





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

import numpy as np



def A_     (		A__		,				A__    )  ->		np.array:
       a__						:     Optional[Any]			=    F'{sampling_rate}'
       a__						:     Dict			=    '1'
       a__						:     Optional[Any]			=    'f32le'
       a__						:     str			=    [
           '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__						:     int			=    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__						:     List[Any]			=    output_stream[0]
       a__						:     Union[str, Any]			=    np.frombuffer(A__		,				np.floataa    )
       if audio.shape[0] == 0:
              raise ValueError('Malformed soundfile'    )
       return audio



def A_     (		A__		,				A__		,				A__ = "f32le"		,				)  ->		Optional[int]:
       a__						:     Dict			=    F'{sampling_rate}'
       a__						:     Optional[int]			=    '1'
       if format_for_conversion == "s16le":
              a__						:     Union[str, Any]			=    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__						:     Tuple			=    'alsa'
              a__						:     Any			=    'default'
       elif system == "Darwin":
              a__						:     Tuple			=    'avfoundation'
              a__						:     Tuple			=    ':0'
       elif system == "Windows":
              a__						:     Tuple			=    'dshow'
              a__						:     Optional[Any]			=    'default'

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



def A_     (		A__		,				A__		,				A__ = None		,				A__ = None		,				A__ = "f32le"		,				)  ->		Any:
       if stream_chunk_s is not None:
              a__						:     str			=    stream_chunk_s
       else:
              a__						:     Any			=    chunk_length_s

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

       if stride_length_s is None:
              a__						:     int			=    chunk_length_s / 6
       a__						:     Optional[Any]			=    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__						:     Tuple			=    int(round(sampling_rate * stride_length_s[1]    )    ) * size_of_sample
       a__						:     int			=    datetime.datetime.now()
       a__						:     Optional[Any]			=    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__						:     str			=    np.frombuffer(item['raw']		,				dtype=A__    )
              a__						:     Any			=    (
                  item['stride'][0] // size_of_sample,
                  item['stride'][1] // size_of_sample,
              )
              a__						:     Dict			=    sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 10 * delta:
                     # We're late !! SKIP
                     continue
              yield item



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



def A_     (		A__		,				A__    )  ->		List[Any]:
       a__						:     str			=    2**24  # 16Mo
       try:
              with subprocess.Popen(A__		,				stdout=subprocess.PIPE		,				bufsize=A__    ) as ffmpeg_process:
                     while True:
                            a__						:     Optional[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









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A__		,				'rb'    ) as fp:
                     a__						:     int			=    pickle.load(A__		,				encoding='latin1'    )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              a__						:     int			=    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
              print(F'Save vocabulary to {pytorch_vocab_dump_path}'    )
              a__						:     List[Any]			=    corpus.vocab.__dict__
              torch.save(A__		,				A__    )

              a__						:     Dict			=    corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'		,				A__    )
              a__						:     Optional[int]			=    pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(F'Save dataset to {pytorch_dataset_dump_path}'    )
              torch.save(A__		,				A__    )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              a__						:     Union[str, Any]			=    os.path.abspath(A__    )
              a__						:     Optional[Any]			=    os.path.abspath(A__    )

              print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'    )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     a__						:     Dict			=    TransfoXLConfig()
              else:
                     a__						:     Dict			=    TransfoXLConfig.from_json_file(A__    )
              print(F'Building PyTorch model from configuration: {config}'    )
              a__						:     Optional[int]			=    TransfoXLLMHeadModel(A__    )

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    os.path.join(A__		,				A__    )
              print(F'Save PyTorch model to {os.path.abspath(A__    )}'    )
              torch.save(model.state_dict()		,				A__    )
              print(F'Save configuration file to {os.path.abspath(A__    )}'    )
              with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
                     f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	List[Any]      						=  argparse.ArgumentParser()
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""",
        default="""""",
        type=str,
        help="""An optional path to a TensorFlow checkpoint path to be converted.""",
    )
    parser.add_argument(
        """--transfo_xl_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--transfo_xl_dataset_file""",
        default="""""",
        type=str,
        help="""An optional dataset file to be converted in a vocabulary.""",
    )
    lowercase				:	Any      						=  parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )


