


'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_   :      Optional[Any]				    =      logging.get_logger(__name__)

a_   :      Optional[Any]				    =      {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
a_   :      Optional[Any]				    =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]						)				->					int:




     '''simple docstring'''



     for attribute in key.split('.'						):
          _a      =							getattr(lowerCAmelCase__			,  lowerCAmelCase__						)

     if weight_type is not None:
          _a      =							getattr(lowerCAmelCase__			,  lowerCAmelCase__						).shape
     else:
          _a      =							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":
          _a      =							value
     elif weight_type == "weight_g":
          _a      =							value
     elif weight_type == "weight_v":
          _a      =							value
     elif weight_type == "bias":
          _a      =							value
     else:
          _a      =							value

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






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     _a      =							[]
     _a      =							fairseq_model.state_dict()

     _a      =							hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =							False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  hf_model.config.feat_extract_norm == 'group'			,  )
               _a      =							True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =							'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'						)[:-1]						) != key):
                              # special case since naming is very similar
                              continue
                         _a      =							True
                         if "*" in mapped_key:
                              _a      =							name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
                              _a      =							mapped_key.replace('*'			,  lowerCAmelCase__						)
                         if "weight_g" in name:
                              _a      =							'weight_g'
                         elif "weight_v" in name:
                              _a      =							'weight_v'
                         elif "bias" in name:
                              _a      =							'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a      =							'weight'
                         else:
                              _a      =							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__					:List[Any]			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Tuple						)				->					List[Any]:




     '''simple docstring'''



     _a      =							full_name.split('conv_layers.'						)[-1]
     _a      =							name.split('.'						)
     _a      =							int(items[0]						)
     _a      =							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.'						)
               _a      =							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.'						)
               _a      =							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[layer_id].layer_norm.bias.data.shape} was found.'						)
               _a      =							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[layer_id].layer_norm.weight.data.shape} was found.'						)
               _a      =							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 		_A			(lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:str=None			,  lowerCAmelCase__					:Optional[Any]=None			,  lowerCAmelCase__					:List[str]=True						)				->					Any:




     '''simple docstring'''



     if config_path is not None:
          _a      =							UniSpeechSatConfig.from_pretrained(lowerCAmelCase__						)
     else:
          _a      =							UniSpeechSatConfig()

     _a      =							''

     if is_finetuned:
          _a      =							UniSpeechSatForCTC(lowerCAmelCase__						)
     else:
          _a      =							UniSpeechSatForPreTraining(lowerCAmelCase__						)

     _a ,				_a ,				_a      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]			,  arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
     _a      =							model[0].eval()

     recursively_load_weights(lowerCAmelCase__			,  lowerCAmelCase__						)

     hf_wavavec.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
 a_   :      List[Any]				    =      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(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 a_   :      Any				    =      parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							[
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          # Removed: 'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertFalse(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							[
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          # Removed: 'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertFalse(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							[
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # pass variant but use the non-variant filenames
      _a      =							[
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertFalse(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							[
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      # pass variant but use the non-variant filenames
      _a      =							[
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )




 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          # 'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertFalse(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )

