





'''simple docstring'''





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

import numpy as np






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

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






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

		SCREAMING_SNAKE_CASE__			=      platform.system()
		if system == "Linux":
				SCREAMING_SNAKE_CASE__			=      """alsa"""
				SCREAMING_SNAKE_CASE__			=      """default"""
		elif system == "Darwin":
				SCREAMING_SNAKE_CASE__			=      """avfoundation"""
				SCREAMING_SNAKE_CASE__			=      """:0"""
		elif system == "Windows":
				SCREAMING_SNAKE_CASE__			=      """dshow"""
				SCREAMING_SNAKE_CASE__			=      """default"""

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






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

		SCREAMING_SNAKE_CASE__			=      ffmpeg_microphone(__SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE					,   format_for_conversion=__SCREAMING_SNAKE_CASE    )
		if format_for_conversion == "s16le":
				SCREAMING_SNAKE_CASE__			=      np.intaa
				SCREAMING_SNAKE_CASE__			=      2
		elif format_for_conversion == "f32le":
				SCREAMING_SNAKE_CASE__			=      np.floataa
				SCREAMING_SNAKE_CASE__			=      4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''    )

		if stride_length_s is None:
				SCREAMING_SNAKE_CASE__			=      chunk_length_s / 6
		SCREAMING_SNAKE_CASE__			=      int(round(sampling_rate * chunk_length_s    )    ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE					,   (int, float)    ):
				SCREAMING_SNAKE_CASE__			=      [stride_length_s, stride_length_s]

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






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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE					,   stdout=subprocess.PIPE					,   bufsize=__SCREAMING_SNAKE_CASE    ) as ffmpeg_process:
						while True:
								SCREAMING_SNAKE_CASE__			=      ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE    )
								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






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):
							lowerCamelCase_           =					"nat"

							lowerCamelCase_           =					{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :List[Any]							,				__A    :Optional[Any]=4							,				__A    :Any=3							,				__A    :Optional[int]=64							,				__A    :Optional[int]=[3, 4, 6, 5]							,				__A    :Union[str, Any]=[2, 4, 8, 16]							,				__A    :Optional[Any]=7							,				__A    :Optional[Any]=3.0							,				__A    :List[Any]=True							,				__A    :int=0.0							,				__A    :Dict=0.0							,				__A    :Optional[Any]=0.1							,				__A    :str="gelu"							,				__A    :Optional[Any]=0.0_2							,				__A    :Optional[int]=1E-5							,				__A    :Optional[int]=0.0							,				__A    :Optional[Any]=None							,				__A    :Union[str, Any]=None							,				**__A    :Union[str, Any]							,				)   ->   Optional[int]:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__			=      patch_size
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      embed_dim
									SCREAMING_SNAKE_CASE__			=      depths
									SCREAMING_SNAKE_CASE__			=      len(__A		)
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      kernel_size
									SCREAMING_SNAKE_CASE__			=      mlp_ratio
									SCREAMING_SNAKE_CASE__			=      qkv_bias
									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      drop_path_rate
									SCREAMING_SNAKE_CASE__			=      hidden_act
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      initializer_range
									# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									SCREAMING_SNAKE_CASE__			=      int(embed_dim * 2 ** (len(__A		) - 1)		)
									SCREAMING_SNAKE_CASE__			=      layer_scale_init_value
									SCREAMING_SNAKE_CASE__			=      ["""stem"""] + [f'''stage{idx}''' for idx in range(1							,				len(__A		) + 1		)]
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      get_aligned_output_features_output_indices(
									    out_features=__A							,				out_indices=__A							,				stage_names=self.stage_names		)