





"""simple docstring"""






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

import numpy as np

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       np.array:






       """simple docstring"""




       _UpperCAmelCase     =     F"""{sampling_rate}"""
       _UpperCAmelCase     =     '1'
       _UpperCAmelCase     =     'f32le'
       _UpperCAmelCase     =     [
           'ffmpeg',
           '-i',
           'pipe:0',
           '-ac',
           ac,
           '-ar',
           ar,
           '-f',
           format_for_conversion,
           '-hide_banner',
           '-loglevel',
           'quiet',
           'pipe:1',
       ]

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = "f32le",)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     F"""{sampling_rate}"""
       _UpperCAmelCase     =     '1'
       if format_for_conversion == "s16le":
              _UpperCAmelCase     =     2
       elif format_for_conversion == "f32le":
              _UpperCAmelCase     =     4
       else:
              raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""							)

       _UpperCAmelCase     =     platform.system()
       if system == "Linux":
              _UpperCAmelCase     =     'alsa'
              _UpperCAmelCase     =     'default'
       elif system == "Darwin":
              _UpperCAmelCase     =     'avfoundation'
              _UpperCAmelCase     =     ':0'
       elif system == "Windows":
              _UpperCAmelCase     =     'dshow'
              _UpperCAmelCase     =     'default'

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = "f32le",)		->       Any:






       """simple docstring"""




       if stream_chunk_s is not None:
              _UpperCAmelCase     =     stream_chunk_s
       else:
              _UpperCAmelCase     =     chunk_length_s

       _UpperCAmelCase     =     ffmpeg_microphone(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,format_for_conversion=SCREAMING_SNAKE_CASE							)
       if format_for_conversion == "s16le":
              _UpperCAmelCase     =     np.intaa
              _UpperCAmelCase     =     2
       elif format_for_conversion == "f32le":
              _UpperCAmelCase     =     np.floataa
              _UpperCAmelCase     =     4
       else:
              raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""							)

       if stride_length_s is None:
              _UpperCAmelCase     =     chunk_length_s / 6
       _UpperCAmelCase     =     int(round(sampling_rate * chunk_length_s							)							) * size_of_sample
       if isinstance(SCREAMING_SNAKE_CASE,(int, float)							):
              _UpperCAmelCase     =     [stride_length_s, stride_length_s]

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = False							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     b''
       _UpperCAmelCase    ,							_UpperCAmelCase     =     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}"""							)
       _UpperCAmelCase     =     0
       for raw in iterator:
              acc += raw
              if stream and len(SCREAMING_SNAKE_CASE							) < chunk_len:
                     _UpperCAmelCase     =     (_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(SCREAMING_SNAKE_CASE							) >= chunk_len:
                            # We are flushing the accumulator
                            _UpperCAmelCase     =     (_stride_left, stride_right)
                            _UpperCAmelCase     =     {'raw': acc[:chunk_len], 'stride': stride}
                            if stream:
                                   _UpperCAmelCase     =     False
                            yield item
                            _UpperCAmelCase     =     stride_left
                            _UpperCAmelCase     =     acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(SCREAMING_SNAKE_CASE							) > stride_left:
              _UpperCAmelCase     =     {'raw': acc, 'stride': (_stride_left, 0)}
              if stream:
                     _UpperCAmelCase     =     False
              yield item


def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     2**24  # 16Mo
       try:
              with subprocess.Popen(SCREAMING_SNAKE_CASE,stdout=subprocess.PIPE,bufsize=SCREAMING_SNAKE_CASE							) as ffmpeg_process:
                     while True:
                            _UpperCAmelCase     =     ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE							)
                            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













"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       return model


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






