



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


__lowerCamelCase  :				Tuple					=					logging.get_logger(__name__)





class 			__magic_name__ (				snake_case_    ):
		lowercase   :							Dict        =['''input_features''', '''is_longer''']






		def __init__(    self   :  Optional[int]					,		UpperCamelCase__   :  str=64					,		UpperCamelCase__   :  Any=4_80_00					,		UpperCamelCase__   :  int=4_80					,		UpperCamelCase__   :  Optional[int]=10					,		UpperCamelCase__   :  Optional[Any]=10_24					,		UpperCamelCase__   :  Tuple=0.0					,		UpperCamelCase__   :  List[str]=False					,		UpperCamelCase__   :  Union[str, Any] = 0					,		UpperCamelCase__   :  Optional[Any] = 1_40_00					,		UpperCamelCase__   :  Tuple = None					,		UpperCamelCase__   :  Optional[int] = "fusion"					,		UpperCamelCase__   :  List[str] = "repeatpad"					,		**UpperCamelCase__   :  Tuple					,		)					->       List[str]:
								'''simple docstring'''







								super().__init__(
								    feature_size=UpperCamelCase__					,		sampling_rate=UpperCamelCase__					,		padding_value=UpperCamelCase__					,		return_attention_mask=UpperCamelCase__					,		**UpperCamelCase__					,		)
								UpperCAmelCase								=  top_db
								UpperCAmelCase								=  truncation
								UpperCAmelCase								=  padding
								UpperCAmelCase								=  fft_window_size
								UpperCAmelCase								=  (fft_window_size >> 1) + 1
								UpperCAmelCase								=  hop_length
								UpperCAmelCase								=  max_length_s
								UpperCAmelCase								=  max_length_s * sampling_rate
								UpperCAmelCase								=  sampling_rate
								UpperCAmelCase								=  frequency_min
								UpperCAmelCase								=  frequency_max
								UpperCAmelCase								=  mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins					,		num_mel_filters=UpperCamelCase__					,		min_frequency=UpperCamelCase__					,		max_frequency=UpperCamelCase__					,		sampling_rate=UpperCamelCase__					,		norm=UpperCamelCase__					,		mel_scale="htk"					,		)
								UpperCAmelCase								=  mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins					,		num_mel_filters=UpperCamelCase__					,		min_frequency=UpperCamelCase__					,		max_frequency=UpperCamelCase__					,		sampling_rate=UpperCamelCase__					,		norm="slaney"					,		mel_scale="slaney"					,		)






		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       Dict[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  copy.deepcopy(self.__dict__   )
								UpperCAmelCase								=  self.__class__.__name__
								if "mel_filters" in output:
														del output["mel_filters"]
								if "mel_filters_slaney" in output:
														del output["mel_filters_slaney"]
								return output






		def SCREAMING_SNAKE_CASE_      (    self   :  Any					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  List[Any] = None   )					->       np.ndarray:
								'''simple docstring'''







								UpperCAmelCase								=  spectrogram(
								    UpperCamelCase__					,		window_function(self.fft_window_size					,		"hann"   )					,		frame_length=self.fft_window_size					,		hop_length=self.hop_length					,		power=2.0					,		mel_filters=UpperCamelCase__					,		log_mel="dB"					,		)
								return log_mel_spectrogram.T






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  str   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  np.array_split(list(range(0					,		total_frames - chunk_frames + 1   )   )					,		3   )
								if len(ranges[1]   ) == 0:
														# if the audio is too short, we just use the first chunk
														UpperCAmelCase								=  [0]
								if len(ranges[2]   ) == 0:
														# if the audio is too short, we just use the first chunk
														UpperCAmelCase								=  [0]
								# randomly choose index for each part
								UpperCAmelCase								=  np.random.choice(ranges[0]   )
								UpperCAmelCase								=  np.random.choice(ranges[1]   )
								UpperCAmelCase								=  np.random.choice(ranges[2]   )

								UpperCAmelCase								=  mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase								=  mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase								=  mel[idx_back : idx_back + chunk_frames, :]

								UpperCAmelCase								=  torch.tensor(mel[None, None, :]   )
								UpperCAmelCase								=  torch.nn.functional.interpolate(
								    UpperCamelCase__					,		size=[chunk_frames, 64]					,		mode="bilinear"					,		align_corners=UpperCamelCase__   )
								UpperCAmelCase								=  mel_shrink[0][0].numpy()
								UpperCAmelCase								=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,		axis=0   )
								return mel_fusion






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  int   )					->       np.array:
								'''simple docstring'''







								if waveform.shape[0] > max_length:
														if truncation == "rand_trunc":
																				UpperCAmelCase								=  True
																				# random crop to max_length (for compatibility) -> this should be handled by self.pad
																				UpperCAmelCase								=  len(UpperCamelCase__   ) - max_length
																				UpperCAmelCase								=  np.random.randint(0					,		overflow + 1   )
																				UpperCAmelCase								=  waveform[idx : idx + max_length]
																				UpperCAmelCase								=  self._np_extract_fbank_features(UpperCamelCase__					,		self.mel_filters_slaney   )[None, :]
														elif truncation == "fusion":
																				UpperCAmelCase								=  self._np_extract_fbank_features(UpperCamelCase__					,		self.mel_filters   )
																				UpperCAmelCase								=  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																				UpperCAmelCase								=  mel.shape[0]
																				if chunk_frames == total_frames:
																										# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																										# In this case, we just use the whole audio.
																										UpperCAmelCase								=  np.stack([mel, mel, mel, mel]					,		axis=0   )
																										UpperCAmelCase								=  False
																				else:
																										UpperCAmelCase								=  self._random_mel_fusion(UpperCamelCase__					,		UpperCamelCase__					,		UpperCamelCase__   )
																										UpperCAmelCase								=  True
														else:
																				raise NotImplementedError(F'data_truncating {truncation} not implemented'   )

								else:
														UpperCAmelCase								=  False
														# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
														if waveform.shape[0] < max_length:
																				if padding == "repeat":
																										UpperCAmelCase								=  int(max_length / len(UpperCamelCase__   )   )
																										UpperCAmelCase								=  np.stack(np.tile(UpperCamelCase__					,		n_repeat + 1   )   )[:max_length]
																				if padding == "repeatpad":
																										UpperCAmelCase								=  int(max_length / len(UpperCamelCase__   )   )
																										UpperCAmelCase								=  np.stack(np.tile(UpperCamelCase__					,		UpperCamelCase__   )   )
																				UpperCAmelCase								=  np.pad(UpperCamelCase__					,		(0, max_length - waveform.shape[0])					,		mode="constant"					,		constant_values=0   )

														if truncation == "fusion":
																				UpperCAmelCase								=  self._np_extract_fbank_features(UpperCamelCase__					,		self.mel_filters   )
																				UpperCAmelCase								=  np.stack([input_mel, input_mel, input_mel, input_mel]					,		axis=0   )
														else:
																				UpperCAmelCase								=  self._np_extract_fbank_features(UpperCamelCase__					,		self.mel_filters_slaney   )[None, :]

								return input_mel, longer







		def __call__(    self   :  Optional[int]					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  Tuple = None					,		UpperCamelCase__   :  int = None					,		UpperCamelCase__   :  Union[str, Any] = None					,		UpperCamelCase__   :  List[Any] = None					,		UpperCamelCase__   :  Any = None					,		**UpperCamelCase__   :  Any					,		)					->       BatchFeature:
								'''simple docstring'''







								UpperCAmelCase								=  truncation if truncation is not None else self.truncation
								UpperCAmelCase								=  padding if padding else self.padding

								if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																				raise ValueError(
																				    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																				    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																				    F' was sampled with {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."   )

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

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

								# always return batch
								if not is_batched:
														UpperCAmelCase								=  [np.asarray(UpperCamelCase__   )]

								# convert to mel spectrogram, truncate and pad if needed.
								UpperCAmelCase								=  [
								    self._get_input_mel(UpperCamelCase__					,		max_length if max_length else self.nb_max_samples					,		UpperCamelCase__					,		UpperCamelCase__   )
								    for waveform in raw_speech
								]

								UpperCAmelCase								=  []
								UpperCAmelCase								=  []
								for mel, longer in padded_inputs:
														input_mel.append(UpperCamelCase__   )
														is_longer.append(UpperCamelCase__   )

								if truncation == "fusion" and sum(UpperCamelCase__   ) == 0:
														# if no audio is longer than 10s, then randomly select one audio to be longer
														UpperCAmelCase								=  np.random.randint(0					,		len(UpperCamelCase__   )   )
														UpperCAmelCase								=  True

								if isinstance(input_mel[0]					,		UpperCamelCase__   ):
														UpperCAmelCase								=  [np.asarray(UpperCamelCase__					,		dtype=np.floataa   ) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase								=  [[longer] for longer in is_longer]

								UpperCAmelCase								=  {"input_features": input_mel, "is_longer": is_longer}
								UpperCAmelCase								=  BatchFeature(UpperCamelCase__   )

								if return_tensors is not None:
														UpperCAmelCase								=  input_features.convert_to_tensors(UpperCamelCase__   )

								return input_features











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				DPTConfig()

							if "large" in checkpoint_url:
														snake_case_           =				10_24
														snake_case_           =				40_96
														snake_case_           =				24
														snake_case_           =				16
														snake_case_           =				[5, 11, 17, 23]
														snake_case_           =				[2_56, 5_12, 10_24, 10_24]
														snake_case_           =				(1, 3_84, 3_84)

							if "ade" in checkpoint_url:
														snake_case_           =				True

														snake_case_           =				1_50
														snake_case_           =				'huggingface/label-files'
														snake_case_           =				'ade20k-id2label.json'
														snake_case_           =				json.load(open(cached_download(hf_hub_url(a_				,	a_				,	repo_type='dataset'))				,	'r'))
														snake_case_           =				{int(a_): v for k, v in idalabel.items()}
														snake_case_           =				idalabel
														snake_case_           =				{v: k for k, v in idalabel.items()}
														snake_case_           =				[1, 1_50, 4_80, 4_80]

							return config, expected_shape





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				['pretrained.model.head.weight', 'pretrained.model.head.bias']
							for k in ignore_keys:
														state_dict.pop(a_				,	a_)





def 		__UpperCAmelCase     (	a_):
							if (
							    "pretrained.model" in name
							    and "cls_token" not in name
							    and "pos_embed" not in name
							    and "patch_embed" not in name
							):
														snake_case_           =				name.replace('pretrained.model'				,	'dpt.encoder')
							if "pretrained.model" in name:
														snake_case_           =				name.replace('pretrained.model'				,	'dpt.embeddings')
							if "patch_embed" in name:
														snake_case_           =				name.replace('patch_embed'				,	'patch_embeddings')
							if "pos_embed" in name:
														snake_case_           =				name.replace('pos_embed'				,	'position_embeddings')
							if "attn.proj" in name:
														snake_case_           =				name.replace('attn.proj'				,	'attention.output.dense')
							if "proj" in name and "project" not in name:
														snake_case_           =				name.replace('proj'				,	'projection')
							if "blocks" in name:
														snake_case_           =				name.replace('blocks'				,	'layer')
							if "mlp.fc1" in name:
														snake_case_           =				name.replace('mlp.fc1'				,	'intermediate.dense')
							if "mlp.fc2" in name:
														snake_case_           =				name.replace('mlp.fc2'				,	'output.dense')
							if "norm1" in name:
														snake_case_           =				name.replace('norm1'				,	'layernorm_before')
							if "norm2" in name:
														snake_case_           =				name.replace('norm2'				,	'layernorm_after')
							if "scratch.output_conv" in name:
														snake_case_           =				name.replace('scratch.output_conv'				,	'head')
							if "scratch" in name:
														snake_case_           =				name.replace('scratch'				,	'neck')
							if "layer1_rn" in name:
														snake_case_           =				name.replace('layer1_rn'				,	'convs.0')
							if "layer2_rn" in name:
														snake_case_           =				name.replace('layer2_rn'				,	'convs.1')
							if "layer3_rn" in name:
														snake_case_           =				name.replace('layer3_rn'				,	'convs.2')
							if "layer4_rn" in name:
														snake_case_           =				name.replace('layer4_rn'				,	'convs.3')
							if "refinenet" in name:
														snake_case_           =				int(name[len('neck.refinenet') : len('neck.refinenet') + 1])
														# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
														snake_case_           =				name.replace(f'''refinenet{layer_idx}'''				,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
							if "out_conv" in name:
														snake_case_           =				name.replace('out_conv'				,	'projection')
							if "resConfUnit1" in name:
														snake_case_           =				name.replace('resConfUnit1'				,	'residual_layer1')
							if "resConfUnit2" in name:
														snake_case_           =				name.replace('resConfUnit2'				,	'residual_layer2')
							if "conv1" in name:
														snake_case_           =				name.replace('conv1'				,	'convolution1')
							if "conv2" in name:
														snake_case_           =				name.replace('conv2'				,	'convolution2')
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess1.0.project.0'				,	'neck.reassemble_stage.readout_projects.0.0')
							if "pretrained.act_postprocess2.0.project.0" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess2.0.project.0'				,	'neck.reassemble_stage.readout_projects.1.0')
							if "pretrained.act_postprocess3.0.project.0" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess3.0.project.0'				,	'neck.reassemble_stage.readout_projects.2.0')
							if "pretrained.act_postprocess4.0.project.0" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess4.0.project.0'				,	'neck.reassemble_stage.readout_projects.3.0')
							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess1.3'				,	'neck.reassemble_stage.layers.0.projection')
							if "pretrained.act_postprocess1.4" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess1.4'				,	'neck.reassemble_stage.layers.0.resize')
							if "pretrained.act_postprocess2.3" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess2.3'				,	'neck.reassemble_stage.layers.1.projection')
							if "pretrained.act_postprocess2.4" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess2.4'				,	'neck.reassemble_stage.layers.1.resize')
							if "pretrained.act_postprocess3.3" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess3.3'				,	'neck.reassemble_stage.layers.2.projection')
							if "pretrained.act_postprocess4.3" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess4.3'				,	'neck.reassemble_stage.layers.3.projection')
							if "pretrained.act_postprocess4.4" in name:
														snake_case_           =				name.replace('pretrained.act_postprocess4.4'				,	'neck.reassemble_stage.layers.3.resize')
							if "pretrained" in name:
														snake_case_           =				name.replace('pretrained'				,	'dpt')
							if "bn" in name:
														snake_case_           =				name.replace('bn'				,	'batch_norm')
							if "head" in name:
														snake_case_           =				name.replace('head'				,	'head.head')
							if "encoder.norm" in name:
														snake_case_           =				name.replace('encoder.norm'				,	'layernorm')
							if "auxlayer" in name:
														snake_case_           =				name.replace('auxlayer'				,	'auxiliary_head.head')

							return name





def 		__UpperCAmelCase     (	a_				,	a_):
							for i in range(config.num_hidden_layers):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														snake_case_           =				state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
														snake_case_           =				state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
														# next, add query, keys and values (in that order) to the state dict
														snake_case_           =				in_proj_weight[: config.hidden_size, :]
														snake_case_           =				in_proj_bias[: config.hidden_size]
														snake_case_           =				in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														snake_case_           =				in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														snake_case_           =				in_proj_weight[
														    -config.hidden_size :, :
														]
														snake_case_           =				in_proj_bias[-config.hidden_size :]





def 		__UpperCAmelCase     (	):
							snake_case_           =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =				Image.open(requests.get(a_				,	stream=a_).raw)
							return im





@torch.no_grad()
def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_     ,						snake_case_           =				get_dpt_config(a_)
							# load original state_dict from URL
							snake_case_           =				torch.hub.load_state_dict_from_url(a_				,	map_location='cpu')
							# remove certain keys
							remove_ignore_keys_(a_)
							# rename keys
							for key in state_dict.copy().keys():
														snake_case_           =				state_dict.pop(a_)
														snake_case_           =				val
							# read in qkv matrices
							read_in_q_k_v(a_				,	a_)

							# load HuggingFace model
							snake_case_           =				DPTForSemanticSegmentation(a_) if 'ade' in checkpoint_url else DPTForDepthEstimation(a_)
							model.load_state_dict(a_)
							model.eval()

							# Check outputs on an image
							snake_case_           =				4_80 if 'ade' in checkpoint_url else 3_84
							snake_case_           =				DPTImageProcessor(size=a_)

							snake_case_           =				prepare_img()
							snake_case_           =				image_processor(a_				,	return_tensors='pt')

							# forward pass
							snake_case_           =				model(**a_).logits if 'ade' in checkpoint_url else model(**a_).predicted_depth

							# Assert logits
							snake_case_           =				torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]])
							if "ade" in checkpoint_url:
														snake_case_           =				torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]])
							assert outputs.shape == torch.Size(a_)
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]				,	a_				,	atol=1E-4)
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]				,	a_)
							)

							Path(a_).mkdir(exist_ok=a_)
							print(f'''Saving model to {pytorch_dump_folder_path}''')
							model.save_pretrained(a_)
							print(f'''Saving image processor to {pytorch_dump_folder_path}''')
							image_processor.save_pretrained(a_)

							if push_to_hub:
														print('Pushing model to hub...')
														model.push_to_hub(
														    repo_path_or_name=Path(a_				,	a_)				,	organization='nielsr'				,	commit_message='Add model'				,	use_temp_dir=a_				,	)
														image_processor.push_to_hub(
														    repo_path_or_name=Path(a_				,	a_)				,	organization='nielsr'				,	commit_message='Add image processor'				,	use_temp_dir=a_				,	)


if __name__ == "__main__":
	lowercase    				=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--checkpoint_url",
	    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
	    type=str,
	    help="URL of the original DPT checkpoint you'd like to convert.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--push_to_hub",
	    action="store_true",
	)
	parser.add_argument(
	    "--model_name",
	    default="dpt-large",
	    type=str,
	    help="Name of the model, in case you're pushing to the hub.",
	)

	lowercase    				=   parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





