






'''simple docstring'''







import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_A     :      Optional[int]   					=logging.get_logger(__name__)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	str:
							lowerCamelCase__ :     Union[str, Any]	  =	SwinConfig.from_pretrained(
							    """microsoft/swin-tiny-patch4-window7-224"""    ,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				)
							lowerCamelCase__ :     Dict	  =	MaskFormerConfig(backbone_config=UpperCamelCase				)

							lowerCamelCase__ :     str	  =	"""huggingface/label-files"""
							if "ade20k-full" in model_name:
														# this should be ok
														lowerCamelCase__ :     List[Any]	  =	847
														lowerCamelCase__ :     int	  =	"""maskformer-ade20k-full-id2label.json"""
							elif "ade" in model_name:
														# this should be ok
														lowerCamelCase__ :     Optional[Any]	  =	150
														lowerCamelCase__ :     Tuple	  =	"""ade20k-id2label.json"""
							elif "coco-stuff" in model_name:
														# this should be ok
														lowerCamelCase__ :     Tuple	  =	171
														lowerCamelCase__ :     List[Any]	  =	"""maskformer-coco-stuff-id2label.json"""
							elif "coco" in model_name:
														# TODO
														lowerCamelCase__ :     Optional[Any]	  =	133
														lowerCamelCase__ :     List[Any]	  =	"""coco-panoptic-id2label.json"""
							elif "cityscapes" in model_name:
														# this should be ok
														lowerCamelCase__ :     int	  =	19
														lowerCamelCase__ :     List[Any]	  =	"""cityscapes-id2label.json"""
							elif "vistas" in model_name:
														# this should be ok
														lowerCamelCase__ :     Tuple	  =	65
														lowerCamelCase__ :     Optional[int]	  =	"""mapillary-vistas-id2label.json"""

							lowerCamelCase__ :     Dict	  =	json.load(open(hf_hub_download(UpperCamelCase    ,      UpperCamelCase    ,      repo_type="""dataset"""				)    ,      """r"""				)				)
							lowerCamelCase__ :     Tuple	  =	{int(UpperCamelCase				): v for k, v in idalabel.items()}

							return config





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     Dict	  =	[]
							# stem
							# fmt: off
							rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")				)
							rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")				)
							rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")				)
							rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")				)
							# stages
							for i in range(len(config.backbone_config.depths				)				):
														for j in range(config.backbone_config.depths[i]				):
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')				)

														if i < 3:
																					rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')				)
																					rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')				)
														rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')				)
														rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')				)

							# FPN
							rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")				)
							rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")				)
							rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")				)
							for source_index, target_index in zip(range(3    ,      0    ,      -1				)    ,      range(0    ,      3				)				):
														rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')				)
														rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')				)
														rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')				)
														rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')				)
														rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')				)
														rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')				)
							rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")				)
							rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")				)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers				):
														# self-attention out projection
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')				)
														# cross-attention out projection
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')				)
														# MLP 1
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')				)
														# MLP 2
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')				)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')				)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')				)
														# layernorm 3 (final layernorm)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')				)

							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")				)
							rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")				)

							# heads on top
							rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")				)

							rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")				)
							rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")				)

							rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")				)
							rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")				)

							for i in range(3				):
														rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')				)
														rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')				)
							# fmt: on

							return rename_keys





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Any:
							lowerCamelCase__ :     Tuple	  =	dct.pop(UpperCamelCase				)
							lowerCamelCase__ :     Optional[int]	  =	val





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Any	  =	[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
							for i in range(len(backbone_config.depths				)				):
														lowerCamelCase__ :     str	  =	num_features[i]
														for j in range(backbone_config.depths[i]				):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					lowerCamelCase__ :     str	  =	state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''				)
																					lowerCamelCase__ :     Any	  =	state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''				)
																					# next, add query, keys and values (in that order) to the state dict
																					lowerCamelCase__ :     Union[str, Any]	  =	in_proj_weight[:dim, :]
																					lowerCamelCase__ :     int	  =	in_proj_bias[: dim]
																					lowerCamelCase__ :     int	  =	in_proj_weight[
																					    dim : dim * 2, :
																					]
																					lowerCamelCase__ :     Union[str, Any]	  =	in_proj_bias[
																					    dim : dim * 2
																					]
																					lowerCamelCase__ :     Optional[Any]	  =	in_proj_weight[
																					    -dim :, :
																					]
																					lowerCamelCase__ :     Union[str, Any]	  =	in_proj_bias[-dim :]
																					# fmt: on





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	str:
							# fmt: off
							lowerCamelCase__ :     List[Any]	  =	config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers				):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														lowerCamelCase__ :     List[str]	  =	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''				)
														lowerCamelCase__ :     Optional[Any]	  =	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''				)
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__ :     Tuple	  =	in_proj_weight[: hidden_size, :]
														lowerCamelCase__ :     Dict	  =	in_proj_bias[:config.hidden_size]
														lowerCamelCase__ :     Any	  =	in_proj_weight[hidden_size : hidden_size * 2, :]
														lowerCamelCase__ :     Any	  =	in_proj_bias[hidden_size : hidden_size * 2]
														lowerCamelCase__ :     Any	  =	in_proj_weight[-hidden_size :, :]
														lowerCamelCase__ :     int	  =	in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														lowerCamelCase__ :     str	  =	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''				)
														lowerCamelCase__ :     int	  =	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''				)
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__ :     Any	  =	in_proj_weight[: hidden_size, :]
														lowerCamelCase__ :     Optional[Any]	  =	in_proj_bias[:config.hidden_size]
														lowerCamelCase__ :     str	  =	in_proj_weight[hidden_size : hidden_size * 2, :]
														lowerCamelCase__ :     List[str]	  =	in_proj_bias[hidden_size : hidden_size * 2]
														lowerCamelCase__ :     List[str]	  =	in_proj_weight[-hidden_size :, :]
														lowerCamelCase__ :     Optional[Any]	  =	in_proj_bias[-hidden_size :]
							# fmt: on





def 			SCREAMING_SNAKE_CASE_	()				->	torch.Tensor:
							lowerCamelCase__ :     Optional[int]	  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__ :     Union[str, Any]	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							return im





@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = False				)				->	int:
							lowerCamelCase__ :     List[str]	  =	get_maskformer_config(UpperCamelCase				)

							# load original state_dict
							with open(UpperCamelCase    ,      """rb"""				) as f:
														lowerCamelCase__ :     int	  =	pickle.load(UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	data["""model"""]

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							lowerCamelCase__ :     Any	  =	create_rename_keys(UpperCamelCase				)
							for src, dest in rename_keys:
														rename_key(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							read_in_swin_q_k_v(UpperCamelCase    ,      config.backbone_config				)
							read_in_decoder_q_k_v(UpperCamelCase    ,      UpperCamelCase				)

							# update to torch tensors
							for key, value in state_dict.items():
														lowerCamelCase__ :     Optional[Any]	  =	torch.from_numpy(UpperCamelCase				)

							# load 🤗 model
							lowerCamelCase__ :     str	  =	MaskFormerForInstanceSegmentation(UpperCamelCase				)
							model.eval()

							for name, param in model.named_parameters():
														print(UpperCamelCase    ,      param.shape				)

							lowerCamelCase__  ,		lowerCamelCase__ :     Any	  =	model.load_state_dict(UpperCamelCase    ,      strict=UpperCamelCase				)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(UpperCamelCase				) == 0, f'''Unexpected keys: {unexpected_keys}'''

							# verify results
							lowerCamelCase__ :     Optional[Any]	  =	prepare_img()
							if "vistas" in model_name:
														lowerCamelCase__ :     Tuple	  =	65
							elif "cityscapes" in model_name:
														lowerCamelCase__ :     List[Any]	  =	65535
							else:
														lowerCamelCase__ :     str	  =	255
							lowerCamelCase__ :     Any	  =	True if """ade""" in model_name else False
							lowerCamelCase__ :     Optional[int]	  =	MaskFormerImageProcessor(ignore_index=UpperCamelCase    ,      reduce_labels=UpperCamelCase				)

							lowerCamelCase__ :     str	  =	image_processor(UpperCamelCase    ,      return_tensors="""pt"""				)

							lowerCamelCase__ :     Any	  =	model(**UpperCamelCase				)

							print("""Logits:"""    ,      outputs.class_queries_logits[0, :3, :3]				)

							if model_name == "maskformer-swin-tiny-ade":
														lowerCamelCase__ :     int	  =	torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]				)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,      UpperCamelCase    ,      atol=1E-4				)
							print("""Looks ok!"""				)

							if pytorch_dump_folder_path is not None:
														print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''				)
														Path(UpperCamelCase				).mkdir(exist_ok=UpperCamelCase				)
														model.save_pretrained(UpperCamelCase				)
														image_processor.save_pretrained(UpperCamelCase				)

							if push_to_hub:
														print("""Pushing model and image processor to the hub..."""				)
														model.push_to_hub(f'''nielsr/{model_name}'''				)
														image_processor.push_to_hub(f'''nielsr/{model_name}'''				)


if __name__ == "__main__":
			_A     :      List[Any]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''maskformer-swin-tiny-ade''',
			    type=str,
			    help=('''Name of the MaskFormer model you\'d like to convert''',),
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
			    type=str,
			    help='''Path to the original state dict (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			_A     :      Dict   					=parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)









'''simple docstring'''







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


_A     :      List[str]   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	feature_size
														lowerCamelCase__ :     Tuple	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     Union[str, Any]	  =	hop_length
														lowerCamelCase__ :     Any	  =	win_length
														lowerCamelCase__ :     Tuple	  =	frame_signal_scale
														lowerCamelCase__ :     Dict	  =	preemphasis_coeff
														lowerCamelCase__ :     Optional[Any]	  =	mel_floor
														lowerCamelCase__ :     Any	  =	normalize_means
														lowerCamelCase__ :     Optional[int]	  =	normalize_vars
														lowerCamelCase__ :     Dict	  =	win_function
														lowerCamelCase__ :     Dict	  =	return_attention_mask

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[Any]	  =	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      ,							)

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




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

														# make sure array is in float32
														lowerCamelCase__ :     str	  =	x.astype(np.floataa					)

														return x




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[np.ndarray]      ,							UpperCamelCase__:    Optional[np.ndarray] = None					):
														lowerCamelCase__ :     Optional[int]	  =	attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(UpperCamelCase__      ,							UpperCamelCase__      ,							self.padding_value					) for x, n in zip(UpperCamelCase__      ,							UpperCamelCase__					)]




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

														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."""					)

														lowerCamelCase__ :     List[str]	  =	isinstance(UpperCamelCase__      ,							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}'''					)
														lowerCamelCase__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

														if is_batched:
																					lowerCamelCase__ :     str	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for speech in raw_speech]
														elif not is_batched and not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					lowerCamelCase__ :     List[Any]	  =	np.asarray(UpperCamelCase__      ,							dtype=np.floataa					)
														elif isinstance(UpperCamelCase__      ,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																					lowerCamelCase__ :     Optional[Any]	  =	raw_speech.astype(np.floataa					)

														# always return batch
														if not is_batched:
																					lowerCamelCase__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

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

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

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

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

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

