"""simple docstring"""




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()
_a   :	Optional[int] 					=     logging.get_logger('transformers.models.speecht5')

_a   :	List[str] 					=     {
    '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',
}
_a   :	Optional[int] 					=     {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_a   :	Tuple 					=     {
    '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',
}
_a   :	List[str] 					=     {
    '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',
}
_a   :	Dict 					=     {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_a   :	Any 					=     {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_a   :	Dict 					=     {
    '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',
}
_a   :	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',
}
_a   :	Tuple 					=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a   :	Tuple 					=     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a   :	Tuple 					=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a   :	Optional[Any] 					=     []
_a   :	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',
]
_a   :	Optional[Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_a   :	Union[str, Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_a   :	List[Any] 					=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: str    ,_lowerCamelCase		: List[Any]			) ->   Optional[int]:
  for attribute in key.split("""."""			):
    _lowerCAmelCase		: Optional[Any]						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)

  if weight_type is not None:
    _lowerCAmelCase		: str						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			).shape
  else:
    _lowerCAmelCase		: int						    =  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":
    _lowerCAmelCase		: List[str]						    =  value
  elif weight_type == "weight_g":
    _lowerCAmelCase		: Optional[Any]						    =  value
  elif weight_type == "weight_v":
    _lowerCAmelCase		: Optional[Any]						    =  value
  elif weight_type == "bias":
    _lowerCAmelCase		: Tuple						    =  value
  elif weight_type == "running_mean":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "running_var":
    _lowerCAmelCase		: str						    =  value
  elif weight_type == "num_batches_tracked":
    _lowerCAmelCase		: Dict						    =  value
  else:
    _lowerCAmelCase		: List[Any]						    =  value

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any			) ->   Dict:
  for key in ignore_keys:
    if key.endswith(""".*"""			):
      if name.startswith(key[:-1]			):
        return True
    elif ".*." in key:
      _lowerCAmelCase		: Union[str, Any]						    =  key.split(""".*."""			)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Union[str, Any]			) ->   str:
  _lowerCAmelCase		: str						    =  []

  if task == "s2t":
    _lowerCAmelCase		: Tuple						    =  hf_model.speechta.encoder.prenet.feature_encoder
    _lowerCAmelCase		: str						    =  MAPPING_S2T
    _lowerCAmelCase		: Union[str, Any]						    =  IGNORE_KEYS_S2T
  elif task == "t2s":
    _lowerCAmelCase		: Tuple						    =  None
    _lowerCAmelCase		: Optional[int]						    =  MAPPING_T2S
    _lowerCAmelCase		: Dict						    =  IGNORE_KEYS_T2S
  elif task == "s2s":
    _lowerCAmelCase		: Dict						    =  hf_model.speechta.encoder.prenet.feature_encoder
    _lowerCAmelCase		: str						    =  MAPPING_S2S
    _lowerCAmelCase		: List[str]						    =  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

    _lowerCAmelCase		: Union[str, Any]						    =  False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,hf_model.config.feat_extract_norm == """group"""    ,)
      _lowerCAmelCase		: int						    =  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:
          _lowerCAmelCase		: Optional[Any]						    =  key.split(""".*."""			)
          if prefix in name and suffix in name:
            _lowerCAmelCase		: List[Any]						    =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _lowerCAmelCase		: List[str]						    =  True
          if "*" in mapped_key:
            _lowerCAmelCase		: Optional[Any]						    =  name.split(_lowerCamelCase			)[0].split("""."""			)[-2]
            _lowerCAmelCase		: List[Any]						    =  mapped_key.replace("""*"""    ,_lowerCamelCase			)
          if "weight_g" in name:
            _lowerCAmelCase		: Tuple						    =  """weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase		: Optional[int]						    =  """weight_v"""
          elif "bias" in name:
            _lowerCAmelCase		: Dict						    =  """bias"""
          elif "weight" in name:
            _lowerCAmelCase		: Dict						    =  """weight"""
          elif "running_mean" in name:
            _lowerCAmelCase		: str						    =  """running_mean"""
          elif "running_var" in name:
            _lowerCAmelCase		: int						    =  """running_var"""
          elif "num_batches_tracked" in name:
            _lowerCAmelCase		: Optional[int]						    =  """num_batches_tracked"""
          else:
            _lowerCAmelCase		: Union[str, Any]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Any			) ->   Optional[int]:
  _lowerCAmelCase		: List[str]						    =  full_name.split("""conv_layers."""			)[-1]
  _lowerCAmelCase		: List[Any]						    =  name.split("""."""			)
  _lowerCAmelCase		: str						    =  int(items[0]			)
  _lowerCAmelCase		: int						    =  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."			)
      _lowerCAmelCase		: List[Any]						    =  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."			)
      _lowerCAmelCase		: Optional[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:
      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."			)
      _lowerCAmelCase		: Any						    =  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."			)
      _lowerCAmelCase		: List[str]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Any=None    ,_lowerCamelCase		: Optional[Any]=None    ,_lowerCamelCase		: Optional[int]=None    ,) ->   Union[str, Any]:
  if config_path is not None:
    _lowerCAmelCase		: Dict						    =  SpeechTaConfig.from_pretrained(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  SpeechTaConfig()

  if task == "s2t":
    _lowerCAmelCase		: List[str]						    =  config.max_text_positions
    _lowerCAmelCase		: Tuple						    =  SpeechTaForSpeechToText(_lowerCamelCase			)
  elif task == "t2s":
    _lowerCAmelCase		: Union[str, Any]						    =  1876
    _lowerCAmelCase		: Tuple						    =  600
    _lowerCAmelCase		: Any						    =  config.max_speech_positions
    _lowerCAmelCase		: Optional[int]						    =  SpeechTaForTextToSpeech(_lowerCamelCase			)
  elif task == "s2s":
    _lowerCAmelCase		: int						    =  1876
    _lowerCAmelCase		: Optional[int]						    =  config.max_speech_positions
    _lowerCAmelCase		: Optional[int]						    =  SpeechTaForSpeechToSpeech(_lowerCamelCase			)
  else:
    raise ValueError(f"Unknown task name: {task}"			)

  if vocab_path:
    _lowerCAmelCase		: List[str]						    =  SpeechTaTokenizer(_lowerCamelCase    ,model_max_length=config.max_text_positions			)

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

  _lowerCAmelCase		: str						    =  SpeechTaFeatureExtractor()
  _lowerCAmelCase		: Optional[int]						    =  SpeechTaProcessor(tokenizer=_lowerCamelCase    ,feature_extractor=_lowerCamelCase			)
  processor.save_pretrained(_lowerCamelCase			)

  _lowerCAmelCase		: List[str]						    =  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__":
     _a   :	Optional[int] 					=     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.'
     )

     _a   :	Optional[int] 					=     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,
     )

"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_a   :	Union[str, Any] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_a   :	int 					=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Dict=8			) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase		: Optional[Any]						    =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any=512    ,_lowerCamelCase		: Dict=512			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1			)
  _lowerCAmelCase		: Dict						    =  np.array(pil_image.convert("""RGB"""			)			)
  _lowerCAmelCase		: List[str]						    =  arr.astype(np.floataa			) / 1_27.5 - 1
  _lowerCAmelCase		: int						    =  np.transpose(_lowerCamelCase    ,[2, 0, 1]			)
  _lowerCAmelCase		: Optional[Any]						    =  torch.from_numpy(_lowerCamelCase			).unsqueeze(0			)
  return image






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       self.register_modules(
           unet=a__       ,  scheduler=a__       ,  movq=a__       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  2 ** (len(self.movq.config.block_out_channels						) - 1)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: Optional[Any]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

       _lowerCAmelCase		: List[Any]						    =  max(num_inference_steps - init_timestep       ,  0						)
       _lowerCAmelCase		: Dict						    =  self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       if not isinstance(a__       ,  (torch.Tensor, PIL.Image.Image, list)						):
         raise ValueError(
             F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__						)}"						)

       _lowerCAmelCase		: Union[str, Any]						    =  image.to(device=a__       ,  dtype=a__						)

       _lowerCAmelCase		: int						    =  batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         _lowerCAmelCase		: int						    =  image

       else:
         if isinstance(a__       ,  a__						) and len(a__						) != batch_size:
           raise ValueError(
               F"You have passed a list of generators of length {len(a__						)}, but requested an effective batch"
               F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

         elif isinstance(a__       ,  a__						):
           _lowerCAmelCase		: Optional[int]						    =  [
               self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(a__						)
           ]
           _lowerCAmelCase		: Optional[int]						    =  torch.cat(a__       ,  dim=0						)
         else:
           _lowerCAmelCase		: List[Any]						    =  self.movq.encode(a__						).latent_dist.sample(a__						)

         _lowerCAmelCase		: Dict						    =  self.movq.config.scaling_factor * init_latents

       _lowerCAmelCase		: str						    =  torch.cat([init_latents]       ,  dim=0						)

       _lowerCAmelCase		: Dict						    =  init_latents.shape
       _lowerCAmelCase		: str						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

       # get latents
       _lowerCAmelCase		: Optional[Any]						    =  self.scheduler.add_noise(a__       ,  a__       ,  a__						)

       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

       _lowerCAmelCase		: str						    =  torch.device(F"cuda:{gpu_id}"						)

       _lowerCAmelCase		: int						    =  [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(a__       ,  a__						)






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available() and is_accelerate_version(""">="""       ,  """0.17.0.dev0"""						):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

       _lowerCAmelCase		: Optional[int]						    =  torch.device(F"cuda:{gpu_id}"						)

       if self.device.type != "cpu":
         self.to("""cpu"""       ,  silence_dtype_warnings=a__						)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _lowerCAmelCase		: List[str]						    =  None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  cpu_offload_with_hook(a__       ,  a__       ,  prev_module_hook=a__						)

       # We'll offload the last model manually.
       _lowerCAmelCase		: Tuple						    =  hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       if not hasattr(self.unet       ,  """_hf_hook"""						):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(a__       ,  """_hf_hook"""						)
             and hasattr(module._hf_hook       ,  """execution_device"""						)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device						)
       return self.device






     @torch.no_grad()
     @replace_example_docstring(a__						)
     def __call__(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 100       ,  a__ = 4.0       ,  a__ = 0.3       ,  a__ = 1       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Dict						    =  self._execution_device

       _lowerCAmelCase		: Optional[Any]						    =  guidance_scale > 1.0

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Dict						    =  torch.cat(a__       ,  dim=0						)
       _lowerCAmelCase		: Dict						    =  image_embeds.shape[0]
       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat(a__       ,  dim=0						)

       if do_classifier_free_guidance:
         _lowerCAmelCase		: int						    =  image_embeds.repeat_interleave(a__       ,  dim=0						)
         _lowerCAmelCase		: Any						    =  negative_image_embeds.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Optional[int]						    =  torch.cat([negative_image_embeds, image_embeds]       ,  dim=0						).to(dtype=self.unet.dtype       ,  device=a__						)

       if not isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Any						    =  [image]
       if not all(isinstance(a__       ,  (PIL.Image.Image, torch.Tensor)						) for i in image						):
         raise ValueError(
             F"Input is in incorrect format: {[type(a__						) for i in image]}. Currently, we only support  PIL image and pytorch tensor"						)

       _lowerCAmelCase		: Tuple						    =  torch.cat([prepare_image(a__       ,  a__       ,  a__						) for i in image]       ,  dim=0						)
       _lowerCAmelCase		: Union[str, Any]						    =  image.to(dtype=image_embeds.dtype       ,  device=a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  self.movq.encode(a__						)["""latents"""]
       _lowerCAmelCase		: Tuple						    =  latents.repeat_interleave(a__       ,  dim=0						)
       self.scheduler.set_timesteps(a__       ,  device=a__						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  self.get_timesteps(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  downscale_height_and_width(a__       ,  a__       ,  self.movq_scale_factor						)
       _lowerCAmelCase		: List[str]						    =  self.prepare_latents(
           a__       ,  a__       ,  a__       ,  a__       ,  image_embeds.dtype       ,  a__       ,  a__						)
       for i, t in enumerate(self.progress_bar(a__						)						):
         # expand the latents if we are doing classifier free guidance
         _lowerCAmelCase		: List[str]						    =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

         _lowerCAmelCase		: int						    =  {"""image_embeds""": image_embeds}
         _lowerCAmelCase		: List[str]						    =  self.unet(
             sample=a__       ,  timestep=a__       ,  encoder_hidden_states=a__       ,  added_cond_kwargs=a__       ,  return_dict=a__       ,  )[0]

         if do_classifier_free_guidance:
           _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  noise_pred.chunk(2						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  variance_pred.chunk(2						)
           _lowerCAmelCase		: List[str]						    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           _lowerCAmelCase		: List[str]						    =  torch.cat([noise_pred, variance_pred_text]       ,  dim=1						)

         if not (
             hasattr(self.scheduler.config       ,  """variance_type"""						)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
           _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase		: List[str]						    =  self.scheduler.step(
             a__       ,  a__       ,  a__       ,  generator=a__       ,  )[0]

       # post-processing
       _lowerCAmelCase		: int						    =  self.movq.decode(a__       ,  force_not_quantize=a__						)["""sample"""]

       if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"						)

       if output_type in ["np", "pil"]:
         _lowerCAmelCase		: List[Any]						    =  image * 0.5 + 0.5
         _lowerCAmelCase		: Any						    =  image.clamp(0       ,  1						)
         _lowerCAmelCase		: Tuple						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						).float().numpy()

       if output_type == "pil":
         _lowerCAmelCase		: List[str]						    =  self.numpy_to_pil(a__						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__						)




