
"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCamelCase			:       Union[str, 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",
}
UpperCamelCase			:       List[str]									=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def 							A		(       snake_case   :Dict       ,      snake_case   :Dict       ,      snake_case   :List[Any]       ,      snake_case   :Any       ,      snake_case   :Union[str, Any]	)   ->			str:
 for attribute in key.split('.'	):
  __UpperCamelCase       						= getattr(snake_case       ,      snake_case	)

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

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






def 							A		(       snake_case   :Tuple       ,      snake_case   :int	)   ->			List[Any]:
 __UpperCamelCase       						= []
 __UpperCamelCase       						= fairseq_model.state_dict()

 __UpperCamelCase       						= hf_model.unispeech_sat.feature_extractor

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

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






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






@torch.no_grad()
def 							A		(       snake_case   :Union[str, Any]       ,      snake_case   :Optional[int]       ,      snake_case   :Tuple=None       ,      snake_case   :Optional[Any]=None       ,      snake_case   :Optional[Any]=True	)   ->			Optional[int]:
 if config_path is not None:
  __UpperCamelCase       						= UniSpeechSatConfig.from_pretrained(snake_case	)
 else:
  __UpperCamelCase       						= UniSpeechSatConfig()

 __UpperCamelCase       						= ''

 if is_finetuned:
  __UpperCamelCase       						= UniSpeechSatForCTC(snake_case	)
 else:
  __UpperCamelCase       						= UniSpeechSatForPreTraining(snake_case	)

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

 recursively_load_weights(snake_case       ,      snake_case	)

 hf_wavavec.save_pretrained(snake_case	)


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"
      )
      UpperCamelCase			:       str									=				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"""

from typing import TYPE_CHECKING

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


UpperCamelCase			:       List[Any]									=				{
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				["XLMRobertaTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       int									=				["XLMRobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[int]									=				[
          "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMRobertaForCausalLM",
          "XLMRobertaForMaskedLM",
          "XLMRobertaForMultipleChoice",
          "XLMRobertaForQuestionAnswering",
          "XLMRobertaForSequenceClassification",
          "XLMRobertaForTokenClassification",
          "XLMRobertaModel",
          "XLMRobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFXLMRobertaForCausalLM",
          "TFXLMRobertaForMaskedLM",
          "TFXLMRobertaForMultipleChoice",
          "TFXLMRobertaForQuestionAnswering",
          "TFXLMRobertaForSequenceClassification",
          "TFXLMRobertaForTokenClassification",
          "TFXLMRobertaModel",
          "TFXLMRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxXLMRobertaForMaskedLM",
          "FlaxXLMRobertaForCausalLM",
          "FlaxXLMRobertaForMultipleChoice",
          "FlaxXLMRobertaForQuestionAnswering",
          "FlaxXLMRobertaForSequenceClassification",
          "FlaxXLMRobertaForTokenClassification",
          "FlaxXLMRobertaModel",
          "FlaxXLMRobertaPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaConfig,
          XLMRobertaOnnxConfig,
      )

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta import XLMRobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta import (
                XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaForCausalLM,
                XLMRobertaForMaskedLM,
                XLMRobertaForMultipleChoice,
                XLMRobertaForQuestionAnswering,
                XLMRobertaForSequenceClassification,
                XLMRobertaForTokenClassification,
                XLMRobertaModel,
                XLMRobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlm_roberta import (
                TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLMRobertaForCausalLM,
                TFXLMRobertaForMaskedLM,
                TFXLMRobertaForMultipleChoice,
                TFXLMRobertaForQuestionAnswering,
                TFXLMRobertaForSequenceClassification,
                TFXLMRobertaForTokenClassification,
                TFXLMRobertaModel,
                TFXLMRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xlm_roberta import (
                FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxXLMRobertaForCausalLM,
                FlaxXLMRobertaForMaskedLM,
                FlaxXLMRobertaForMultipleChoice,
                FlaxXLMRobertaForQuestionAnswering,
                FlaxXLMRobertaForSequenceClassification,
                FlaxXLMRobertaForTokenClassification,
                FlaxXLMRobertaModel,
                FlaxXLMRobertaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       Dict									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



