
"""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      :List[str]	    =		logging.get_logger(__name__)

__lowerCamelCase      :str	    =		{
    "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      :List[str]	    =		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Dict    )   ->      Union[str, Any]:



      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					:					int        =	'''lm_head'''

            lowerCamelCase					:					List[str]        =	getattr(UpperCamelCase__			,				UpperCamelCase__    )

      if weight_type is not None:
            lowerCamelCase					:					Tuple        =	getattr(UpperCamelCase__			,				UpperCamelCase__    ).shape
      else:
            lowerCamelCase					:					Any        =	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					:					Optional[Any]        =	value
      elif weight_type == "weight_g":
            lowerCamelCase					:					Tuple        =	value
      elif weight_type == "weight_v":
            lowerCamelCase					:					Optional[Any]        =	value
      elif weight_type == "bias":
            lowerCamelCase					:					str        =	value
      else:
            lowerCamelCase					:					Any        =	value

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




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						int    )   ->      Any:
      lowerCamelCase					:					Dict        =	[]
      lowerCamelCase					:					Optional[int]        =	fairseq_model.state_dict()

      lowerCamelCase					:					int        =	hf_model.unispeech.feature_extractor

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

      logger.warning(F'Unused weights: {unused_weights}'    )




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						List[str]    )   ->      Dict:
      lowerCamelCase					:					Optional[int]        =	full_name.split("""conv_layers."""    )[-1]
      lowerCamelCase					:					Optional[int]        =	name.split("""."""    )
      lowerCamelCase					:					List[Any]        =	int(items[0]    )
      lowerCamelCase					:					Optional[Any]        =	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					:					List[str]        =	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					:					Tuple        =	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					:					Optional[int]        =	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					:					Optional[int]        =	value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
      else:
            unused_weights.append(UpperCamelCase__    )




@torch.no_grad()
def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						str=None			,				UpperCamelCase__       :						Optional[Any]=None			,				UpperCamelCase__       :						List[Any]=True    )   ->      List[Any]:



      if config_path is not None:
            lowerCamelCase					:					List[str]        =	UniSpeechConfig.from_pretrained(UpperCamelCase__    )
      else:
            lowerCamelCase					:					Optional[int]        =	UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  lowerCamelCase					:					str        =	Dictionary.load_from_json(UpperCamelCase__    )

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

                  # fairseq has the <pad> and <s> switched
                  lowerCamelCase					:					str        =	42
                  lowerCamelCase					:					List[str]        =	43
                  with open(UpperCamelCase__			,				"""w"""			,				encoding="""utf-8"""    ) as vocab_handle:
                        json.dump(UpperCamelCase__			,				UpperCamelCase__    )
                  lowerCamelCase					:					Dict        =	WavaVecaPhonemeCTCTokenizer(
                      UpperCamelCase__			,				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=UpperCamelCase__			,				)
                  lowerCamelCase					:					int        =	True if config.feat_extract_norm == '''layer''' else False
                  lowerCamelCase					:					Union[str, Any]        =	WavaVecaFeatureExtractor(
                      feature_size=1			,				sampling_rate=16000			,				padding_value=0			,				do_normalize=UpperCamelCase__			,				return_attention_mask=UpperCamelCase__			,				)
                  lowerCamelCase					:					int        =	WavaVecaProcessor(feature_extractor=UpperCamelCase__			,				tokenizer=UpperCamelCase__    )
                  processor.save_pretrained(UpperCamelCase__    )

            lowerCamelCase					:					Dict        =	UniSpeechForCTC(UpperCamelCase__    )
      else:
            lowerCamelCase					:					Dict        =	UniSpeechForPreTraining(UpperCamelCase__    )

      if is_finetuned:
            lowerCamelCase					:					Dict        =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    ), """w2v_path""": checkpoint_path}    )
      else:
            lowerCamelCase					:					Tuple        =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

      lowerCamelCase					:					Optional[int]        =	model[0].eval()

      recursively_load_weights(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

      hf_unispeech.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    __lowerCamelCase      :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'
    )
    __lowerCamelCase      :int	    =		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"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




