
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_A						      =				logging.get_logger(__name__)

_A						      =				{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

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

    if weight_type is not None:
        snake_case				  =      getattr(A__				,	A__	).shape
    else:
        snake_case				  =      hf_pointer.shape

    assert hf_shape == value.shape, (
        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
    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__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      []
    snake_case				  =      fairseq_model.state_dict()

    snake_case				  =      hf_model.sew.feature_extractor if is_finetuned else hf_model.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:
            for key, mapped_key in MAPPING.items():
                snake_case				  =      "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") 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 "weight" in name:
                        snake_case				  =      "weight"
                    elif "bias" in name:
                        snake_case				  =      "bias"
                    else:
                        snake_case				  =      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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							int:
    """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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'{full_name} has size {value.shape}, but {feature_extractor[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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor[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__	)



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

    snake_case				  =      SEWConfig()
    if is_finetuned:
        snake_case				  =      model.wav_encoder.wav_model.cfg
    else:
        snake_case				  =      model.cfg

    snake_case				  =      fs_config.conv_bias
    snake_case				  =      eval(fs_config.conv_feature_layers	)
    snake_case				  =      [x[0] for x in conv_layers]
    snake_case				  =      [x[1] for x in conv_layers]
    snake_case				  =      [x[2] for x in conv_layers]
    snake_case				  =      "gelu"
    snake_case				  =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
    snake_case				  =      0.0
    snake_case				  =      fs_config.activation_fn.name
    snake_case				  =      fs_config.encoder_embed_dim
    snake_case				  =      0.02
    snake_case				  =      fs_config.encoder_ffn_embed_dim
    snake_case				  =      1e-5
    snake_case				  =      fs_config.encoder_layerdrop
    snake_case				  =      fs_config.encoder_attention_heads
    snake_case				  =      fs_config.conv_pos_groups
    snake_case				  =      fs_config.conv_pos
    snake_case				  =      len(A__	)
    snake_case				  =      fs_config.encoder_layers
    snake_case				  =      fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        snake_case				  =      model.cfg
        snake_case				  =      fs_config.final_dropout
        snake_case				  =      fs_config.layerdrop
    snake_case				  =      fs_config.activation_dropout
    snake_case				  =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    snake_case				  =      fs_config.attention_dropout
    snake_case				  =      fs_config.dropout_input
    snake_case				  =      fs_config.dropout
    snake_case				  =      fs_config.mask_channel_length
    snake_case				  =      fs_config.mask_channel_prob
    snake_case				  =      fs_config.mask_length
    snake_case				  =      fs_config.mask_prob

    snake_case				  =      "Wav2Vec2FeatureExtractor"
    snake_case				  =      "Wav2Vec2CTCTokenizer"

    return config



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None				,	A__=True	)							->							List[Any]:
    """simple docstring"""

    if is_finetuned:
        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  ,							snake_case  ,							snake_case				  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

    if config_path is not None:
        snake_case				  =      SEWConfig.from_pretrained(A__	)
    else:
        snake_case				  =      convert_config(model[0]				,	A__	)
    snake_case				  =      model[0].eval()

    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__				,	)

    if 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.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__	)
            with open(A__				,	"w"				,	encoding="utf-8"	) as vocab_handle:
                json.dump(target_dict.indices				,	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				  =      WavaVecaProcessor(feature_extractor=A__				,	tokenizer=A__	)
            processor.save_pretrained(A__	)

        snake_case				  =      SEWForCTC(A__	)
    else:
        snake_case				  =      SEWModel(A__	)
        feature_extractor.save_pretrained(A__	)

    recursively_load_weights(A__				,	A__				,	A__	)

    hf_model.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(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _A						      =				parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )



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),
             ]     )

