
'''simple docstring'''




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()
lowercase			:				int    		=			logging.get_logger(__name__)

lowercase			:				Tuple    		=			{
    '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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    for attribute in key.split('''.'''						):
        A       :   int					=						getattr(snake_case__		,				snake_case__						)

    if weight_type is not None:
        A       :   Optional[int]					=						getattr(snake_case__		,				snake_case__						).shape
    else:
        A       :   Union[str, Any]					=						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":
        A       :   Optional[int]					=						value
    elif weight_type == "weight_g":
        A       :   str					=						value
    elif weight_type == "weight_v":
        A       :   str					=						value
    elif weight_type == "bias":
        A       :   List[str]					=						value
    else:
        A       :   Any					=						value

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						[]
    A       :   Optional[int]					=						fairseq_model.state_dict()

    A       :   Optional[Any]					=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        A       :   Tuple					=						False
        if "conv_layers" in name:
            load_conv_layer(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				hf_model.config.feat_extract_norm == '''group'''		,				)
            A       :   str					=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :   str					=						'''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]:
                    A       :   Union[str, Any]					=						True
                    if "*" in mapped_key:
                        A       :   List[Any]					=						name.split(snake_case__						)[0].split('''.'''						)[-2]
                        A       :   Optional[int]					=						mapped_key.replace('''*'''		,				snake_case__						)
                    if "weight_g" in name:
                        A       :   List[str]					=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :   Union[str, Any]					=						'''weight_v'''
                    elif "weight" in name:
                        A       :   Dict					=						'''weight'''
                    elif "bias" in name:
                        A       :   Dict					=						'''bias'''
                    else:
                        A       :   Optional[Any]					=						None
                    set_recursively(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)
                continue
        if not is_used:
            unused_weights.append(snake_case__						)

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						SEWConfig()
    if is_finetuned:
        A       :   List[Any]					=						model.wav_encoder.wav_model.cfg
    else:
        A       :   str					=						model.cfg

    A       :   int					=						fs_config.conv_bias
    A       :   Optional[int]					=						eval(fs_config.conv_feature_layers						)
    A       :   Union[str, Any]					=						[x[0] for x in conv_layers]
    A       :   Dict					=						[x[1] for x in conv_layers]
    A       :   Dict					=						[x[2] for x in conv_layers]
    A       :   str					=						'''gelu'''
    A       :   Tuple					=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    A       :   Optional[Any]					=						0.0
    A       :   List[str]					=						fs_config.activation_fn.name
    A       :   List[str]					=						fs_config.encoder_embed_dim
    A       :   Optional[Any]					=						0.02
    A       :   Any					=						fs_config.encoder_ffn_embed_dim
    A       :   str					=						1E-5
    A       :   str					=						fs_config.encoder_layerdrop
    A       :   List[str]					=						fs_config.encoder_attention_heads
    A       :   List[str]					=						fs_config.conv_pos_groups
    A       :   str					=						fs_config.conv_pos
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   str					=						fs_config.encoder_layers
    A       :   List[Any]					=						fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        A       :   Optional[int]					=						model.cfg
        A       :   Union[str, Any]					=						fs_config.final_dropout
        A       :   Optional[int]					=						fs_config.layerdrop
    A       :   str					=						fs_config.activation_dropout
    A       :   int					=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    A       :   Dict					=						fs_config.attention_dropout
    A       :   List[str]					=						fs_config.dropout_input
    A       :   Union[str, Any]					=						fs_config.dropout
    A       :   int					=						fs_config.mask_channel_length
    A       :   str					=						fs_config.mask_channel_prob
    A       :   List[str]					=						fs_config.mask_length
    A       :   Tuple					=						fs_config.mask_prob

    A       :   Optional[int]					=						'''Wav2Vec2FeatureExtractor'''
    A       :   Union[str, Any]					=						'''Wav2Vec2CTCTokenizer'''

    return config







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=True						):




    '''simple docstring'''



    if is_finetuned:
        A,  A,  A       :   Any					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
    else:
        A,  A,  A       :   int					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

    if config_path is not None:
        A       :   List[str]					=						SEWConfig.from_pretrained(snake_case__						)
    else:
        A       :   Dict					=						convert_config(model[0]		,				snake_case__						)
    A       :   str					=						model[0].eval()

    A       :   Union[str, Any]					=						True if config.feat_extract_norm == '''layer''' else False
    A       :   Optional[int]					=						WavaVecaFeatureExtractor(
        feature_size=1		,				sampling_rate=1_6000		,				padding_value=0		,				do_normalize=snake_case__		,				return_attention_mask=snake_case__		,				)

    if is_finetuned:
        if dict_path:
            A       :   Optional[Any]					=						Dictionary.load(snake_case__						)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A       :   Optional[Any]					=						target_dict.pad_index
            A       :   Optional[Any]					=						target_dict.bos_index
            A       :   Union[str, Any]					=						target_dict.pad_index
            A       :   Union[str, Any]					=						target_dict.bos_index
            A       :   int					=						target_dict.eos_index
            A       :   Tuple					=						len(target_dict.symbols						)
            A       :   List[str]					=						os.path.join(snake_case__		,				'''vocab.json'''						)
            if not os.path.isdir(snake_case__						):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__						)						)
                return
            os.makedirs(snake_case__		,				exist_ok=snake_case__						)
            with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as vocab_handle:
                json.dump(target_dict.indices		,				snake_case__						)
            A       :   str					=						WavaVecaCTCTokenizer(
                snake_case__		,				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=snake_case__		,				)
            A       :   List[str]					=						WavaVecaProcessor(feature_extractor=snake_case__		,				tokenizer=snake_case__						)
            processor.save_pretrained(snake_case__						)

        A       :   Tuple					=						SEWForCTC(snake_case__						)
    else:
        A       :   Dict					=						SEWModel(snake_case__						)
        feature_extractor.save_pretrained(snake_case__						)

    recursively_load_weights(snake_case__		,				snake_case__		,				snake_case__						)

    hf_model.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Dict    		=			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'
      )
      lowercase			:				Any    		=			parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )









'''simple docstring'''

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.array:
		_a     :      Optional[int]        =							f"""{sampling_rate}"""
		_a     :      Any        =							'1'
		_a     :      Optional[int]        =							'f32le'
		_a     :      Any        =							[
		    'ffmpeg',
		    '-i',
		    'pipe:0',
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]

		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdin=subprocess.PIPE						,			stdout=subprocess.PIPE  ) as ffmpeg_process:
						_a     :      int        =							ffmpeg_process.communicate(lowerCAmelCase_  )
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
		_a     :      int        =							output_stream[0]
		_a     :      List[str]        =							np.frombuffer(lowerCAmelCase_						,			np.floataa  )
		if audio.shape[0] == 0:
				raise ValueError('Malformed soundfile'  )
		return audio



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = "f32le"						,			)			->      Union[str, Any]:
		_a     :      List[str]        =							f"""{sampling_rate}"""
		_a     :      List[str]        =							'1'
		if format_for_conversion == "s16le":
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Dict        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		_a     :      Any        =							platform.system()
		if system == "Linux":
				_a     :      Union[str, Any]        =							'alsa'
				_a     :      Union[str, Any]        =							'default'
		elif system == "Darwin":
				_a     :      Any        =							'avfoundation'
				_a     :      Optional[int]        =							':0'
		elif system == "Windows":
				_a     :      str        =							'dshow'
				_a     :      Tuple        =							'default'

		_a     :      str        =							[
		    'ffmpeg',
		    '-f',
		    format_,
		    '-i',
		    input_,
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-fflags',
		    'nobuffer',
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		_a     :      Union[str, Any]        =							_ffmpeg_stream(lowerCAmelCase_						,			lowerCAmelCase_  )
		for item in iterator:
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = "f32le"						,			)			->      str:
		if stream_chunk_s is not None:
				_a     :      str        =							stream_chunk_s
		else:
				_a     :      List[str]        =							chunk_length_s

		_a     :      int        =							ffmpeg_microphone(lowerCAmelCase_						,			lowerCAmelCase_						,			format_for_conversion=lowerCAmelCase_  )
		if format_for_conversion == "s16le":
				_a     :      Optional[Any]        =							np.intaa
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Tuple        =							np.floataa
				_a     :      Any        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		if stride_length_s is None:
				_a     :      str        =							chunk_length_s / 6
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		if isinstance(lowerCAmelCase_						,			(int, float)  ):
				_a     :      List[str]        =							[stride_length_s, stride_length_s]

		_a     :      str        =							int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
		_a     :      List[str]        =							int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
		_a     :      Any        =							datetime.datetime.now()
		_a     :      Dict        =							datetime.timedelta(seconds=lowerCAmelCase_  )
		for item in chunk_bytes_iter(lowerCAmelCase_						,			lowerCAmelCase_						,			stride=(stride_left, stride_right)						,			stream=lowerCAmelCase_  ):
				# Put everything back in numpy scale
				_a     :      List[Any]        =							np.frombuffer(item['raw']						,			dtype=lowerCAmelCase_  )
				_a     :      List[str]        =							(
				    item['stride'][0] // size_of_sample,
				    item['stride'][1] // size_of_sample,
				)
				_a     :      Union[str, Any]        =							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      Tuple        =							B''
		_a       ,					_a     :      str        =							stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""  )
		_a     :      Optional[int]        =							0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase_  ) < chunk_len:
						_a     :      str        =							(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase_  ) >= chunk_len:
								# We are flushing the accumulator
								_a     :      Union[str, Any]        =							(_stride_left, stride_right)
								_a     :      Dict        =							{'raw': acc[:chunk_len], 'stride': stride}
								if stream:
										_a     :      List[str]        =							False
								yield item
								_a     :      int        =							stride_left
								_a     :      List[Any]        =							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase_  ) > stride_left:
				_a     :      str        =							{'raw': acc, 'stride': (_stride_left, 0)}
				if stream:
						_a     :      str        =							False
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      Optional[Any]        =							2**24  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdout=subprocess.PIPE						,			bufsize=lowerCAmelCase_  ) as ffmpeg_process:
						while True:
								_a     :      Any        =							ffmpeg_process.stdout.read(lowerCAmelCase_  )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error




