


'''simple docstring'''



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

import numpy as np







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     f'''{sampling_rate}'''
			UpperCAmelCase__  :					Optional[int]        =     """1"""
			UpperCAmelCase__  :					Union[str, Any]        =     """f32le"""
			UpperCAmelCase__  :					Any        =     [
			    """ffmpeg""",
			    """-i""",
			    """pipe:0""",
			    """-ac""",
			    ac,
			    """-ar""",
			    ar,
			    """-f""",
			    format_for_conversion,
			    """-hide_banner""",
			    """-loglevel""",
			    """quiet""",
			    """pipe:1""",
			]

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = "f32le" ,      ):
			UpperCAmelCase__  :					List[str]        =     f'''{sampling_rate}'''
			UpperCAmelCase__  :					List[Any]        =     """1"""
			if format_for_conversion == "s16le":
						UpperCAmelCase__  :					Optional[Any]        =     2
			elif format_for_conversion == "f32le":
						UpperCAmelCase__  :					Tuple        =     4
			else:
						raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

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

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = None ,      UpperCamelCase__ = None ,      UpperCamelCase__ = "f32le" ,      ):
			if stream_chunk_s is not None:
						UpperCAmelCase__  :					List[str]        =     stream_chunk_s
			else:
						UpperCAmelCase__  :					Union[str, Any]        =     chunk_length_s

			UpperCAmelCase__  :					Tuple        =     ffmpeg_microphone(UpperCamelCase__ ,      UpperCamelCase__ ,      format_for_conversion=UpperCamelCase__					)
			if format_for_conversion == "s16le":
						UpperCAmelCase__  :					Tuple        =     np.intaa
						UpperCAmelCase__  :					Optional[int]        =     2
			elif format_for_conversion == "f32le":
						UpperCAmelCase__  :					Tuple        =     np.floataa
						UpperCAmelCase__  :					List[str]        =     4
			else:
						raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

			if stride_length_s is None:
						UpperCAmelCase__  :					Optional[Any]        =     chunk_length_s / 6
			UpperCAmelCase__  :					Dict        =     int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
			if isinstance(UpperCamelCase__ ,      (int, float)					):
						UpperCAmelCase__  :					Optional[int]        =     [stride_length_s, stride_length_s]

			UpperCAmelCase__  :					int        =     int(round(sampling_rate * stride_length_s[0]					)					) * size_of_sample
			UpperCAmelCase__  :					Optional[int]        =     int(round(sampling_rate * stride_length_s[1]					)					) * size_of_sample
			UpperCAmelCase__  :					str        =     datetime.datetime.now()
			UpperCAmelCase__  :					Optional[Any]        =     datetime.timedelta(seconds=UpperCamelCase__					)
			for item in chunk_bytes_iter(UpperCamelCase__ ,      UpperCamelCase__ ,      stride=(stride_left, stride_right) ,      stream=UpperCamelCase__					):
						# Put everything back in numpy scale
						UpperCAmelCase__  :					List[Any]        =     np.frombuffer(item["""raw"""] ,      dtype=UpperCamelCase__					)
						UpperCAmelCase__  :					List[Any]        =     (
						    item["""stride"""][0] // size_of_sample,
						    item["""stride"""][1] // size_of_sample,
						)
						UpperCAmelCase__  :					str        =     sampling_rate
						audio_time += delta
						if datetime.datetime.now() > audio_time + 1_0 * delta:
									# We're late !! SKIP
									continue
						yield item







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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Dict        =     2**2_4  # 16Mo
			try:
						with subprocess.Popen(UpperCamelCase__ ,      stdout=subprocess.PIPE ,      bufsize=UpperCamelCase__					) as ffmpeg_process:
									while True:
												UpperCAmelCase__  :					Any        =     ffmpeg_process.stdout.read(UpperCamelCase__					)
												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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)