


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

import numpy as np






def 		_A		(		_lowercase					,  _lowercase )					-> 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(_lowercase					,  stdin=subprocess.PIPE					,  stdout=subprocess.PIPE ) as ffmpeg_process:
               __UpperCamelCase =			ffmpeg_process.communicate(_lowercase )
     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(_lowercase					,  np.floataa )
     if audio.shape[0] == 0:
          raise ValueError('Malformed soundfile' )
     return audio






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase = "f32le"					,  )					-> List[str]:


     """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(_lowercase					,  _lowercase )
     for item in iterator:
          yield item






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase = None					,  _lowercase = None					,  _lowercase = "f32le"					,  )					-> Tuple:


     """simple docstring"""


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

     __UpperCamelCase =			ffmpeg_microphone(_lowercase					,  _lowercase					,  format_for_conversion=_lowercase )
     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(_lowercase					,  (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=_lowercase )
     for item in chunk_bytes_iter(_lowercase					,  _lowercase					,  stride=(stride_left, stride_right)					,  stream=_lowercase ):
          # Put everything back in numpy scale
          __UpperCamelCase =			np.frombuffer(item['raw']					,  dtype=_lowercase )
          __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 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase = False )					-> Optional[int]:


     """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(_lowercase ) < chunk_len:
               __UpperCamelCase =			(_stride_left, 0)
               yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
          else:
               while len(_lowercase ) >= 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(_lowercase ) > stride_left:
          __UpperCamelCase =			{'raw': acc, 'stride': (_stride_left, 0)}
          if stream:
               __UpperCamelCase =			False
          yield item






def 		_A		(		_lowercase					,  _lowercase )					-> str:


     """simple docstring"""


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

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					R"""\w+[.]\d+"""
   __UpperCamelCase	:			Optional[int]					=					re.findall(__lowerCAmelCase					,							__lowerCAmelCase							)
   for pat in pats:
      __UpperCamelCase	:			str					=					key.replace(__lowerCAmelCase					,							"""_""".join(pat.split("""."""							)							)							)
   return key


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  str:
   __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
   if (
       any("""norm""" in str_ for str_ in pt_tuple_key							)
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      __UpperCamelCase	:			Any					=					pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      __UpperCamelCase	:			Dict					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight":
      __UpperCamelCase	:			int					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]=42							)     ->  int:
   # Step 1: Convert pytorch tensor to numpy
   __UpperCamelCase	:			Dict					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   __UpperCamelCase	:			str					=					flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

   __UpperCamelCase	:			Union[str, Any]					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			str					=					rename_key(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					tuple(renamed_pt_key.split("""."""							)							)

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # also add unexpected weight so that warning is thrown
      __UpperCamelCase	:			Any					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)



