



"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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""",
    """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""",
}
lowerCamelCase       	=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      for attribute in key.split("."				):
            UpperCAmelCase_      			=							getattr(__a     ,      __a				)

      if weight_type is not None:
            UpperCAmelCase_      			=							getattr(__a     ,      __a				).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__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      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(
                      __a     ,      __a     ,      __a     ,      __a     ,      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(__a				)[0].split("."				)[-2]
                                    UpperCAmelCase_      			=							mapped_key.replace("*"     ,      __a				)
                              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(__a     ,      __a     ,      __a     ,      __a     ,      __a				)
                        continue
            if not is_used:
                  unused_weights.append(__a				)

      logger.warning(f"""Unused weights: {unused_weights}"""				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      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(__a				)
@torch.no_grad()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=True				):
      if config_path is not None:
            UpperCAmelCase_      			=							UniSpeechSatConfig.from_pretrained(__a				)
      else:
            UpperCAmelCase_      			=							UniSpeechSatConfig()

      UpperCAmelCase_      			=							""

      if is_finetuned:
            UpperCAmelCase_      			=							UniSpeechSatForCTC(__a				)
      else:
            UpperCAmelCase_      			=							UniSpeechSatForPreTraining(__a				)

      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(__a     ,      __a				)

      hf_wavavec.save_pretrained(__a				)


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_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''roberta'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]=50265		,    _UpperCAmelCase      :  str=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  int=12		,    _UpperCAmelCase      :  Tuple=3072		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  Optional[int]=512		,    _UpperCAmelCase      :  Optional[Any]=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Optional[Any]=1e-12		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  int=2		,    _UpperCAmelCase      :  Tuple="absolute"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  int=None		,    **_UpperCAmelCase      :  List[str]		,    )			->						Any:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )

             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							position_embedding_type
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  List[Any]   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_      			=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_      			=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]   )




