


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Dict     						=   logging.get_logger('''transformers.models.speecht5''')

SCREAMING_SNAKE_CASE		:Dict     						=   {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
SCREAMING_SNAKE_CASE		:List[str]     						=   {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
SCREAMING_SNAKE_CASE		:Optional[Any]     						=   {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
SCREAMING_SNAKE_CASE		:int     						=   {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
SCREAMING_SNAKE_CASE		:Any     						=   {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
SCREAMING_SNAKE_CASE		:Any     						=   {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
SCREAMING_SNAKE_CASE		:Tuple     						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE		:List[str]     						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE		:Optional[int]     						=   []
SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
SCREAMING_SNAKE_CASE		:int     						=   IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE		:List[str]     						=   IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Tuple							)->Optional[int]:
   '''simple docstring'''






   for attribute in key.split("."							):
      snake_case_    			=    getattr(lowerCAmelCase_						,  lowerCAmelCase_							)

   if weight_type is not None:
      snake_case_    			=    getattr(lowerCAmelCase_						,  lowerCAmelCase_							).shape
   else:
      snake_case_    			=    hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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":
      snake_case_    			=    value
   elif weight_type == "weight_g":
      snake_case_    			=    value
   elif weight_type == "weight_v":
      snake_case_    			=    value
   elif weight_type == "bias":
      snake_case_    			=    value
   elif weight_type == "running_mean":
      snake_case_    			=    value
   elif weight_type == "running_var":
      snake_case_    			=    value
   elif weight_type == "num_batches_tracked":
      snake_case_    			=    value
   else:
      snake_case_    			=    value

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



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Union[str, Any]							)->Tuple:
   '''simple docstring'''






   for key in ignore_keys:
      if key.endswith(".*"							):
         if name.startswith(key[:-1]							):
            return True
      elif ".*." in key:
         snake_case_    			=    key.split(".*."							)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Any							)->Optional[Any]:
   '''simple docstring'''






   snake_case_    			=    []

   if task == "s2t":
      snake_case_    			=    hf_model.speechta.encoder.prenet.feature_encoder
      snake_case_    			=    MAPPING_S2T
      snake_case_    			=    IGNORE_KEYS_S2T
   elif task == "t2s":
      snake_case_    			=    None
      snake_case_    			=    MAPPING_T2S
      snake_case_    			=    IGNORE_KEYS_T2S
   elif task == "s2s":
      snake_case_    			=    hf_model.speechta.encoder.prenet.feature_encoder
      snake_case_    			=    MAPPING_S2S
      snake_case_    			=    IGNORE_KEYS_S2S
   else:
      raise ValueError(F'''Unsupported task: {task}'''							)

   for name, value in fairseq_dict.items():
      if should_ignore(lowerCAmelCase_						,  lowerCAmelCase_							):
         logger.info(F'''{name} was ignored'''							)
         continue

      snake_case_    			=    False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  hf_model.config.feat_extract_norm == "group"						,  )
         snake_case_    			=    True
      else:
         for key, mapped_key in MAPPING.items():
            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

            if "*" in key:
               snake_case_    			=    key.split(".*."							)
               if prefix in name and suffix in name:
                  snake_case_    			=    suffix

                            # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
            if key in name:
               snake_case_    			=    True
               if "*" in mapped_key:
                  snake_case_    			=    name.split(lowerCAmelCase_							)[0].split("."							)[-2]
                  snake_case_    			=    mapped_key.replace("*"						,  lowerCAmelCase_							)
               if "weight_g" in name:
                  snake_case_    			=    'weight_g'
               elif "weight_v" in name:
                  snake_case_    			=    'weight_v'
               elif "bias" in name:
                  snake_case_    			=    'bias'
               elif "weight" in name:
                  snake_case_    			=    'weight'
               elif "running_mean" in name:
                  snake_case_    			=    'running_mean'
               elif "running_var" in name:
                  snake_case_    			=    'running_var'
               elif "num_batches_tracked" in name:
                  snake_case_    			=    'num_batches_tracked'
               else:
                  snake_case_    			=    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 	_lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Optional[int]							)->List[str]:
   '''simple docstring'''






   snake_case_    			=    full_name.split("conv_layers."							)[-1]
   snake_case_    			=    name.split("."							)
   snake_case_    			=    int(items[0]							)
   snake_case_    			=    int(items[1]							)

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
         snake_case_    			=    value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
         snake_case_    			=    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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
         snake_case_    			=    value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
         snake_case_    			=    value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
   else:
      unused_weights.append(lowerCAmelCase_							)



@torch.no_grad()
def 	_lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:int						,  lowerCAmelCase_							:Any=None						,  lowerCAmelCase_							:List[Any]=None						,  lowerCAmelCase_							:Tuple=None						,  )->Union[str, Any]:
   '''simple docstring'''






   if config_path is not None:
      snake_case_    			=    SpeechTaConfig.from_pretrained(lowerCAmelCase_							)
   else:
      snake_case_    			=    SpeechTaConfig()

   if task == "s2t":
      snake_case_    			=    config.max_text_positions
      snake_case_    			=    SpeechTaForSpeechToText(lowerCAmelCase_							)
   elif task == "t2s":
      snake_case_    			=    1_876
      snake_case_    			=    600
      snake_case_    			=    config.max_speech_positions
      snake_case_    			=    SpeechTaForTextToSpeech(lowerCAmelCase_							)
   elif task == "s2s":
      snake_case_    			=    1_876
      snake_case_    			=    config.max_speech_positions
      snake_case_    			=    SpeechTaForSpeechToSpeech(lowerCAmelCase_							)
   else:
      raise ValueError(F'''Unknown task name: {task}'''							)

   if vocab_path:
      snake_case_    			=    SpeechTaTokenizer(lowerCAmelCase_						,  model_max_length=config.max_text_positions							)

      # Mask token behaves like a normal word, i.e. include the space before it
      snake_case_    			=    AddedToken("<mask>"						,  lstrip=lowerCAmelCase_						,  rstrip=lowerCAmelCase_							)
      snake_case_    			=    mask_token
      tokenizer.add_special_tokens({"mask_token": mask_token}							)
      tokenizer.add_tokens(["<ctc_blank>"]							)

   snake_case_    			=    SpeechTaFeatureExtractor()
   snake_case_    			=    SpeechTaProcessor(tokenizer=lowerCAmelCase_						,  feature_extractor=lowerCAmelCase_							)
   processor.save_pretrained(lowerCAmelCase_							)

   snake_case_    			=    torch.load(lowerCAmelCase_							)
   recursively_load_weights(fairseq_checkpoint["model"]						,  lowerCAmelCase_						,  lowerCAmelCase_							)

   model.save_pretrained(lowerCAmelCase_							)

   if repo_id:
      print("Pushing to the hub..."							)
      processor.push_to_hub(lowerCAmelCase_							)
      model.push_to_hub(lowerCAmelCase_							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE		:Optional[Any]     						=   argparse.ArgumentParser()
   parser.add_argument(
       '''--task''',
       default='''s2t''',
       type=str,
       help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
   )
   parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
   parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   parser.add_argument(
       '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
   )

   SCREAMING_SNAKE_CASE		:str     						=   parser.parse_args()
   convert_speechta_checkpoint(
       args.task,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.vocab_path,
       args.push_to_hub,
   )





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   StableDiffusionDiffEditPipeline
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 lowercase				     =   frozenset(
     []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase				     =   frozenset([]       )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						Dict =					UNetaDConditionModel(
      block_out_channels=(3_2, 6_4)   ,      layers_per_block=2   ,      sample_size=3_2   ,      in_channels=4   ,      out_channels=4   ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,      cross_attention_dim=3_2   ,      attention_head_dim=(2, 4)   ,      use_linear_projection=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					DDIMScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_one=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_zero=snake_case   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					AutoencoderKL(
      block_out_channels=[3_2, 6_4]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=4   ,      sample_size=1_2_8   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					CLIPTextConfig(
      bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=3_2   ,      intermediate_size=3_7   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=1_0_0_0   ,      hidden_act='gelu'   ,      projection_dim=5_1_2   ,      )
  UpperCamelCase_					:						List[str] =					CLIPTextModel(snake_case			)
  UpperCamelCase_					:						List[str] =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  UpperCamelCase_					:						Any =					{
      'unet': unet,
      'scheduler': scheduler,
      'inverse_scheduler': inverse_scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }

  return components

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict   ,      snake_case	:							Tuple=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					floats_tensor((1, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Optional[int] =					floats_tensor((1, 2, 4, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Any =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					{
      'prompt': 'a dog and a newt',
      'mask_image': mask,
      'image_latents': latents,
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict=0			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						int =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						Optional[int] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Optional[int] =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						List[str] =					{
      'image': image,
      'source_prompt': 'a cat and a frog',
      'target_prompt': 'a dog and a newt',
      'generator': generator,
      'num_inference_steps': 2,
      'num_maps_per_mask': 2,
      'mask_encode_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Dict=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Any =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						List[str] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						Any =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						int =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						int =					{
      'image': image,
      'prompt': 'a cat and a frog',
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'decode_latents': True,
      'output_type': 'numpy',
  }
  return inputs

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

  """simple docstring"""





  if not hasattr(self.pipeline_class   ,      '_optional_components'			):
   return

  UpperCamelCase_					:						int =					self.get_dummy_components()
  UpperCamelCase_					:						str =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  # set all optional components to None and update pipeline config accordingly
  for optional_component in pipe._optional_components:
   setattr(snake_case   ,      snake_case   ,      snake_case			)
  pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

  UpperCamelCase_					:						Any =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						Any =					pipe(**snake_case			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(snake_case			)
   UpperCamelCase_					:						int =					self.pipeline_class.from_pretrained(snake_case			)
   pipe_loaded.to(snake_case			)
   pipe_loaded.set_progress_bar_config(disable=snake_case			)

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(snake_case   ,      snake_case			) is None   ,      f"`{optional_component}` did not stay set to None after loading."   ,      )

  UpperCamelCase_					:						Dict =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						int =					pipe_loaded(**snake_case			)[0]

  UpperCamelCase_					:						int =					np.abs(output - output_loaded			).max()
  self.assertLess(snake_case   ,      1e-4			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'cpu'

  UpperCamelCase_					:						Any =					self.get_dummy_components()
  UpperCamelCase_					:						int =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					self.get_dummy_mask_inputs(snake_case			)
  UpperCamelCase_					:						Optional[int] =					pipe.generate_mask(**snake_case			)
  UpperCamelCase_					:						List[str] =					mask[0, -3:, -3:]

  self.assertEqual(mask.shape   ,      (1, 1_6, 1_6)			)
  UpperCamelCase_					:						Tuple =					np.array([0] * 9			)
  UpperCamelCase_					:						int =					np.abs(mask_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)
  self.assertEqual(mask[0, -3, -4]   ,      0			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'cpu'

  UpperCamelCase_					:						List[str] =					self.get_dummy_components()
  UpperCamelCase_					:						List[Any] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Tuple =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						str =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Any =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  super().test_inference_batch_single_identical(expected_max_diff=5e-3			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'cpu'

  UpperCamelCase_					:						Tuple =					self.get_dummy_components()

  UpperCamelCase_					:						int =					{'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
  UpperCamelCase_					:						str =					DPMSolverMultistepScheduler(**snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler(**snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Any =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Any =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						int =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Union[str, Any] =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





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

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

  UpperCamelCase_					:						Any =					raw_image.convert('RGB'			).resize((7_6_8, 7_6_8)			)

  UpperCamelCase_					:						Optional[Any] =					raw_image

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[str] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						int =					DDIMScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Any =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						int =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case			).latents

  UpperCamelCase_					:						Optional[int] =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						Any =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[Any] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						Optional[int] =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						str =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Union[str, Any] =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						Any =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case   ,      num_inference_steps=2_5   ,      ).latents

  UpperCamelCase_					:						int =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      num_inference_steps=2_5   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						str =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1



