

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__												=						logging.get_logger(__name__)



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     Dict=False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						OrderedDict()
					for key, value in state_dict.items():
										if encoder_only and not key.startswith('''head'''      ):
															_a      :       Dict   =						'''segformer.encoder.''' + key
										if key.startswith('''backbone'''      ):
															_a      :       Union[str, Any]   =						key.replace('''backbone'''						,'''segformer.encoder'''      )
										if "patch_embed" in key:
															# replace for example patch_embed1 by patch_embeddings.0
															_a      :       Optional[int]   =						key[key.find('''patch_embed'''      ) + len('''patch_embed'''      )]
															_a      :       List[str]   =						key.replace(F"""patch_embed{idx}"""						,F"""patch_embeddings.{int(__a      )-1}"""      )
										if "norm" in key:
															_a      :       List[str]   =						key.replace('''norm'''						,'''layer_norm'''      )
										if "segformer.encoder.layer_norm" in key:
															# replace for example layer_norm1 by layer_norm.0
															_a      :       int   =						key[key.find('''segformer.encoder.layer_norm'''      ) + len('''segformer.encoder.layer_norm'''      )]
															_a      :       int   =						key.replace(F"""layer_norm{idx}"""						,F"""layer_norm.{int(__a      )-1}"""      )
										if "layer_norm1" in key:
															_a      :       Tuple   =						key.replace('''layer_norm1'''						,'''layer_norm_1'''      )
										if "layer_norm2" in key:
															_a      :       List[Any]   =						key.replace('''layer_norm2'''						,'''layer_norm_2'''      )
										if "block" in key:
															# replace for example block1 by block.0
															_a      :       List[str]   =						key[key.find('''block'''      ) + len('''block'''      )]
															_a      :       Tuple   =						key.replace(F"""block{idx}"""						,F"""block.{int(__a      )-1}"""      )
										if "attn.q" in key:
															_a      :       Dict   =						key.replace('''attn.q'''						,'''attention.self.query'''      )
										if "attn.proj" in key:
															_a      :       Union[str, Any]   =						key.replace('''attn.proj'''						,'''attention.output.dense'''      )
										if "attn" in key:
															_a      :       Any   =						key.replace('''attn'''						,'''attention.self'''      )
										if "fc1" in key:
															_a      :       List[str]   =						key.replace('''fc1'''						,'''dense1'''      )
										if "fc2" in key:
															_a      :       Any   =						key.replace('''fc2'''						,'''dense2'''      )
										if "linear_pred" in key:
															_a      :       List[str]   =						key.replace('''linear_pred'''						,'''classifier'''      )
										if "linear_fuse" in key:
															_a      :       List[Any]   =						key.replace('''linear_fuse.conv'''						,'''linear_fuse'''      )
															_a      :       List[str]   =						key.replace('''linear_fuse.bn'''						,'''batch_norm'''      )
										if "linear_c" in key:
															# replace for example linear_c4 by linear_c.3
															_a      :       Dict   =						key[key.find('''linear_c'''      ) + len('''linear_c'''      )]
															_a      :       Optional[Any]   =						key.replace(F"""linear_c{idx}"""						,F"""linear_c.{int(__a      )-1}"""      )
										if key.startswith('''head'''      ):
															_a      :       List[str]   =						key.replace('''head'''						,'''classifier'''      )
										_a      :       Any   =						value

					return new_state_dict



def   __UpperCAmelCase					(      __a :     str						,__a :     Union[str, Any]      )     ->	str:

					"""simple docstring"""

					for i in range(config.num_encoder_blocks      ):
										for j in range(config.depths[i]      ):
															# read in weights + bias of keys and values (which is a single matrix in the original implementation)
															_a      :       Any   =						state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
															_a      :       List[str]   =						state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
															# next, add keys and values (in that order) to the state dict
															_a      :       Tuple   =						kv_weight[
															    : config.hidden_sizes[i], :
															]
															_a      :       Tuple   =						kv_bias[: config.hidden_sizes[i]]
															_a      :       str   =						kv_weight[
															    config.hidden_sizes[i] :, :
															]
															_a      :       Dict   =						kv_bias[
															    config.hidden_sizes[i] :
															]



def   __UpperCAmelCase					(      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       int   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )

					return image



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Dict						,__a :     str						,__a :     Optional[int]      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						SegformerConfig()
					_a      :       int   =						False

					# set attributes based on model_name
					_a      :       Union[str, Any]   =						'''huggingface/label-files'''
					if "segformer" in model_name:
										_a      :       List[str]   =						model_name[len('''segformer.'''      ) : len('''segformer.'''      ) + 2]
										if "ade" in model_name:
															_a      :       Optional[Any]   =						150
															_a      :       List[Any]   =						'''ade20k-id2label.json'''
															_a      :       Any   =						(1, 150, 128, 128)
										elif "city" in model_name:
															_a      :       Tuple   =						19
															_a      :       List[str]   =						'''cityscapes-id2label.json'''
															_a      :       int   =						(1, 19, 128, 128)
										else:
															raise ValueError(F"""Model {model_name} not supported"""      )
					elif "mit" in model_name:
										_a      :       List[Any]   =						True
										_a      :       Optional[Any]   =						model_name[4:6]
										_a      :       Dict   =						1_000
										_a      :       Union[str, Any]   =						'''imagenet-1k-id2label.json'''
										_a      :       Union[str, Any]   =						(1, 1_000)
					else:
										raise ValueError(F"""Model {model_name} not supported"""      )

					# set config attributes
					_a      :       Optional[Any]   =						json.load(open(hf_hub_download(__a						,__a						,repo_type='''dataset'''      )						,'''r'''      )      )
					_a      :       int   =						{int(__a      ): v for k, v in idalabel.items()}
					_a      :       int   =						idalabel
					_a      :       List[Any]   =						{v: k for k, v in idalabel.items()}
					if size == "b0":
										pass
					elif size == "b1":
										_a      :       str   =						[64, 128, 320, 512]
										_a      :       Optional[int]   =						256
					elif size == "b2":
										_a      :       List[str]   =						[64, 128, 320, 512]
										_a      :       Any   =						768
										_a      :       Dict   =						[3, 4, 6, 3]
					elif size == "b3":
										_a      :       Any   =						[64, 128, 320, 512]
										_a      :       List[Any]   =						768
										_a      :       List[Any]   =						[3, 4, 18, 3]
					elif size == "b4":
										_a      :       Dict   =						[64, 128, 320, 512]
										_a      :       int   =						768
										_a      :       List[Any]   =						[3, 8, 27, 3]
					elif size == "b5":
										_a      :       Any   =						[64, 128, 320, 512]
										_a      :       List[str]   =						768
										_a      :       Optional[int]   =						[3, 6, 40, 3]
					else:
										raise ValueError(F"""Size {size} not supported"""      )

					# load image processor (only resize + normalize)
					_a      :       str   =						SegformerImageProcessor(
					    image_scale=(512, 512)						,keep_ratio=__a						,align=__a						,do_random_crop=__a      )

					# prepare image
					_a      :       List[Any]   =						prepare_img()
					_a      :       Union[str, Any]   =						image_processor(images=__a						,return_tensors='''pt'''      ).pixel_values

					logger.info(F"""Converting model {model_name}..."""      )

					# load original state dict
					if encoder_only:
										_a      :       List[Any]   =						torch.load(__a						,map_location=torch.device('''cpu'''      )      )
					else:
										_a      :       Any   =						torch.load(__a						,map_location=torch.device('''cpu'''      )      )['''state_dict''']

					# rename keys
					_a      :       int   =						rename_keys(__a						,encoder_only=__a      )
					if not encoder_only:
										del state_dict["decode_head.conv_seg.weight"]
										del state_dict["decode_head.conv_seg.bias"]

					# key and value matrices need special treatment
					read_in_k_v(__a						,__a      )

					# create HuggingFace model and load state dict
					if encoder_only:
										_a      :       Dict   =						False
										_a      :       Optional[int]   =						SegformerForImageClassification(__a      )
					else:
										_a      :       List[str]   =						SegformerForSemanticSegmentation(__a      )
					model.load_state_dict(__a      )
					model.eval()

					# forward pass
					_a      :       int   =						model(__a      )
					_a      :       List[Any]   =						outputs.logits

					# set expected_slice based on model name
					# ADE20k checkpoints
					if model_name == "segformer.b0.512x512.ade.160k":
										_a      :       Optional[int]   =						torch.tensor(
										    [
										        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
										        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
										        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
										    ]      )
					elif model_name == "segformer.b1.512x512.ade.160k":
										_a      :       Optional[int]   =						torch.tensor(
										    [
										        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
										        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
										        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
										    ]      )
					elif model_name == "segformer.b2.512x512.ade.160k":
										_a      :       Union[str, Any]   =						torch.tensor(
										    [
										        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
										        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
										        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
										    ]      )
					elif model_name == "segformer.b3.512x512.ade.160k":
										_a      :       List[Any]   =						torch.tensor(
										    [
										        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
										        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
										        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
										    ]      )
					elif model_name == "segformer.b4.512x512.ade.160k":
										_a      :       List[str]   =						torch.tensor(
										    [
										        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
										        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
										        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
										    ]      )
					elif model_name == "segformer.b5.640x640.ade.160k":
										_a      :       List[str]   =						torch.tensor(
										    [
										        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
										        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
										        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
										    ]      )
					# Cityscapes checkpoints
					elif model_name == "segformer.b0.1024x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
										        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
										        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
										    ]      )
					elif model_name == "segformer.b0.512x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
										        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
										        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
										    ]      )
					elif model_name == "segformer.b0.640x1280.city.160k":
										_a      :       Optional[int]   =						torch.tensor(
										    [
										        [
										            [-1.1_372E01, -1.2_787E01, -1.3_477E01],
										            [-1.2_536E01, -1.4_194E01, -1.4_409E01],
										            [-1.3_217E01, -1.4_888E01, -1.5_327E01],
										        ],
										        [
										            [-1.4_791E01, -1.7_122E01, -1.8_277E01],
										            [-1.7_163E01, -1.9_192E01, -1.9_533E01],
										            [-1.7_897E01, -1.9_991E01, -2.0_315E01],
										        ],
										        [
										            [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
										            [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
										            [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
										        ],
										    ]      )
					elif model_name == "segformer.b0.768x768.city.160k":
										_a      :       Optional[Any]   =						torch.tensor(
										    [
										        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
										        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
										        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
										    ]      )
					elif model_name == "segformer.b1.1024x1024.city.160k":
										_a      :       str   =						torch.tensor(
										    [
										        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
										        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
										        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
										    ]      )
					elif model_name == "segformer.b2.1024x1024.city.160k":
										_a      :       int   =						torch.tensor(
										    [
										        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
										        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
										        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
										    ]      )
					elif model_name == "segformer.b3.1024x1024.city.160k":
										_a      :       Dict   =						torch.tensor(
										    [
										        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
										        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
										        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
										    ]      )
					elif model_name == "segformer.b4.1024x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
										        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
										        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
										    ]      )
					elif model_name == "segformer.b5.1024x1024.city.160k":
										_a      :       int   =						torch.tensor(
										    [
										        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
										        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
										        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
										    ]      )
					else:
										_a      :       List[Any]   =						logits.argmax(-1      ).item()
										print('''Predicted class:'''						,model.config.idalabel[predicted_class_idx]      )

					# verify logits
					if not encoder_only:
										assert logits.shape == expected_shape
										assert torch.allclose(logits[0, :3, :3, :3]						,__a						,atol=1E-2      )

					# finally, save model and image processor
					logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
					Path(__a      ).mkdir(exist_ok=__a      )
					model.save_pretrained(__a      )
					image_processor.save_pretrained(__a      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    default='''segformer.b0.512x512.ade.160k''',
			    type=str,
			    help='''Name of the model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			a__												=						parser.parse_args()
			convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






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



