
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase								=					{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TrajectoryTransformerModel""",
         """TrajectoryTransformerPreTrainedModel""",
         """load_tf_weights_in_trajectory_transformer""",
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

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






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								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     for attribute in key.split("."      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )

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

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

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

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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

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

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

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


UpperCamelCase								=					{
    '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       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca.feature_extractor

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

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



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

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



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Optional[int]=True       ,    lowerCAmelCase_					: List[str]=False      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaConfig()

     if is_seq_class:
          lowerCAmelCase__          =			read_txt_into_dict(lowerCAmelCase_      )
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			WavaVecaForSequenceClassification(lowerCAmelCase_      )
          lowerCAmelCase__          =			WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase_      )

     elif is_finetuned:
          if dict_path:
               lowerCAmelCase__          =			Dictionary.load(lowerCAmelCase_      )

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

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			WavaVecaCTCTokenizer(
                   lowerCAmelCase_       ,    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=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			True if config.feat_extract_norm == "layer" else False
               lowerCAmelCase__          =			WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,    tokenizer=lowerCAmelCase_      )
               processor.save_pretrained(lowerCAmelCase_      )

          lowerCAmelCase__          =			WavaVecaForCTC(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaForPreTraining(lowerCAmelCase_      )

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

          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=lowerCAmelCase_      )

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(lowerCAmelCase_       ,    lowerCAmelCase_       ,    not is_finetuned      )

     hf_wavavec.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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								=					parser.parse_args()

     UpperCamelCase								=					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,
     )
