import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A     :  Optional[int]					     =       logging.get_logger(__name__)

A     :  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",
}
A     :  Dict					     =       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      for attribute in key.split("."				):
            SCREAMING_SNAKE_CASE_          =  getattr(__UpperCamelCase     ,      __UpperCamelCase				)

      if weight_type is not None:
            SCREAMING_SNAKE_CASE_          =  getattr(__UpperCamelCase     ,      __UpperCamelCase				).shape
      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
      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__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE_          =  hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE_          =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      hf_model.config.feat_extract_norm == "group"     ,      )
                  SCREAMING_SNAKE_CASE_          =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE_          =  "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
                              SCREAMING_SNAKE_CASE_          =  True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE_          =  name.split(__UpperCamelCase				)[0].split("."				)[-2]
                                    SCREAMING_SNAKE_CASE_          =  mapped_key.replace("*"     ,      __UpperCamelCase				)
                              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
                              set_recursively(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCamelCase				)

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


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      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[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[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(__UpperCamelCase				)


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=True				):
      if config_path is not None:
            SCREAMING_SNAKE_CASE_          =  UniSpeechSatConfig.from_pretrained(__UpperCamelCase				)
      else:
            SCREAMING_SNAKE_CASE_          =  UniSpeechSatConfig()

      SCREAMING_SNAKE_CASE_          =  ""

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

      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]				)}				)
      SCREAMING_SNAKE_CASE_          =  model[0].eval()

      recursively_load_weights(__UpperCamelCase     ,      __UpperCamelCase				)

      hf_wavavec.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
      A     :  Union[str, 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"
      )
      A     :  Optional[Any]					     =       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
      )



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


