

'''simple docstring'''
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_snake_case         =  [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

_snake_case         =  [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

_snake_case         =  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
    # loop over downblocks/upblocks

    for j in range(2):
        # loop over resnets/attentions for downblocks
        _snake_case         =  F'''down_blocks.{i}.resnets.{j}.'''
        _snake_case         =  F'''input_blocks.{3*i + j + 1}.0.'''
        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

        if i < 3:
            # no attention layers in down_blocks.3
            _snake_case         =  F'''down_blocks.{i}.attentions.{j}.'''
            _snake_case         =  F'''input_blocks.{3*i + j + 1}.1.'''
            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

    for j in range(3):
        # loop over resnets/attentions for upblocks
        _snake_case         =  F'''up_blocks.{i}.resnets.{j}.'''
        _snake_case         =  F'''output_blocks.{3*i + j}.0.'''
        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

        if i > 0:
            # no attention layers in up_blocks.0
            _snake_case         =  F'''up_blocks.{i}.attentions.{j}.'''
            _snake_case         =  F'''output_blocks.{3*i + j}.1.'''
            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

    if i < 3:
        # no downsample in down_blocks.3
        _snake_case         =  F'''down_blocks.{i}.downsamplers.0.conv.'''
        _snake_case         =  F'''input_blocks.{3*(i+1)}.0.op.'''
        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

        # no upsample in up_blocks.3
        _snake_case         =  F'''up_blocks.{i}.upsamplers.0.'''
        _snake_case         =  F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_snake_case         =  'mid_block.attentions.0.'
_snake_case         =  'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    _snake_case         =  F'''mid_block.resnets.{j}.'''
    _snake_case         =  F'''middle_block.{2*j}.'''
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def  _A		(     snake_case					)      ->     Tuple:
   # buyer beware: this is a *brittle* function,
   # and correct output requires that all of these pieces interact in
   # the exact order in which I have arranged them.
   _lowercase      :				List[str]      					=       {k: k for k in unet_state_dict.keys()}
   for sd_name, hf_name in unet_conversion_map:
      _lowercase      :				Dict      					=       sd_name
   for k, v in mapping.items():
      if "resnets" in k:
         for sd_part, hf_part in unet_conversion_map_resnet:
            _lowercase      :				Optional[int]      					=       v.replace(snake_case		,							snake_case					)
         _lowercase      :				List[Any]      					=       v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         _lowercase      :				str      					=       v.replace(snake_case		,							snake_case					)
      _lowercase      :				List[str]      					=       v
   _lowercase      :				Tuple      					=       {v: unet_state_dict[k] for k, v in mapping.items()}
   return new_state_dict


# ================#
# VAE Conversion #
# ================#

_snake_case         =  [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
    # down_blocks have two resnets
    for j in range(2):
        _snake_case         =  F'''encoder.down_blocks.{i}.resnets.{j}.'''
        _snake_case         =  F'''encoder.down.{i}.block.{j}.'''
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        _snake_case         =  F'''down_blocks.{i}.downsamplers.0.'''
        _snake_case         =  F'''down.{i}.downsample.'''
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        _snake_case         =  F'''up_blocks.{i}.upsamplers.0.'''
        _snake_case         =  F'''up.{3-i}.upsample.'''
        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

    # up_blocks have three resnets
    # also, up blocks in hf are numbered in reverse from sd
    for j in range(3):
        _snake_case         =  F'''decoder.up_blocks.{i}.resnets.{j}.'''
        _snake_case         =  F'''decoder.up.{3-i}.block.{j}.'''
        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
    _snake_case         =  F'''mid_block.resnets.{i}.'''
    _snake_case         =  F'''mid.block_{i+1}.'''
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_snake_case         =  [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]







def  _A		(     snake_case					)      ->     Dict:
   # convert HF linear weights to SD conv2d weights
   return w.reshape(*w.shape		,							1		,							1					)







def  _A		(     snake_case					)      ->     int:
   _lowercase      :				int      					=       {k: k for k in vae_state_dict.keys()}
   for k, v in mapping.items():
      for sd_part, hf_part in vae_conversion_map:
         _lowercase      :				Any      					=       v.replace(snake_case		,							snake_case					)
      _lowercase      :				List[str]      					=       v
   for k, v in mapping.items():
      if "attentions" in k:
         for sd_part, hf_part in vae_conversion_map_attn:
            _lowercase      :				Optional[int]      					=       v.replace(snake_case		,							snake_case					)
         _lowercase      :				Optional[Any]      					=       v
   _lowercase      :				List[str]      					=       {v: vae_state_dict[k] for k, v in mapping.items()}
   _lowercase      :				Union[str, Any]      					=       ["q", "k", "v", "proj_out"]
   for k, v in new_state_dict.items():
      for weight_name in weights_to_convert:
         if F'''mid.attn_1.{weight_name}.weight''' in k:
            print(F'''Reshaping {k} for SD format'''					)
            _lowercase      :				List[Any]      					=       reshape_weight_for_sd(snake_case					)
   return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_snake_case         =  [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
_snake_case         =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_snake_case         =  re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_snake_case         =  {'q': 0, 'k': 1, 'v': 2}







def  _A		(     snake_case					)      ->     List[Any]:
   _lowercase      :				Optional[int]      					=       {}
   _lowercase      :				Dict      					=       {}
   _lowercase      :				int      					=       {}
   for k, v in text_enc_dict.items():
      if (
          k.endswith(".self_attn.q_proj.weight"					)
          or k.endswith(".self_attn.k_proj.weight"					)
          or k.endswith(".self_attn.v_proj.weight"					)
      ):
         _lowercase      :				Optional[int]      					=       k[: -len(".q_proj.weight"					)]
         _lowercase      :				Any      					=       k[-len("q_proj.weight"					)]
         if k_pre not in capture_qkv_weight:
            _lowercase      :				Optional[int]      					=       [None, None, None]
         _lowercase      :				Any      					=       v
         continue

      if (
          k.endswith(".self_attn.q_proj.bias"					)
          or k.endswith(".self_attn.k_proj.bias"					)
          or k.endswith(".self_attn.v_proj.bias"					)
      ):
         _lowercase      :				Tuple      					=       k[: -len(".q_proj.bias"					)]
         _lowercase      :				Dict      					=       k[-len("q_proj.bias"					)]
         if k_pre not in capture_qkv_bias:
            _lowercase      :				Union[str, Any]      					=       [None, None, None]
         _lowercase      :				str      					=       v
         continue

      _lowercase      :				Tuple      					=       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]		,							snake_case					)
      _lowercase      :				Union[str, Any]      					=       v

   for k_pre, tensors in capture_qkv_weight.items():
      if None in tensors:
         raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"					)
      _lowercase      :				Optional[int]      					=       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]		,							snake_case					)
      _lowercase      :				List[Any]      					=       torch.cat(snake_case					)

   for k_pre, tensors in capture_qkv_bias.items():
      if None in tensors:
         raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"					)
      _lowercase      :				Dict      					=       textenc_pattern.sub(lambda snake_case					: protected[re.escape(m.group(0					)					)]		,							snake_case					)
      _lowercase      :				Dict      					=       torch.cat(snake_case					)

   return new_state_dict







def  _A		(     snake_case					)      ->     Optional[int]:
   return text_enc_dict


if __name__ == "__main__":
    _snake_case         =  argparse.ArgumentParser()

    parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
    parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
    parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
    parser.add_argument(
        '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
    )

    _snake_case         =  parser.parse_args()

    assert args.model_path is not None, "Must provide a model path!"

    assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

    # Path for safetensors
    _snake_case         =  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
    _snake_case         =  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
    _snake_case         =  osp.join(args.model_path, 'text_encoder', 'model.safetensors')

    # Load models from safetensors if it exists, if it doesn't pytorch
    if osp.exists(unet_path):
        _snake_case         =  load_file(unet_path, device='cpu')
    else:
        _snake_case         =  osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
        _snake_case         =  torch.load(unet_path, map_location='cpu')

    if osp.exists(vae_path):
        _snake_case         =  load_file(vae_path, device='cpu')
    else:
        _snake_case         =  osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
        _snake_case         =  torch.load(vae_path, map_location='cpu')

    if osp.exists(text_enc_path):
        _snake_case         =  load_file(text_enc_path, device='cpu')
    else:
        _snake_case         =  osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
        _snake_case         =  torch.load(text_enc_path, map_location='cpu')

    # Convert the UNet model
    _snake_case         =  convert_unet_state_dict(unet_state_dict)
    _snake_case         =  {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

    # Convert the VAE model
    _snake_case         =  convert_vae_state_dict(vae_state_dict)
    _snake_case         =  {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
    _snake_case         =  'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

    if is_vaa_model:
        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
        _snake_case         =  {'transformer.' + k: v for k, v in text_enc_dict.items()}
        _snake_case         =  convert_text_enc_state_dict_vaa(text_enc_dict)
        _snake_case         =  {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
    else:
        _snake_case         =  convert_text_enc_state_dict(text_enc_dict)
        _snake_case         =  {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

    # Put together new checkpoint
    _snake_case         =  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
    if args.half:
        _snake_case         =  {k: v.half() for k, v in state_dict.items()}

    if args.use_safetensors:
        save_file(state_dict, args.checkpoint_path)
    else:
        _snake_case         =  {'state_dict': state_dict}
        torch.save(state_dict, args.checkpoint_path)









'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					int     	=							'Wav2Vec2FeatureExtractor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       self.feature_extractor
      _lowercase      :				Optional[Any]      					=       False


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      try:
         return super().from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      except OSError:
         warnings.warn(
             f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
             " include a `tokenizer_class` attribute is deprecated and will be "
             "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
             " attribute to either your `config.json` or `tokenizer_config.json` "
             "file to suppress this warning: "      ,    _UpperCamelCase      ,    )

         _lowercase      :				Dict      					=       WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
         _lowercase      :				str      					=       WavaVecaCTCTokenizer.from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )

         return cls(feature_extractor=_UpperCamelCase      ,    tokenizer=_UpperCamelCase  )


   def __call__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if self._in_target_context_manager:
         return self.current_processor(*_UpperCamelCase      ,    **_UpperCamelCase  )

      if "raw_speech" in kwargs:
         warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."  )
         _lowercase      :				int      					=       kwargs.pop("raw_speech"  )
      else:
         _lowercase      :				List[Any]      					=       kwargs.pop("audio"      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       kwargs.pop("sampling_rate"      ,    _UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       kwargs.pop("text"      ,    _UpperCamelCase  )
      if len(_UpperCamelCase  ) > 0:
         _lowercase      :				int      					=       args[0]
         _lowercase      :				Any      					=       args[1:]

      if audio is None and text is None:
         raise ValueError("You need to specify either an `audio` or `text` input to process."  )

      if audio is not None:
         _lowercase      :				Dict      					=       self.feature_extractor(_UpperCamelCase      ,    *_UpperCamelCase      ,    sampling_rate=_UpperCamelCase      ,    **_UpperCamelCase  )
      if text is not None:
         _lowercase      :				Union[str, Any]      					=       self.tokenizer(_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is None:
         return inputs
      elif audio is None:
         return encodings
      else:
         _lowercase      :				int      					=       encodings["input_ids"]
         return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if self._in_target_context_manager:
         return self.current_processor.pad(*_UpperCamelCase      ,    **_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       kwargs.pop("input_features"      ,    _UpperCamelCase  )
      _lowercase      :				Any      					=       kwargs.pop("labels"      ,    _UpperCamelCase  )
      if len(_UpperCamelCase  ) > 0:
         _lowercase      :				Any      					=       args[0]
         _lowercase      :				Any      					=       args[1:]

      if input_features is not None:
         _lowercase      :				Any      					=       self.feature_extractor.pad(_UpperCamelCase      ,    *_UpperCamelCase      ,    **_UpperCamelCase  )
      if labels is not None:
         _lowercase      :				int      					=       self.tokenizer.pad(_UpperCamelCase      ,    **_UpperCamelCase  )

      if labels is None:
         return input_features
      elif input_features is None:
         return labels
      else:
         _lowercase      :				Optional[Any]      					=       labels["input_ids"]
         return input_features


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )






   @contextmanager
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
          "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
          "your audio inputs, or in a separate call."  )
      _lowercase      :				Optional[Any]      					=       True
      _lowercase      :				Dict      					=       self.tokenizer
      yield
      _lowercase      :				List[str]      					=       self.feature_extractor
      _lowercase      :				List[str]      					=       False






