






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()
__lowerCamelCase      :			Dict							       =		logging.get_logger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str=False     )							-> Tuple:







  """simple docstring"""





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

  return new_state_dict



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """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)
      SCREAMING_SNAKE_CASE__      							=				state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""     )
      SCREAMING_SNAKE_CASE__      							=				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
      SCREAMING_SNAKE_CASE__      							=				kv_weight[
          : config.hidden_sizes[i], :
      ]
      SCREAMING_SNAKE_CASE__      							=				kv_bias[: config.hidden_sizes[i]]
      SCREAMING_SNAKE_CASE__      							=				kv_weight[
          config.hidden_sizes[i] :, :
      ]
      SCREAMING_SNAKE_CASE__      							=				kv_bias[
          config.hidden_sizes[i] :
      ]



def      __SCREAMING_SNAKE_CASE  ( )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE__      							=				Image.open(requests.get(a_			,					stream=a_     ).raw     )

  return image



@torch.no_grad()
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				SegformerConfig()
  SCREAMING_SNAKE_CASE__      							=				False

  # set attributes based on model_name
  SCREAMING_SNAKE_CASE__      							=				"""huggingface/label-files"""
  if "segformer" in model_name:
    SCREAMING_SNAKE_CASE__      							=				model_name[len("""segformer."""     ) : len("""segformer."""     ) + 2]
    if "ade" in model_name:
      SCREAMING_SNAKE_CASE__      							=				1_50
      SCREAMING_SNAKE_CASE__      							=				"""ade20k-id2label.json"""
      SCREAMING_SNAKE_CASE__      							=				(1, 1_50, 1_28, 1_28)
    elif "city" in model_name:
      SCREAMING_SNAKE_CASE__      							=				19
      SCREAMING_SNAKE_CASE__      							=				"""cityscapes-id2label.json"""
      SCREAMING_SNAKE_CASE__      							=				(1, 19, 1_28, 1_28)
    else:
      raise ValueError(f"""Model {model_name} not supported"""     )
  elif "mit" in model_name:
    SCREAMING_SNAKE_CASE__      							=				True
    SCREAMING_SNAKE_CASE__      							=				model_name[4:6]
    SCREAMING_SNAKE_CASE__      							=				10_00
    SCREAMING_SNAKE_CASE__      							=				"""imagenet-1k-id2label.json"""
    SCREAMING_SNAKE_CASE__      							=				(1, 10_00)
  else:
    raise ValueError(f"""Model {model_name} not supported"""     )

  # set config attributes
  SCREAMING_SNAKE_CASE__      							=				json.load(open(hf_hub_download(a_			,					a_			,					repo_type="""dataset"""     )			,					"""r"""     )     )
  SCREAMING_SNAKE_CASE__      							=				{int(a_     ): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE__      							=				idalabel
  SCREAMING_SNAKE_CASE__      							=				{v: k for k, v in idalabel.items()}
  if size == "b0":
    pass
  elif size == "b1":
    SCREAMING_SNAKE_CASE__      							=				[64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__      							=				2_56
  elif size == "b2":
    SCREAMING_SNAKE_CASE__      							=				[64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__      							=				7_68
    SCREAMING_SNAKE_CASE__      							=				[3, 4, 6, 3]
  elif size == "b3":
    SCREAMING_SNAKE_CASE__      							=				[64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__      							=				7_68
    SCREAMING_SNAKE_CASE__      							=				[3, 4, 18, 3]
  elif size == "b4":
    SCREAMING_SNAKE_CASE__      							=				[64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__      							=				7_68
    SCREAMING_SNAKE_CASE__      							=				[3, 8, 27, 3]
  elif size == "b5":
    SCREAMING_SNAKE_CASE__      							=				[64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__      							=				7_68
    SCREAMING_SNAKE_CASE__      							=				[3, 6, 40, 3]
  else:
    raise ValueError(f"""Size {size} not supported"""     )

  # load image processor (only resize + normalize)
  SCREAMING_SNAKE_CASE__      							=				SegformerImageProcessor(
      image_scale=(5_12, 5_12)			,					keep_ratio=a_			,					align=a_			,					do_random_crop=a_     )

  # prepare image
  SCREAMING_SNAKE_CASE__      							=				prepare_img()
  SCREAMING_SNAKE_CASE__      							=				image_processor(images=a_			,					return_tensors="""pt"""     ).pixel_values

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

  # load original state dict
  if encoder_only:
    SCREAMING_SNAKE_CASE__      							=				torch.load(a_			,					map_location=torch.device("""cpu"""     )     )
  else:
    SCREAMING_SNAKE_CASE__      							=				torch.load(a_			,					map_location=torch.device("""cpu"""     )     )["""state_dict"""]

  # rename keys
  SCREAMING_SNAKE_CASE__      							=				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:
    SCREAMING_SNAKE_CASE__      							=				False
    SCREAMING_SNAKE_CASE__      							=				SegformerForImageClassification(a_     )
  else:
    SCREAMING_SNAKE_CASE__      							=				SegformerForSemanticSegmentation(a_     )
  model.load_state_dict(a_     )
  model.eval()

  # forward pass
  SCREAMING_SNAKE_CASE__      							=				model(a_     )
  SCREAMING_SNAKE_CASE__      							=				outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
            [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
        ]     )
  elif model_name == "segformer.b1.512x512.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
            [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
            [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
        ]     )
  elif model_name == "segformer.b2.512x512.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
            [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
            [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
        ]     )
  elif model_name == "segformer.b3.512x512.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
            [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
            [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
        ]     )
  elif model_name == "segformer.b4.512x512.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
            [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
            [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
        ]     )
  elif model_name == "segformer.b5.640x640.ade.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
            [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
            [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
        ]     )
  # Cityscapes checkpoints
  elif model_name == "segformer.b0.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
            [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
            [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
        ]     )
  elif model_name == "segformer.b0.512x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
            [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
            [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
        ]     )
  elif model_name == "segformer.b0.640x1280.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [
                [-1.1372E01, -1.2787E01, -1.3477E01],
                [-1.2536E01, -1.4194E01, -1.4409E01],
                [-1.3217E01, -1.4888E01, -1.5327E01],
            ],
            [
                [-1.4791E01, -1.7122E01, -1.8277E01],
                [-1.7163E01, -1.9192E01, -1.9533E01],
                [-1.7897E01, -1.9991E01, -2.0315E01],
            ],
            [
                [7.6723E-01, 4.1921E-01, -7.7878E-02],
                [4.7772E-01, 9.5557E-03, -2.8082E-01],
                [3.6032E-01, -2.4826E-01, -5.1168E-01],
            ],
        ]     )
  elif model_name == "segformer.b0.768x768.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
            [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
            [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
        ]     )
  elif model_name == "segformer.b1.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
            [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ]     )
  elif model_name == "segformer.b2.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
            [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
            [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
        ]     )
  elif model_name == "segformer.b3.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
            [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
            [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
        ]     )
  elif model_name == "segformer.b4.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
            [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
            [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
        ]     )
  elif model_name == "segformer.b5.1024x1024.city.160k":
    SCREAMING_SNAKE_CASE__      							=				torch.tensor(
        [
            [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
            [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
            [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
        ]     )
  else:
    SCREAMING_SNAKE_CASE__      							=				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__":
   __lowerCamelCase      :			List[str]							       =		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.'''
   )
   __lowerCamelCase      :			Optional[int]							       =		parser.parse_args()
   convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__lowerCamelCase      :			Any							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(   self      :    int	,     _lowercase      :    Dict=80	,     _lowercase      :    Union[str, Any]=1_60_00	,     _lowercase      :    str=0.0	,     _lowercase      :    int=10	,     _lowercase      :    str=25	,     _lowercase      :    Union[str, Any]="hamming_window"	,     _lowercase      :    Dict=3_27_68.0	,     _lowercase      :    Optional[int]=0.97	,     _lowercase      :    Optional[int]=1.0	,     _lowercase      :    int=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=False	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(feature_size=_lowercase	,     sampling_rate=_lowercase	,     padding_value=_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				hop_length
     SCREAMING_SNAKE_CASE__      							=				win_length
     SCREAMING_SNAKE_CASE__      							=				frame_signal_scale
     SCREAMING_SNAKE_CASE__      							=				preemphasis_coeff
     SCREAMING_SNAKE_CASE__      							=				mel_floor
     SCREAMING_SNAKE_CASE__      							=				normalize_means
     SCREAMING_SNAKE_CASE__      							=				normalize_vars
     SCREAMING_SNAKE_CASE__      							=				win_function
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask

     SCREAMING_SNAKE_CASE__      							=				win_length * sampling_rate // 10_00
     SCREAMING_SNAKE_CASE__      							=				hop_length * sampling_rate // 10_00

     SCREAMING_SNAKE_CASE__      							=				optimal_fft_length(self.sample_size				)
     SCREAMING_SNAKE_CASE__      							=				(self.n_fft // 2) + 1




   def      __a						(   self      :    str	,     _lowercase      :    np.array				):



     """simple docstring"""






     if self.win_function == "hamming_window":
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function	,     periodic=_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function				)

     SCREAMING_SNAKE_CASE__      							=				mel_filter_bank(
         num_frequency_bins=self.n_freqs	,     num_mel_filters=self.feature_size	,     min_frequency=0.0	,     max_frequency=self.sampling_rate / 2.0	,     sampling_rate=self.sampling_rate	,     )

     SCREAMING_SNAKE_CASE__      							=				spectrogram(
         one_waveform * self.frame_signal_scale	,     window=_lowercase	,     frame_length=self.sample_size	,     hop_length=self.sample_stride	,     fft_length=self.n_fft	,     center=_lowercase	,     preemphasis=self.preemphasis_coeff	,     mel_filters=_lowercase	,     mel_floor=self.mel_floor	,     log_mel="""log"""	,     )
     return msfc_features.T




   def      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     if self.normalize_means:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].mean(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.subtract(_lowercase	,     _lowercase				)
     if self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].std(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.divide(_lowercase	,     _lowercase				)

     if input_length < x.shape[0]:
       SCREAMING_SNAKE_CASE__      							=				padding_value

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__      							=				x.astype(np.floataa				)

     return x




   def      __a						(   self      :    int	,     _lowercase      :    List[np.ndarray]	,     _lowercase      :    Optional[np.ndarray] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowercase	,     _lowercase	,     self.padding_value				) for x, n in zip(_lowercase	,     _lowercase				)]




   def __call__(   self      :    Dict	,     _lowercase      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     _lowercase      :    Union[bool, str, PaddingStrategy] = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    Optional[int] = None	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             f""" {self.sampling_rate} and not {sampling_rate}."""				)
     else:
       logger.warning(
           """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
           """Failing to do so can result in silent errors that might be hard to debug."""				)

     SCREAMING_SNAKE_CASE__      							=				isinstance(_lowercase	,     np.ndarray				) and len(raw_speech.shape				) > 1
     if is_batched_numpy and len(raw_speech.shape				) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
     SCREAMING_SNAKE_CASE__      							=				is_batched_numpy or (
         isinstance(_lowercase	,     (list, tuple)				) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)				))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowercase	,     np.ndarray				):
       SCREAMING_SNAKE_CASE__      							=				np.asarray(_lowercase	,     dtype=np.floataa				)
     elif isinstance(_lowercase	,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
       SCREAMING_SNAKE_CASE__      							=				raw_speech.astype(np.floataa				)

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__      							=				[raw_speech]

     # extract fbank features
     SCREAMING_SNAKE_CASE__      							=				[self._extract_mfsc_features(_lowercase				) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__      							=				BatchFeature({"""input_features""": features}				)

     SCREAMING_SNAKE_CASE__      							=				self.pad(
         _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     truncation=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     **_lowercase	,     )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""input_features"""				)
     if isinstance(input_features[0]	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for feature in input_features]

     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""attention_mask"""				)
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.intaa				) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				(
           np.array(_lowercase	,     dtype=np.intaa				)
           if self._get_padding_strategies(_lowercase	,     max_length=_lowercase				) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       SCREAMING_SNAKE_CASE__      							=				self.normalize(
           padded_inputs["""input_features"""]	,     attention_mask=_lowercase				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__      							=				padded_inputs.convert_to_tensors(_lowercase				)

     return padded_inputs
