

"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_A							=  logging.get_logger(__name__)

_A							=  {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[Any]:
      for attribute in key.split("""."""    ):
            UpperCAmelCase__			:							str							=    getattr(lowerCAmelCase			,	lowerCAmelCase    )

      if weight_type is not None:
            UpperCAmelCase__			:							Union[str, Any]							=    getattr(lowerCAmelCase			,	lowerCAmelCase    ).shape
      else:
            UpperCAmelCase__			:							int							=    hf_pointer.shape

      assert hf_shape == value.shape, (
          F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
          F""" {value.shape} for {full_name}"""
      )

      if weight_type == "weight":
            UpperCAmelCase__			:							Dict							=    value
      elif weight_type == "weight_g":
            UpperCAmelCase__			:							List[Any]							=    value
      elif weight_type == "weight_v":
            UpperCAmelCase__			:							Optional[Any]							=    value
      elif weight_type == "bias":
            UpperCAmelCase__			:							Dict							=    value
      else:
            UpperCAmelCase__			:							Optional[Any]							=    value

      logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> List[str]:
      UpperCAmelCase__			:							int							=    []
      UpperCAmelCase__			:							Dict							=    fairseq_model.state_dict()

      UpperCAmelCase__			:							str							=    hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase__			:							Optional[Any]							=    False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	hf_model.config.feat_extract_norm == """group"""			,	)
                  UpperCAmelCase__			:							Optional[Any]							=    True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase__			:							List[str]							=    """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                        if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
                              UpperCAmelCase__			:							List[Any]							=    True
                              if "*" in mapped_key:
                                    UpperCAmelCase__			:							int							=    name.split(lowerCAmelCase    )[0].split("""."""    )[-2]
                                    UpperCAmelCase__			:							List[str]							=    mapped_key.replace("""*"""			,	lowerCAmelCase    )
                              if "weight_g" in name:
                                    UpperCAmelCase__			:							List[Any]							=    """weight_g"""
                              elif "weight_v" in name:
                                    UpperCAmelCase__			:							Any							=    """weight_v"""
                              elif "weight" in name:
                                    UpperCAmelCase__			:							Tuple							=    """weight"""
                              elif "bias" in name:
                                    UpperCAmelCase__			:							int							=    """bias"""
                              else:
                                    UpperCAmelCase__			:							List[str]							=    None
                              set_recursively(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase    )

      logger.warning(F"""Unused weights: {unused_weights}"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> List[Any]:
      UpperCAmelCase__			:							List[str]							=    full_name.split("""conv_layers."""    )[-1]
      UpperCAmelCase__			:							str							=    name.split("""."""    )
      UpperCAmelCase__			:							Tuple							=    int(items[0]    )
      UpperCAmelCase__			:							Any							=    int(items[1]    )

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  UpperCAmelCase__			:							Union[str, Any]							=    value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  UpperCAmelCase__			:							List[Any]							=    value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  UpperCAmelCase__			:							Dict							=    value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  UpperCAmelCase__			:							int							=    value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
      else:
            unused_weights.append(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							Optional[Any]							=    SEWConfig()
      if is_finetuned:
            UpperCAmelCase__			:							int							=    model.wav_encoder.wav_model.cfg
      else:
            UpperCAmelCase__			:							Dict							=    model.cfg

      UpperCAmelCase__			:							Union[str, Any]							=    fs_config.conv_bias
      UpperCAmelCase__			:							int							=    eval(fs_config.conv_feature_layers    )
      UpperCAmelCase__			:							List[Any]							=    [x[0] for x in conv_layers]
      UpperCAmelCase__			:							List[Any]							=    [x[1] for x in conv_layers]
      UpperCAmelCase__			:							List[Any]							=    [x[2] for x in conv_layers]
      UpperCAmelCase__			:							List[Any]							=    """gelu"""
      UpperCAmelCase__			:							int							=    """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      UpperCAmelCase__			:							int							=    0.0
      UpperCAmelCase__			:							Optional[Any]							=    fs_config.activation_fn.name
      UpperCAmelCase__			:							Dict							=    fs_config.encoder_embed_dim
      UpperCAmelCase__			:							List[Any]							=    0.02
      UpperCAmelCase__			:							int							=    fs_config.encoder_ffn_embed_dim
      UpperCAmelCase__			:							Dict							=    1E-5
      UpperCAmelCase__			:							List[Any]							=    fs_config.encoder_layerdrop
      UpperCAmelCase__			:							int							=    fs_config.encoder_attention_heads
      UpperCAmelCase__			:							Union[str, Any]							=    fs_config.conv_pos_groups
      UpperCAmelCase__			:							Any							=    fs_config.conv_pos
      UpperCAmelCase__			:							Union[str, Any]							=    len(lowerCAmelCase    )
      UpperCAmelCase__			:							Union[str, Any]							=    fs_config.encoder_layers
      UpperCAmelCase__			:							Tuple							=    fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            UpperCAmelCase__			:							List[Any]							=    model.cfg
            UpperCAmelCase__			:							List[str]							=    fs_config.final_dropout
            UpperCAmelCase__			:							Tuple							=    fs_config.layerdrop
      UpperCAmelCase__			:							Any							=    fs_config.activation_dropout
      UpperCAmelCase__			:							Optional[Any]							=    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      UpperCAmelCase__			:							List[str]							=    fs_config.attention_dropout
      UpperCAmelCase__			:							Union[str, Any]							=    fs_config.dropout_input
      UpperCAmelCase__			:							Any							=    fs_config.dropout
      UpperCAmelCase__			:							Any							=    fs_config.mask_channel_length
      UpperCAmelCase__			:							Tuple							=    fs_config.mask_channel_prob
      UpperCAmelCase__			:							Any							=    fs_config.mask_length
      UpperCAmelCase__			:							Any							=    fs_config.mask_prob

      UpperCAmelCase__			:							Any							=    """Wav2Vec2FeatureExtractor"""
      UpperCAmelCase__			:							Optional[int]							=    """Wav2Vec2CTCTokenizer"""

      return config




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=None			,	lowerCAmelCase=None			,	lowerCAmelCase=True    )	-> Optional[Any]:

      if is_finetuned:
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
      else:
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							Any							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

      if config_path is not None:
            UpperCAmelCase__			:							Optional[int]							=    SEWConfig.from_pretrained(lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							Optional[Any]							=    convert_config(model[0]			,	lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[Any]							=    model[0].eval()

      UpperCAmelCase__			:							Tuple							=    True if config.feat_extract_norm == """layer""" else False
      UpperCAmelCase__			:							str							=    WavaVecaFeatureExtractor(
          feature_size=1			,	sampling_rate=1_60_00			,	padding_value=0			,	do_normalize=lowerCAmelCase			,	return_attention_mask=lowerCAmelCase			,	)

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase__			:							str							=    Dictionary.load(lowerCAmelCase    )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase__			:							int							=    target_dict.pad_index
                  UpperCAmelCase__			:							List[Any]							=    target_dict.bos_index
                  UpperCAmelCase__			:							Optional[int]							=    target_dict.pad_index
                  UpperCAmelCase__			:							List[Any]							=    target_dict.bos_index
                  UpperCAmelCase__			:							Any							=    target_dict.eos_index
                  UpperCAmelCase__			:							Union[str, Any]							=    len(target_dict.symbols    )
                  UpperCAmelCase__			:							str							=    os.path.join(lowerCAmelCase			,	"""vocab.json"""    )
                  if not os.path.isdir(lowerCAmelCase    ):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase    )    )
                        return
                  os.makedirs(lowerCAmelCase			,	exist_ok=lowerCAmelCase    )
                  with open(lowerCAmelCase			,	"""w"""			,	encoding="""utf-8"""    ) as vocab_handle:
                        json.dump(target_dict.indices			,	lowerCAmelCase    )
                  UpperCAmelCase__			:							str							=    WavaVecaCTCTokenizer(
                      lowerCAmelCase			,	unk_token=target_dict.unk_word			,	pad_token=target_dict.pad_word			,	bos_token=target_dict.bos_word			,	eos_token=target_dict.eos_word			,	word_delimiter_token="""|"""			,	do_lower_case=lowerCAmelCase			,	)
                  UpperCAmelCase__			:							Optional[int]							=    WavaVecaProcessor(feature_extractor=lowerCAmelCase			,	tokenizer=lowerCAmelCase    )
                  processor.save_pretrained(lowerCAmelCase    )

            UpperCAmelCase__			:							int							=    SEWForCTC(lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							str							=    SEWModel(lowerCAmelCase    )
            feature_extractor.save_pretrained(lowerCAmelCase    )

      recursively_load_weights(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )

      hf_model.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
    parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
    )
    _A							=  parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )




"""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, 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       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  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"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    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							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """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,
        }

        UpperCAmelCase__			:							List[Any]							=    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	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

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

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """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  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

        print(F"""image.shape {image.shape}"""	)

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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  _a      (self	):


        """simple docstring"""

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




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

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

        UpperCAmelCase__			:							Union[str, Any]							=    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
        UpperCAmelCase__			:							List[str]							=    np.ones((768, 768)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

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

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

