
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()
_A						      =				logging.get_logger(__name__)

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



def 		lowercase_    (     A__	)							->							Union[str, Any]:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							List[str]:
    """simple docstring"""

    for attribute in key.split("."	):
        snake_case				  =      getattr(A__				,	A__	)

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

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

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

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							str:
    """simple docstring"""

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

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

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


_A						      =				{
    "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 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None	)							->							str:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      []
    snake_case				  =      fairseq_model.state_dict()

    snake_case				  =      hf_model.wavaveca.feature_extractor

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

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      full_name.split("conv_layers."	)[-1]
    snake_case				  =      name.split("."	)
    snake_case				  =      int(items[0]	)
    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.'	)
            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.'	)
            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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'	)
            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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'	)
            snake_case				  =      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 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None				,	A__=True				,	A__=False	)							->							List[Any]:
    """simple docstring"""

    if config_path is not None:
        snake_case				  =      WavaVecaConfig.from_pretrained(A__	)
    else:
        snake_case				  =      WavaVecaConfig()

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

    elif is_finetuned:
        if dict_path:
            snake_case				  =      Dictionary.load(A__	)

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

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

        snake_case				  =      WavaVecaForCTC(A__	)
    else:
        snake_case				  =      WavaVecaForPreTraining(A__	)

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

        snake_case  ,							snake_case  ,							snake_case				  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,	task=A__	)

    snake_case				  =      model[0].eval()

    recursively_load_weights(A__				,	A__				,	not is_finetuned	)

    hf_wavavec.save_pretrained(A__	)


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

   _A						      =				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,
   )



from collections import OrderedDict
from typing import Mapping

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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "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   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	"roberta"





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=5_0_2_6_5       ,					_A   :					Dict=7_6_8       ,					_A   :					Tuple=1_2       ,					_A   :					Optional[Any]=1_2       ,					_A   :					int=3_0_7_2       ,					_A   :					List[str]="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[int]=5_1_2       ,					_A   :					Dict=2       ,					_A   :					Optional[Any]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					str=1       ,					_A   :					Dict=0       ,					_A   :					Optional[int]=2       ,					_A   :					int="absolute"       ,					_A   :					Any=True       ,					_A   :					Union[str, Any]=None       ,					**_A   :					Optional[int]       ,					)		->		Tuple:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

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







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]     )

