


"""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()
__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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""",
}
__snake_case         =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					{}
    with open(lowercase		,							"r"				) as file:
        for line_number, line in enumerate(lowercase				):
            snake_case					:   str         =					line.strip()
            if line:
                snake_case					:   Optional[int]         =					line.split()
                snake_case					:   Dict         =					line_number
                snake_case					:   Any         =					words[0]
                snake_case					:   str         =					value
    return result







def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						int		,							lowercase						:						Union[str, Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						str				)			->		Union[str, Any]:


    """simple docstring"""





    for attribute in key.split("."				):
        snake_case					:   Union[str, Any]         =					getattr(lowercase		,							lowercase				)

    snake_case					:   Dict         =					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowercase				):
            snake_case					:   int         =					PARAM_MAPPING[full_name.split("."				)[-1]]
            snake_case					:   Optional[int]         =					"param"

    if weight_type is not None and weight_type != "param":
        snake_case					:   List[str]         =					getattr(lowercase		,							lowercase				).shape
    elif weight_type is not None and weight_type == "param":
        snake_case					:   Any         =					hf_pointer
        for attribute in hf_param_name.split("."				):
            snake_case					:   int         =					getattr(lowercase		,							lowercase				)
        snake_case					:   Tuple         =					shape_pointer.shape

        # let's reduce dimension
        snake_case					:   Dict         =					value[0]
    else:
        snake_case					:   int         =					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":
        snake_case					:   Tuple         =					value
    elif weight_type == "weight_g":
        snake_case					:   List[Any]         =					value
    elif weight_type == "weight_v":
        snake_case					:   List[str]         =					value
    elif weight_type == "bias":
        snake_case					:   Any         =					value
    elif weight_type == "param":
        for attribute in hf_param_name.split("."				):
            snake_case					:   Dict         =					getattr(lowercase		,							lowercase				)
        snake_case					:   Optional[Any]         =					value
    else:
        snake_case					:   Any         =					value

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







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						int		,							lowercase						:						Union[str, Any]		,							lowercase						:						Optional[Any]				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowercase				):
            snake_case					:   Dict         =					PARAM_MAPPING[full_name.split("."				)[-1]]
            snake_case					:   Optional[int]         =					"param"

    if weight_type is not None and weight_type != "param":
        snake_case					:   List[str]         =					".".join([key, weight_type]				)
    elif weight_type is not None and weight_type == "param":
        snake_case					:   List[Any]         =					".".join([key, hf_param_name]				)
    else:
        snake_case					:   Union[str, Any]         =					key

    snake_case					:   str         =					value if "lm_head" in full_key else value[0]


__snake_case         =					{
    """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 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						str		,							lowercase						:						Optional[int]=None		,							lowercase						:						List[str]=None				)			->		str:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Optional[int]				)			->		Dict:


    """simple docstring"""





    snake_case					:   Optional[int]         =					[]
    snake_case					:   Union[str, Any]         =					fairseq_model.state_dict()

    snake_case					:   int         =					hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case					:   int         =					False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase		,							lowercase		,							lowercase		,							lowercase		,							hf_model.config.feat_extract_norm == "group"		,							)
            snake_case					:   Union[str, Any]         =					True
        else:
            snake_case					:   List[str]         =					load_wavaveca_layer(lowercase		,							lowercase		,							lowercase				)
        if not is_used:
            unused_weights.append(lowercase				)

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







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						Tuple		,							lowercase						:						int		,							lowercase						:						Tuple		,							lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Any         =					full_name.split("conv_layers."				)[-1]
    snake_case					:   Dict         =					name.split("."				)
    snake_case					:   Union[str, Any]         =					int(items[0]				)
    snake_case					:   Optional[int]         =					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.'				)
            snake_case					:   str         =					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.'				)
            snake_case					:   str         =					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.'				)
            snake_case					:   str         =					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.'				)
            snake_case					:   Tuple         =					value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
    else:
        unused_weights.append(lowercase				)







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						List[Any]		,							lowercase						:						Union[str, Any]=None		,							lowercase						:						Any=None		,							lowercase						:						Dict=True		,							lowercase						:						str=False				)			->		Union[str, Any]:


    """simple docstring"""





    if config_path is not None:
        snake_case					:   List[Any]         =					WavaVecaConfig.from_pretrained(lowercase				)
    else:
        snake_case					:   str         =					WavaVecaConfig()

    if is_seq_class:
        snake_case					:   Union[str, Any]         =					read_txt_into_dict(lowercase				)
        snake_case					:   Dict         =					idalabel
        snake_case					:   int         =					WavaVecaForSequenceClassification(lowercase				)
        snake_case					:   Optional[Any]         =					WavaVecaFeatureExtractor(
            feature_size=1		,							sampling_rate=1_6000		,							padding_value=0		,							do_normalize=lowercase		,							return_attention_mask=lowercase		,							)
        feature_extractor.save_pretrained(lowercase				)

    elif is_finetuned:
        if dict_path:
            snake_case					:   List[str]         =					Dictionary.load(lowercase				)

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

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

        snake_case					:   Optional[int]         =					WavaVecaForCTC(lowercase				)
    else:
        snake_case					:   Optional[int]         =					WavaVecaForPreTraining(lowercase				)

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

        snake_case					:   int         =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,							task=lowercase				)

    snake_case					:   Optional[Any]         =					model[0].eval()

    recursively_load_weights(lowercase		,							lowercase		,							not is_finetuned				)

    hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
  __snake_case         =					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""",
  )
  __snake_case         =					parser.parse_args()

  __snake_case         =					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,
  )









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   Union[str, Any]         =					None

    # Fills the data structure with the probabilities of
    # different transitions and pointers to previous states
    for o in range(1		,							len(lowercase				)				):
        snake_case					:   Optional[Any]         =					observations_space[o]
        snake_case					:   str         =					observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            snake_case					:   str         =					""
            snake_case					:   List[Any]         =					-1
            for k_state in states_space:
                snake_case					:   Tuple         =					(
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    snake_case					:   Optional[Any]         =					probability
                    snake_case					:   int         =					k_state

            # Update probabilities and pointers dicts
            snake_case					:   List[str]         =					(
                probabilities[(arg_max, prior_observation)]
                * transition_probabilities[arg_max][state]
                * emission_probabilities[state][observation]
            )

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

    # argmax for given final observation
    snake_case					:   str         =					""
    snake_case					:   Optional[int]         =					-1
    for k_state in states_space:
        snake_case					:   int         =					probabilities[(k_state, final_observation)]
        if probability > max_probability:
            snake_case					:   Optional[int]         =					probability
            snake_case					:   List[Any]         =					k_state
    snake_case					:   str         =					arg_max

    # Process pointers backwards
    snake_case					:   List[str]         =					last_state
    snake_case					:   Optional[int]         =					[]
    for o in range(len(lowercase				) - 1		,							-1		,							-1				):
        result.append(lowercase				)
        snake_case					:   List[str]         =					pointers[previous, observations_space[o]]
    result.reverse()

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]				):
        raise ValueError("There's an empty parameter"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





