


"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase__				    =       {
    '''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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase__				    =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  for attribute in key.split("."    ):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        lowerCAmelCase					:					Any								=							"""lm_head"""

    lowerCAmelCase					:					int								=							getattr(lowerCAmelCase__    ,				lowerCAmelCase__    )

  if weight_type is not None:
    lowerCAmelCase					:					str								=							getattr(lowerCAmelCase__    ,				lowerCAmelCase__    ).shape
  else:
    lowerCAmelCase					:					str								=							hf_pointer.shape

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

  if weight_type == "weight":
    lowerCAmelCase					:					Union[str, Any]								=							value
  elif weight_type == "weight_g":
    lowerCAmelCase					:					List[Any]								=							value
  elif weight_type == "weight_v":
    lowerCAmelCase					:					Optional[Any]								=							value
  elif weight_type == "bias":
    lowerCAmelCase					:					List[str]								=							value
  else:
    lowerCAmelCase					:					Dict								=							value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							[]
  lowerCAmelCase					:					List[str]								=							fairseq_model.state_dict()

  lowerCAmelCase					:					Tuple								=							hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase					:					Optional[int]								=							False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				hf_model.config.feat_extract_norm == "group"    ,				)
      lowerCAmelCase					:					Optional[Any]								=							True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCAmelCase					:					Optional[Any]								=							"""unispeech.""" + 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]:
          lowerCAmelCase					:					Optional[int]								=							True
          if "*" in mapped_key:
            lowerCAmelCase					:					List[str]								=							name.split(lowerCAmelCase__    )[0].split("."    )[-2]
            lowerCAmelCase					:					List[Any]								=							mapped_key.replace("*"    ,				lowerCAmelCase__    )
          if "weight_g" in name:
            lowerCAmelCase					:					int								=							"""weight_g"""
          elif "weight_v" in name:
            lowerCAmelCase					:					Union[str, Any]								=							"""weight_v"""
          elif "bias" in name:
            lowerCAmelCase					:					Optional[int]								=							"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCAmelCase					:					List[str]								=							"""weight"""
          else:
            lowerCAmelCase					:					int								=							None
          set_recursively(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    )
        continue
    if not is_used:
      unused_weights.append(lowerCAmelCase__    )

  logger.warning(f"""Unused weights: {unused_weights}"""    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							full_name.split("conv_layers."    )[-1]
  lowerCAmelCase					:					str								=							name.split("."    )
  lowerCAmelCase					:					Dict								=							int(items[0]    )
  lowerCAmelCase					:					Tuple								=							int(items[1]    )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
      )
      lowerCAmelCase					:					Optional[int]								=							value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
      )
      lowerCAmelCase					:					int								=							value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
          " found."
      )
      lowerCAmelCase					:					Union[str, Any]								=							value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
      )
      lowerCAmelCase					:					Dict								=							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__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Any=None    ,				SCREAMING_SNAKE_CASE   :   Any=None    ,				SCREAMING_SNAKE_CASE   :   Any=True    ):


  '''simple docstring'''







  if config_path is not None:
    lowerCAmelCase					:					str								=							UniSpeechConfig.from_pretrained(lowerCAmelCase__    )
  else:
    lowerCAmelCase					:					Any								=							UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowerCAmelCase					:					str								=							Dictionary.load_from_json(lowerCAmelCase__    )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase					:					Union[str, Any]								=							target_dict.pad_index
      lowerCAmelCase					:					Tuple								=							target_dict.bos_index
      lowerCAmelCase					:					Tuple								=							target_dict.eos_index
      lowerCAmelCase					:					Union[str, Any]								=							len(target_dict.symbols    )
      lowerCAmelCase					:					Any								=							os.path.join(lowerCAmelCase__    ,				"vocab.json"    )
      if not os.path.isdir(lowerCAmelCase__    ):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase__    )    )
        return
      os.makedirs(lowerCAmelCase__    ,				exist_ok=lowerCAmelCase__    )
      lowerCAmelCase					:					Any								=							target_dict.indices

      # fairseq has the <pad> and <s> switched
      lowerCAmelCase					:					Tuple								=							4_2
      lowerCAmelCase					:					Optional[Any]								=							4_3
      with open(lowerCAmelCase__    ,				"w"    ,				encoding="utf-8"    ) as vocab_handle:
        json.dump(lowerCAmelCase__    ,				lowerCAmelCase__    )
      lowerCAmelCase					:					List[Any]								=							WavaVecaPhonemeCTCTokenizer(
          lowerCAmelCase__    ,				unk_token=target_dict.unk_word    ,				pad_token=target_dict.pad_word    ,				bos_token=target_dict.bos_word    ,				eos_token=target_dict.eos_word    ,				word_delimiter_token="|"    ,				do_lower_case=lowerCAmelCase__    ,				)
      lowerCAmelCase					:					str								=							True if config.feat_extract_norm == """layer""" else False
      lowerCAmelCase					:					Optional[Any]								=							WavaVecaFeatureExtractor(
          feature_size=1    ,				sampling_rate=1_6_0_0_0    ,				padding_value=0    ,				do_normalize=lowerCAmelCase__    ,				return_attention_mask=lowerCAmelCase__    ,				)
      lowerCAmelCase					:					List[Any]								=							WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,				tokenizer=lowerCAmelCase__    )
      processor.save_pretrained(lowerCAmelCase__    )

    lowerCAmelCase					:					Optional[Any]								=							UniSpeechForCTC(lowerCAmelCase__    )
  else:
    lowerCAmelCase					:					Dict								=							UniSpeechForPreTraining(lowerCAmelCase__    )

  if is_finetuned:
    lowerCAmelCase					:					List[Any]								=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,				arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    ), "w2v_path": checkpoint_path}    )
  else:
    lowerCAmelCase					:					List[str]								=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

  lowerCAmelCase					:					int								=							model[0].eval()

  recursively_load_weights(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    )

  hf_unispeech.save_pretrained(lowerCAmelCase__    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       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'''
     )
     lowerCAmelCase__				    =       parser.parse_args()
     convert_unispeech_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 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							[
             "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(snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							[
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         self.assertTrue(is_safetensors_compatible(snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							[
             "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(snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							[
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
         ]
         self.assertTrue(is_safetensors_compatible(snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							[
             "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(snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							[
             "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",
         ]
         lowerCAmelCase					:					Union[str, Any]								=							"fp16"
         self.assertTrue(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							[
             "unet/diffusion_pytorch_model.fp16.bin",
             "unet/diffusion_pytorch_model.fp16.safetensors",
         ]
         lowerCAmelCase					:					Tuple								=							"fp16"
         self.assertTrue(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							[
             "unet/diffusion_pytorch_model.bin",
             "unet/diffusion_pytorch_model.safetensors",
         ]
         lowerCAmelCase					:					Union[str, Any]								=							"fp16"
         self.assertTrue(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							[
             "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',
         ]
         lowerCAmelCase					:					Dict								=							"fp16"
         self.assertFalse(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							[
             "text_encoder/pytorch_model.fp16.bin",
             "text_encoder/model.fp16.safetensors",
         ]
         lowerCAmelCase					:					int								=							"fp16"
         self.assertTrue(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							[
             "text_encoder/pytorch_model.bin",
             "text_encoder/model.safetensors",
         ]
         lowerCAmelCase					:					Tuple								=							"fp16"
         self.assertTrue(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							[
             "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",
         ]
         lowerCAmelCase					:					List[Any]								=							"fp16"
         self.assertFalse(is_safetensors_compatible(snake_case__		,					variant=snake_case__						)						)





