, Any]		      =      {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

          UpperCamelCase       :    int		      =      kwargs.get('additional_special_tokens',     []			)
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(__lowerCAmelCase			)
              for lang_code in fairseq_language_code
              if self.get_lang_token(__lowerCAmelCase			) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=__lowerCAmelCase,     tgt_lang=__lowerCAmelCase,     bos_token=__lowerCAmelCase,     eos_token=__lowerCAmelCase,     sep_token=__lowerCAmelCase,     unk_token=__lowerCAmelCase,     pad_token=__lowerCAmelCase,     language_codes=__lowerCAmelCase,     sp_model_kwargs=self.sp_model_kwargs,     num_madeup_words=__lowerCAmelCase,     **__lowerCAmelCase,     )

          UpperCamelCase       :    Optional[int]		      =      vocab_file
          UpperCamelCase       :    int		      =      load_json(__lowerCAmelCase			)
          UpperCamelCase       :    Any		      =      {v: k for k, v in self.encoder.items()}
          UpperCamelCase       :    List[Any]		      =      spm_file
          UpperCamelCase       :    Optional[int]		      =      load_spm(__lowerCAmelCase,     self.sp_model_kwargs			)

          UpperCamelCase       :    Optional[int]		      =      len(self.encoder			)

          UpperCamelCase       :    Optional[int]		      =      {
              self.get_lang_token(__lowerCAmelCase			): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase			)
          }
          UpperCamelCase       :    Optional[Any]		      =      {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase			)}
          UpperCamelCase       :    str		      =      {v: k for k, v in self.lang_token_to_id.items()}

          UpperCamelCase       :    Optional[int]		      =      src_lang if src_lang is not None else 'en'
          UpperCamelCase       :    Dict		      =      tgt_lang
          UpperCamelCase       :    Tuple		      =      self.get_lang_id(self._src_lang			)
          self.set_src_lang_special_tokens(self._src_lang			)

          UpperCamelCase       :    Union[str, Any]		      =      num_madeup_words
       @property
       def    snake_case_			(     self			)	->						Any:
          return len(self.encoder			) + len(self.lang_token_to_id			)
       @property
       def    snake_case_			(     self			)	->						str:
          return self._src_lang
       @src_lang.setter
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[Any]:
          UpperCamelCase       :    str		      =      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[Any]:
          return self.sp_model.encode(__lowerCAmelCase,     out_type=__lowerCAmelCase			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Tuple:


          if token in self.lang_token_to_id:
             return self.lang_token_to_id[token]
          return self.encoder.get(__lowerCAmelCase,     self.encoder[self.unk_token]			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:


          if index in self.id_to_lang_token:
             return self.id_to_lang_token[index]
          return self.decoder.get(__lowerCAmelCase,     self.unk_token			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    str		      =      []
          UpperCamelCase       :    Optional[int]		      =      ''
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__lowerCAmelCase			) + token
                UpperCamelCase       :    Optional[Any]		      =      []
             else:
                current_sub_tokens.append(__lowerCAmelCase			)
          out_string += self.sp_model.decode(__lowerCAmelCase			)
          return out_string.strip()
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False			)	->						Any:


          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__lowerCAmelCase,     token_ids_a=__lowerCAmelCase,     already_has_special_tokens=__lowerCAmelCase			)

          UpperCamelCase       :    List[Any]		      =      [1] * len(self.prefix_tokens			)
          UpperCamelCase       :    Any		      =      [1] * len(self.suffix_tokens			)
          if token_ids_a is None:
             return prefix_ones + ([0] * len(__lowerCAmelCase			)) + suffix_ones
          return prefix_ones + ([0] * len(__lowerCAmelCase			)) + ([0] * len(__lowerCAmelCase			)) + suffix_ones
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Dict:


          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab
       def __getstate__(     self			)	->						List[Any]:
          UpperCamelCase       :    int		      =      self.__dict__.copy()
          UpperCamelCase       :    List[Any]		      =      None
          return state
       def __setstate__(     self,     SCREAMING_SNAKE_CASE_			)	->						int:
          UpperCamelCase       :    str		      =      d

          # for backward compatibility
          if not hasattr(self,     'sp_model_kwargs'			):
             UpperCamelCase       :    int		      =      {}

          UpperCamelCase       :    Dict		      =      load_spm(self.spm_file,     self.sp_model_kwargs			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Optional[Any]:
          UpperCamelCase       :    List[Any]		      =      Path(__lowerCAmelCase			)
          if not save_dir.is_dir():
             raise OSError(F"""{save_directory} should be a directory"""			)
          UpperCamelCase       :    str		      =      save_dir / (
              (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
          )
          UpperCamelCase       :    Tuple		      =      save_dir / (
              (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
          )

          save_json(self.encoder,     __lowerCAmelCase			)

          if os.path.abspath(self.spm_file			) != os.path.abspath(__lowerCAmelCase			) and os.path.isfile(self.spm_file			):
             copyfile(self.spm_file,     __lowerCAmelCase			)
          elif not os.path.isfile(self.spm_file			):
             with open(__lowerCAmelCase,     'wb'			) as fi:
                UpperCamelCase       :    List[Any]		      =      self.sp_model.serialized_model_proto()
                fi.write(__lowerCAmelCase			)

          return (str(__lowerCAmelCase			), str(__lowerCAmelCase			))
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = "en",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = "ro",     **SCREAMING_SNAKE_CASE_,     )	->						List[str]:
          UpperCamelCase       :    List[Any]		      =      src_lang
          UpperCamelCase       :    Dict		      =      tgt_lang
          self.set_src_lang_special_tokens(self.src_lang			)
          return super().prepare_seqaseq_batch(__lowerCAmelCase,     __lowerCAmelCase,     **__lowerCAmelCase			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Dict:


          if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
          UpperCamelCase       :    Optional[Any]		      =      src_lang
          UpperCamelCase       :    Dict		      =      self(__lowerCAmelCase,     add_special_tokens=__lowerCAmelCase,     **__lowerCAmelCase			)
          UpperCamelCase       :    Tuple		      =      self.get_lang_id(__lowerCAmelCase			)
          UpperCamelCase       :    Optional[int]		      =      tgt_lang_id
          return inputs
       def    snake_case_			(     self			)	->						Any:
          self.set_src_lang_special_tokens(self.src_lang			)
       def    snake_case_			(     self			)	->						int:
          self.set_tgt_lang_special_tokens(self.tgt_lang			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    Any		      =      self.get_lang_token(__lowerCAmelCase			)
          UpperCamelCase       :    Optional[Any]		      =      self.lang_token_to_id[lang_token]
          UpperCamelCase       :    Tuple		      =      [self.cur_lang_id]
          UpperCamelCase       :    Union[str, Any]		      =      [self.eos_token_id]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    List[Any]		      =      self.get_lang_token(__lowerCAmelCase			)
          UpperCamelCase       :    Optional[Any]		      =      self.lang_token_to_id[lang_token]
          UpperCamelCase       :    Optional[int]		      =      [self.cur_lang_id]
          UpperCamelCase       :    List[str]		      =      [self.eos_token_id]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          return self.lang_code_to_token[lang]






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    Optional[Any]		      =      self.get_lang_token(__lowerCAmelCase			)
          return self.lang_token_to_id[lang_token]






def 						UpperCamelCase      (  snake_case__			:       str			,	snake_case__			:       Dict[str, Any]      )      ->	sentencepiece.SentencePieceProcessor:
   UpperCamelCase       :    Optional[int]		      =      sentencepiece.SentencePieceProcessor(**_A      )
   spm.Load(str(_A      )      )
   return spm
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	Union[Dict, List]:


   with open(_A			,	'r'      ) as f:
      return json.load(_A      )


def 						UpperCamelCase      (  snake_case__			:       str			,	snake_case__			:       str      )      ->	None:


   with open(_A			,	'w'      ) as f:
      json.dump(_A			,	_A			,	indent=2      )




'''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)




