
'''simple docstring'''







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

import numpy as np
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: bytes      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Any													=       F'''{sampling_rate}'''
						lowercase_	:     Union[str, Any]													=       '''1'''
						lowercase_	:     Optional[Any]													=       '''f32le'''
						lowercase_	:     Optional[Any]													=       [
						    '''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:
																		lowercase_	:     List[str]													=       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
						lowercase_	:     int													=       output_stream[0]
						lowercase_	:     Dict													=       np.frombuffer(__SCREAMING_SNAKE_CASE      ,      np.floataa							)
						if audio.shape[0] == 0:
												raise ValueError('''Malformed soundfile'''							)
						return audio
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: float      ,      __SCREAMING_SNAKE_CASE					: str = "f32le"      ,      ):



						"""simple docstring"""





						lowercase_	:     str													=       F'''{sampling_rate}'''
						lowercase_	:     Optional[Any]													=       '''1'''
						if format_for_conversion == "s16le":
												lowercase_	:     Any													=       2
						elif format_for_conversion == "f32le":
												lowercase_	:     Optional[int]													=       4
						else:
												raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''							)

						lowercase_	:     List[Any]													=       platform.system()
						if system == "Linux":
												lowercase_	:     Optional[int]													=       '''alsa'''
												lowercase_	:     Dict													=       '''default'''
						elif system == "Darwin":
												lowercase_	:     int													=       '''avfoundation'''
												lowercase_	:     str													=       ''':0'''
						elif system == "Windows":
												lowercase_	:     int													=       '''dshow'''
												lowercase_	:     Optional[Any]													=       '''default'''

						lowercase_	:     Dict													=       [
						    '''ffmpeg''',
						    '''-f''',
						    format_,
						    '''-i''',
						    input_,
						    '''-ac''',
						    ac,
						    '''-ar''',
						    ar,
						    '''-f''',
						    format_for_conversion,
						    '''-fflags''',
						    '''nobuffer''',
						    '''-hide_banner''',
						    '''-loglevel''',
						    '''quiet''',
						    '''pipe:1''',
						]
						lowercase_	:     Any													=       int(round(sampling_rate * chunk_length_s							)							) * size_of_sample
						lowercase_	:     int													=       _ffmpeg_stream(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
						for item in iterator:
												yield item
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: float      ,      __SCREAMING_SNAKE_CASE					: Optional[int] = None      ,      __SCREAMING_SNAKE_CASE					: Optional[Union[Tuple[float, float], float]] = None      ,      __SCREAMING_SNAKE_CASE					: str = "f32le"      ,      ):



						"""simple docstring"""





						if stream_chunk_s is not None:
												lowercase_	:     List[str]													=       stream_chunk_s
						else:
												lowercase_	:     Optional[int]													=       chunk_length_s

						lowercase_	:     List[Any]													=       ffmpeg_microphone(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      format_for_conversion=__SCREAMING_SNAKE_CASE							)
						if format_for_conversion == "s16le":
												lowercase_	:     Tuple													=       np.intaa
												lowercase_	:     Dict													=       2
						elif format_for_conversion == "f32le":
												lowercase_	:     List[Any]													=       np.floataa
												lowercase_	:     List[Any]													=       4
						else:
												raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''							)

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

						lowercase_	:     str													=       int(round(sampling_rate * stride_length_s[0]							)							) * size_of_sample
						lowercase_	:     int													=       int(round(sampling_rate * stride_length_s[1]							)							) * size_of_sample
						lowercase_	:     Tuple													=       datetime.datetime.now()
						lowercase_	:     Dict													=       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
												lowercase_	:     int													=       np.frombuffer(item['''raw''']      ,      dtype=__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Union[str, Any]													=       (
												    item['''stride'''][0] // size_of_sample,
												    item['''stride'''][1] // size_of_sample,
												)
												lowercase_	:     Optional[Any]													=       sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 10 * delta:
																		# We're late !! SKIP
																		continue
												yield item
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Tuple[int, int]      ,      __SCREAMING_SNAKE_CASE					: bool = False							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       B''''''
						lowercase_						, lowercase_	:     Dict													=       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_	:     Any													=       0
						for raw in iterator:
												acc += raw
												if stream and len(__SCREAMING_SNAKE_CASE							) < chunk_len:
																		lowercase_	:     List[str]													=       (_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
																								lowercase_	:     Tuple													=       (_stride_left, stride_right)
																								lowercase_	:     List[str]													=       {'''raw''': acc[:chunk_len], '''stride''': stride}
																								if stream:
																														lowercase_	:     int													=       False
																								yield item
																								lowercase_	:     Optional[Any]													=       stride_left
																								lowercase_	:     Union[str, Any]													=       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(__SCREAMING_SNAKE_CASE							) > stride_left:
												lowercase_	:     Union[str, Any]													=       {'''raw''': acc, '''stride''': (_stride_left, 0)}
												if stream:
																		lowercase_	:     Union[str, Any]													=       False
												yield item




def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     str													=       2**24  # 16Mo
						try:
												with subprocess.Popen(__SCREAMING_SNAKE_CASE      ,      stdout=subprocess.PIPE      ,      bufsize=__SCREAMING_SNAKE_CASE							) as ffmpeg_process:
																		while True:
																								lowercase_	:     Any													=       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







import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

							# Load weights from numpy
							load_tf_weights_in_gpta(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--gpt2_config_file''',
						    default='''''',
						    type=str,
						    help=(
						        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




