




# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
SCREAMING_SNAKE_CASE__       :      List[str]  =   {
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
       SCREAMING_SNAKE_CASE__       :      str  =   'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
       SCREAMING_SNAKE_CASE__       :      Dict  =   'allenai'

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Any:
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       lowerCamelCase   :      Optional[Any]			 =					dict((re.sub(r"@@$" ,"" ,_SCREAMING_SNAKE_CASE    ), v) if k.endswith("@@"    ) else (re.sub(r"$" ,"</w>" ,_SCREAMING_SNAKE_CASE    ), v) for k, v in d.items()    )
       lowerCamelCase   :      Optional[Any]			 =					"<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[f'''{k}</w>''']
              lowerCamelCase   :      Union[str, Any]			 =					d[k]  # restore
       return da

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # prep
       assert os.path.exists(_SCREAMING_SNAKE_CASE    )
       os.makedirs(_SCREAMING_SNAKE_CASE ,exist_ok=_SCREAMING_SNAKE_CASE    )
       print(f'''Writing results to {pytorch_dump_folder_path}'''    )

       # handle various types of models

       lowerCamelCase   :      Dict			 =					basename(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					dirname(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      str			 =					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
       lowerCamelCase   :      List[str]			 =					cls.hub_models()
       lowerCamelCase   :      Dict			 =					{"bpe": "fastbpe", "tokenizer": "moses"}
       lowerCamelCase   :      Optional[int]			 =					"."
       # note: since the model dump is old, fairseq has upgraded its model some
       # time later, and it does a whole lot of rewrites and splits on the saved
       # weights, therefore we can't use torch.load() directly on the model file.
       # see: upgrade_state_dict(state_dict) in fairseq_model.py
       print(f'''using checkpoint {checkpoint_file}'''    )
       lowerCamelCase   :      Any			 =					hub_utils.from_pretrained(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,archive_map=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      List[Any]			 =					vars(chkpt["args"]["model"]    )

       lowerCamelCase   :      int			 =					args["source_lang"]
       lowerCamelCase   :      Union[str, Any]			 =					args["target_lang"]

       lowerCamelCase   :      Dict			 =					dirname(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      int			 =					basename(_SCREAMING_SNAKE_CASE    )

       # dicts
       lowerCamelCase   :      Optional[Any]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,f'''dict.{src_lang}.txt'''    )
       lowerCamelCase   :      Optional[int]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,f'''dict.{tgt_lang}.txt'''    )

       lowerCamelCase   :      Dict			 =					Dictionary.load(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					rewrite_dict_keys(src_dict.indices    )
       lowerCamelCase   :      Any			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[Any]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,"vocab-src.json"    )
       print(f'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''    )
       with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
              f.write(json.dumps(_SCREAMING_SNAKE_CASE ,ensure_ascii=_SCREAMING_SNAKE_CASE ,indent=_SCREAMING_SNAKE_CASE    )    )

       # detect whether this is a do_lower_case situation, which can be derived by checking whether we
       # have at least one uppercase letter in the source vocab
       lowerCamelCase   :      Optional[int]			 =					True
       for k in src_vocab.keys():
              if not k.islower():
                     lowerCamelCase   :      Optional[int]			 =					False
                     break

       lowerCamelCase   :      int			 =					Dictionary.load(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					rewrite_dict_keys(tgt_dict.indices    )
       lowerCamelCase   :      Dict			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[Any]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,"vocab-tgt.json"    )
       print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''    )
       with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
              f.write(json.dumps(_SCREAMING_SNAKE_CASE ,ensure_ascii=_SCREAMING_SNAKE_CASE ,indent=_SCREAMING_SNAKE_CASE    )    )

       # merges_file (bpecodes)
       lowerCamelCase   :      List[str]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,VOCAB_FILES_NAMES["merges_file"]    )
       for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
              lowerCamelCase   :      Optional[Any]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if os.path.exists(_SCREAMING_SNAKE_CASE    ):
                     break
       with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"    ) as fin:
              lowerCamelCase   :      List[str]			 =					fin.read()
       lowerCamelCase   :      Optional[Any]			 =					re.sub(r" \d+$" ,"" ,_SCREAMING_SNAKE_CASE ,0 ,re.M    )  # remove frequency number
       print(f'''Generating {merges_file}'''    )
       with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as fout:
              fout.write(_SCREAMING_SNAKE_CASE    )

       # model config
       lowerCamelCase   :      Any			 =					os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )

       # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
       # may have to modify the tokenizer if a different type is used by a future model
       assert args["bpe"] == "fastbpe", f'''need to extend tokenizer to support bpe={args["bpe"]}'''
       assert args["tokenizer"] == "moses", f'''need to extend tokenizer to support bpe={args["tokenizer"]}'''

       lowerCamelCase   :      Tuple			 =					{
           "architectures": ["FSMTForConditionalGeneration"],
           "model_type": "fsmt",
           "activation_dropout": args["activation_dropout"],
           "activation_function": "relu",
           "attention_dropout": args["attention_dropout"],
           "d_model": args["decoder_embed_dim"],
           "dropout": args["dropout"],
           "init_std": 0.02,
           "max_position_embeddings": args["max_source_positions"],
           "num_hidden_layers": args["encoder_layers"],
           "src_vocab_size": src_vocab_size,
           "tgt_vocab_size": tgt_vocab_size,
           "langs": [src_lang, tgt_lang],
           "encoder_attention_heads": args["encoder_attention_heads"],
           "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
           "encoder_layerdrop": args["encoder_layerdrop"],
           "encoder_layers": args["encoder_layers"],
           "decoder_attention_heads": args["decoder_attention_heads"],
           "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
           "decoder_layerdrop": args["decoder_layerdrop"],
           "decoder_layers": args["decoder_layers"],
           "bos_token_id": 0,
           "pad_token_id": 1,
           "eos_token_id": 2,
           "is_encoder_decoder": True,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_all_embeddings"],
       }

       # good hparam defaults to start with
       lowerCamelCase   :      Optional[Any]			 =					5
       lowerCamelCase   :      Any			 =					False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowerCamelCase   :      Optional[Any]			 =					best_score_hparams[model_dir]["length_penalty"]
       else:
              lowerCamelCase   :      Optional[Any]			 =					1.0

       print(f'''Generating {fsmt_model_config_file}'''    )
       with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
              f.write(json.dumps(_SCREAMING_SNAKE_CASE ,ensure_ascii=_SCREAMING_SNAKE_CASE ,indent=_SCREAMING_SNAKE_CASE    )    )

       # tokenizer config
       lowerCamelCase   :      Optional[Any]			 =					os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      int			 =					{
           "langs": [src_lang, tgt_lang],
           "model_max_length": 1024,
           "do_lower_case": do_lower_case,
       }

       print(f'''Generating {fsmt_tokenizer_config_file}'''    )
       with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
              f.write(json.dumps(_SCREAMING_SNAKE_CASE ,ensure_ascii=_SCREAMING_SNAKE_CASE ,indent=_SCREAMING_SNAKE_CASE    )    )

       # model
       lowerCamelCase   :      List[str]			 =					chkpt["models"][0]
       lowerCamelCase   :      Dict			 =					model.state_dict()

       # rename keys to start with 'model.'
       lowerCamelCase   :      List[str]			 =					OrderedDict(("model." + k, v) for k, v in model_state_dict.items()    )

       # remove unneeded keys
       lowerCamelCase   :      Tuple			 =					[
           "model.model",
           "model.encoder.version",
           "model.decoder.version",
           "model.encoder_embed_tokens.weight",
           "model.decoder_embed_tokens.weight",
           "model.encoder.embed_positions._float_tensor",
           "model.decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              model_state_dict.pop(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[int]			 =					FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # check that it loads ok
       model_new.load_state_dict(_SCREAMING_SNAKE_CASE ,strict=_SCREAMING_SNAKE_CASE    )

       # save
       lowerCamelCase   :      Any			 =					os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''Generating {pytorch_weights_dump_path}'''    )
       torch.save(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       print("Conversion is done!"    )
       print("\nLast step is to upload the files to s3"    )
       print(f'''cd {data_root}'''    )
       print(f'''transformers-cli upload {model_dir}'''    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--fsmt_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help=(
               'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
               ' bpecodes, etc.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   parser.parse_args()
       convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)












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__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    Dict					=    KandinskyImgaImgPipeline
						_a							:    List[Any]					=    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
						_a							:    str					=    [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						]
						_a							:    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""",
						]
						_a							:    int					=    False






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 3_2






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.time_input_dim * 4






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 1_0_0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   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=3_7		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=1_0_0_5		,			)

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

											return text_encoder






						@property
						def 				__SCREAMING_SNAKE_CASE(   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(**_A			)
											return model






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return {
											    "block_out_channels": [3_2, 6_4],
											    "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": 1_2,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.dummy_text_encoder
											__lowerCAmelCase				   =       self.dummy_tokenizer
											__lowerCAmelCase				   =       self.dummy_unet
											__lowerCAmelCase				   =       self.dummy_movq

											__lowerCAmelCase				   =       {
											    "num_train_timesteps": 1_0_0_0,
											    "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,
											}

											__lowerCAmelCase				   =       DDIMScheduler(**_A			)

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

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1			)			).to(_A			)
											# create init_image
											__lowerCAmelCase				   =       floats_tensor((1, 3, 6_4, 6_4)		,			rng=random.Random(_A			)			).to(_A			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			)[0]
											__lowerCAmelCase				   =       Image.fromarray(np.uinta(_A			)			).convert("RGB"			).resize((2_5_6, 2_5_6)			)

											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "horse",
											    "image": init_image,
											    "image_embeds": image_embeds,
											    "negative_image_embeds": negative_image_embeds,
											    "generator": generator,
											    "height": 6_4,
											    "width": 6_4,
											    "num_inference_steps": 1_0,
											    "guidance_scale": 7.0,
											    "strength": 0.2,
											    "output_type": "np",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "cpu"

											__lowerCAmelCase				   =       self.get_dummy_components()

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

											pipe.set_progress_bar_config(disable=_A			)

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

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

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

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowerCAmelCase				   =       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 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   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(_A			)

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

											pipeline.set_progress_bar_config(disable=_A			)

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

											__lowerCAmelCase				   =       pipeline(
											    _A		,			image=_A		,			image_embeds=_A		,			negative_image_embeds=_A		,			generator=_A		,			num_inference_steps=1_0_0		,			height=7_6_8		,			width=7_6_8		,			strength=0.2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)




