
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


SCREAMING_SNAKE_CASE				:Optional[Any]									=							logging.get_logger(__name__)





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					str				,A   :					Any					):
					__A       =					question_encoder
					__A       =					generator
					__A       =					self.question_encoder






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[Any]					):
					if os.path.isfile(A					):
									raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''					)
					os.makedirs(A				,exist_ok=A					)
					__A       =					os.path.join(A				,"question_encoder_tokenizer"					)
					__A       =					os.path.join(A				,"generator_tokenizer"					)
					self.question_encoder.save_pretrained(A					)
					self.generator.save_pretrained(A					)






	@classmethod
	def 							UpperCamelCase_    (			cls   :					Any				,A   :					str				,**A   :					Optional[Any]					):
					# dynamically import AutoTokenizer
					from ..auto.tokenization_auto import AutoTokenizer

					__A       =					kwargs.pop("config"				,A					)

					if config is None:
									__A       =					RagConfig.from_pretrained(A					)

					__A       =					AutoTokenizer.from_pretrained(
					    A				,config=config.question_encoder				,subfolder="question_encoder_tokenizer"					)
					__A       =					AutoTokenizer.from_pretrained(
					    A				,config=config.generator				,subfolder="generator_tokenizer"					)
					return cls(question_encoder=A				,generator=A					)






	def __call__(			self   :					Tuple				,*A   :					List[str]				,**A   :					str					):
					return self.current_tokenizer(*A				,**A					)






	def 							UpperCamelCase_    (			self   :					List[str]				,*A   :					List[str]				,**A   :					List[str]					):
					return self.generator.batch_decode(*A				,**A					)






	def 							UpperCamelCase_    (			self   :					int				,*A   :					int				,**A   :					Union[str, Any]					):
					return self.generator.decode(*A				,**A					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.question_encoder






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.generator







	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					List[str]				,A   :					Optional[List[str]] = None				,A   :					Optional[int] = None				,A   :					Optional[int] = None				,A   :					str = "longest"				,A   :					str = None				,A   :					bool = True				,**A   :					Tuple				,):
					warnings.warn(
					    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
					    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
					    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
					    "details"				,A				,)
					if max_length is None:
									__A       =					self.current_tokenizer.model_max_length
					__A       =					self(
					    A				,add_special_tokens=A				,return_tensors=A				,max_length=A				,padding=A				,truncation=A				,**A				,)
					if tgt_texts is None:
									return model_inputs
					# Process tgt_texts
					if max_target_length is None:
									__A       =					self.current_tokenizer.model_max_length
					__A       =					self(
					    text_target=A				,add_special_tokens=A				,return_tensors=A				,padding=A				,max_length=A				,truncation=A				,**A				,)
					__A       =					labels["input_ids"]
					return model_inputs



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



