

import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     List[Any]						,__a :     List[str]						,__a :     Tuple      )     ->	Dict:

					"""simple docstring"""

					_a      :       Optional[int]   =						FunnelConfig.from_json_file(__a      )
					print(F"""Building PyTorch model from configuration: {config}"""      )
					_a      :       Optional[Any]   =						FunnelBaseModel(__a      ) if base_model else FunnelModel(__a      )

					# Load weights from tf checkpoint
					load_tf_weights_in_funnel(__a						,__a						,__a      )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
					torch.save(model.state_dict()						,__a      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
			)
			a__												=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
			)






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

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

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

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     str = "f32le"						,)     ->	str:

					"""simple docstring"""

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

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

					_a      :       Tuple   =						[
					    '''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      :       str   =						_ffmpeg_stream(__a						,__a      )
					for item in iterator:
										yield item



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     Optional[int] = None						,__a :     Optional[Union[Tuple[float, float], float]] = None						,__a :     str = "f32le"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

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

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

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



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

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







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

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



