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

import numpy as np



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		f'{sampling_rate}'
					lowercase					=		'''1'''
					lowercase					=		'''f32le'''
					lowercase					=		[
					    '''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:
															lowercase					=		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
					lowercase					=		output_stream[0]
					lowercase					=		np.frombuffer(lowerCAmelCase__				,    np.floataa	)
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''	)
					return audio



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = "f32le"				,    ):
					'''simple docstring'''
					lowercase					=		f'{sampling_rate}'
					lowercase					=		'''1'''
					if format_for_conversion == "s16le":
										lowercase					=		2
					elif format_for_conversion == "f32le":
										lowercase					=		4
					else:
										raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'	)

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

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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = "f32le"				,    ):
					'''simple docstring'''
					if stream_chunk_s is not None:
										lowercase					=		stream_chunk_s
					else:
										lowercase					=		chunk_length_s

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

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

					lowercase					=		int(round(sampling_rate * stride_length_s[0]	)	) * size_of_sample
					lowercase					=		int(round(sampling_rate * stride_length_s[1]	)	) * size_of_sample
					lowercase					=		datetime.datetime.now()
					lowercase					=		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
										lowercase					=		np.frombuffer(item['''raw''']				,    dtype=lowerCAmelCase__	)
										lowercase					=		(
										    item['''stride'''][0] // size_of_sample,
										    item['''stride'''][1] // size_of_sample,
										)
										lowercase					=		sampling_rate
										audio_time += delta
										if datetime.datetime.now() > audio_time + 10 * delta:
															# We're late !! SKIP
															continue
										yield item



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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		2**24  # 16Mo
					try:
										with subprocess.Popen(lowerCAmelCase__				,    stdout=subprocess.PIPE				,    bufsize=lowerCAmelCase__	) as ffmpeg_process:
															while True:
																				lowercase					=		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






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    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'''				,    )

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

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					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.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




