
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase				 =					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''LlamaTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''LlamaTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''LlamaForCausalLM''',
		    '''LlamaModel''',
		    '''LlamaPreTrainedModel''',
		    '''LlamaForSequenceClassification''',
		]


if TYPE_CHECKING:
		from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_llama import LlamaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_llama_fast import LlamaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase					:			List[Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCAmelCase					:			Any       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     with open(a__ ,       """r"""		) as file:
          for line_number, line in enumerate(a__		):
               __SCREAMING_SNAKE_CASE      = line.strip()
               if line:
                    __SCREAMING_SNAKE_CASE      = line.split()
                    __SCREAMING_SNAKE_CASE      = line_number
                    __SCREAMING_SNAKE_CASE      = words[0]
                    __SCREAMING_SNAKE_CASE      = value
     return result







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     for attribute in key.split("""."""		):
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		).shape
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = hf_pointer
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = shape_pointer.shape

          # let's reduce dimension
          __SCREAMING_SNAKE_CASE      = value[0]
     else:
          __SCREAMING_SNAKE_CASE      = 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":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_g":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_v":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "bias":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "param":
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = value
     else:
          __SCREAMING_SNAKE_CASE      = value

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, weight_type]		)
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, hf_param_name]		)
     else:
          __SCREAMING_SNAKE_CASE      = key

     __SCREAMING_SNAKE_CASE      = value if """lm_head""" in full_key else value[0]


UpperCAmelCase					:			Any       =	{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}







def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = False
     for key, mapped_key in MAPPING.items():
          __SCREAMING_SNAKE_CASE      = """wav2vec2.""" + 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]:
               __SCREAMING_SNAKE_CASE      = True
               if "*" in mapped_key:
                    __SCREAMING_SNAKE_CASE      = name.split(a__		)[0].split("""."""		)[-2]
                    __SCREAMING_SNAKE_CASE      = mapped_key.replace("""*""" ,       a__		)
               if "weight_g" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_g"""
               elif "weight_v" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_v"""
               elif "bias" in name:
                    __SCREAMING_SNAKE_CASE      = """bias"""
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __SCREAMING_SNAKE_CASE      = """weight"""
               else:
                    __SCREAMING_SNAKE_CASE      = None
               if hf_dict is not None:
                    rename_dict(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               else:
                    set_recursively(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               return is_used
     return is_used







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = fairseq_model.state_dict()

     __SCREAMING_SNAKE_CASE      = hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          __SCREAMING_SNAKE_CASE      = False
          if "conv_layers" in name:
               load_conv_layer(
                   a__ ,       a__ ,       a__ ,       a__ ,       hf_model.config.feat_extract_norm == """group""" ,       )
               __SCREAMING_SNAKE_CASE      = True
          else:
               __SCREAMING_SNAKE_CASE      = load_wavaveca_layer(a__ ,       a__ ,       a__		)
          if not is_used:
               unused_weights.append(a__		)

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = full_name.split("""conv_layers."""		)[-1]
     __SCREAMING_SNAKE_CASE      = name.split("""."""		)
     __SCREAMING_SNAKE_CASE      = int(items[0]		)
     __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = 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.'		)
               __SCREAMING_SNAKE_CASE      = value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
     else:
          unused_weights.append(a__		)







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None ,       a__=True ,       a__=False		):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig.from_pretrained(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig()

     if is_seq_class:
          __SCREAMING_SNAKE_CASE      = read_txt_into_dict(a__		)
          __SCREAMING_SNAKE_CASE      = idalabel
          __SCREAMING_SNAKE_CASE      = WavaVecaForSequenceClassification(a__		)
          __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
              feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
          feature_extractor.save_pretrained(a__		)

     elif is_finetuned:
          if dict_path:
               __SCREAMING_SNAKE_CASE      = Dictionary.load(a__		)

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

               # fairseq has the <pad> and <s> switched
               __SCREAMING_SNAKE_CASE      = 0
               __SCREAMING_SNAKE_CASE      = 1
               with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as vocab_handle:
                    json.dump(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = WavaVecaCTCTokenizer(
                   a__ ,       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=a__ ,       )
               __SCREAMING_SNAKE_CASE      = True if config.feat_extract_norm == """layer""" else False
               __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
                   feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
               __SCREAMING_SNAKE_CASE      = WavaVecaProcessor(feature_extractor=a__ ,       tokenizer=a__		)
               processor.save_pretrained(a__		)

          __SCREAMING_SNAKE_CASE      = WavaVecaForCTC(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaForPreTraining(a__		)

     if is_finetuned or is_seq_class:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
     else:
          __SCREAMING_SNAKE_CASE      = argparse.Namespace(task="""audio_pretraining"""		)
          __SCREAMING_SNAKE_CASE      = fairseq.tasks.setup_task(a__		)

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,       task=a__		)

     __SCREAMING_SNAKE_CASE      = model[0].eval()

     recursively_load_weights(a__ ,       a__ ,       not is_finetuned		)

     hf_wavavec.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			int       =	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'
  )
  parser.add_argument(
      '--is_seq_class',
      action='store_true',
      help='Whether the model to convert is a fine-tuned sequence classification model or not',
  )
  UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()

  UpperCAmelCase					:			int       =	not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )






