





'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


__lowerCAmelCase							:     Union[str, Any]		     =random.Random()





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]=1.0						,	_lowerCamelCase : Optional[int]=None						,	_lowerCamelCase : List[Any]=None	):
				if rng is None:
								A__    =      global_rng

				A__    =      []
				for batch_idx in range(shape[0]	):
								values.append([]	)
								for _ in range(shape[1]	):
												values[-1].append(rng.random() * scale	)

				return values






@require_torch
class        UpperCAmelCase     (	unittest.TestCase    ):
	def __init__(    self				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Tuple=7						,	lowercase_				:Optional[int]=4_00						,	lowercase_				:List[Any]=20_00						,	lowercase_				:List[str]=1						,	lowercase_				:Any=0.0						,	lowercase_				:List[str]=1_60_00						,	lowercase_				:Union[str, Any]=True						,	lowercase_				:List[str]=80						,	lowercase_				:List[Any]=16						,	lowercase_				:Optional[int]=64						,	lowercase_				:List[str]="hann_window"						,	lowercase_				:str=80						,	lowercase_				:Union[str, Any]=76_00						,	lowercase_				:Optional[Any]=1E-10						,	lowercase_				:Any=True						,	)->							Any:
					A__    =      parent
					A__    =      batch_size
					A__    =      min_seq_length
					A__    =      max_seq_length
					A__    =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A__    =      feature_size
					A__    =      padding_value
					A__    =      sampling_rate
					A__    =      do_normalize
					A__    =      num_mel_bins
					A__    =      hop_length
					A__    =      win_length
					A__    =      win_function
					A__    =      fmin
					A__    =      fmax
					A__    =      mel_floor
					A__    =      return_attention_mask
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[str]:
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "do_normalize": self.do_normalize,
					    "num_mel_bins": self.num_mel_bins,
					    "hop_length": self.hop_length,
					    "win_length": self.win_length,
					    "win_function": self.win_function,
					    "fmin": self.fmin,
					    "fmax": self.fmax,
					    "mel_floor": self.mel_floor,
					    "return_attention_mask": self.return_attention_mask,
					}
	def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:List[Any]=False						,	lowercase_				:List[str]=False							)->							Dict:
					def _flatten(lowercase_				:Optional[Any]							):
									return list(itertools.chain(*lowercase_							)							)

					if equal_length:
									A__    =      floats_list((self.batch_size, self.max_seq_length)							)
					else:
									# make sure that inputs increase in size
									A__    =      [
									    _flatten(floats_list((x, self.feature_size)							)							)
									    for x in range(self.min_seq_length						,	self.max_seq_length						,	self.seq_length_diff							)
									]

					if numpify:
									A__    =      [np.asarray(lowercase_							) for x in speech_inputs]

					return speech_inputs



	def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:List[Any]=False						,	lowercase_				:Any=False							)->							List[str]:
					if equal_length:
									A__    =      [floats_list((self.max_seq_length, self.num_mel_bins)							) for _ in range(self.batch_size							)]
					else:
									# make sure that inputs increase in size
									A__    =      [
									    floats_list((x, self.num_mel_bins)							)
									    for x in range(self.min_seq_length						,	self.max_seq_length						,	self.seq_length_diff							)
									]

					if numpify:
									A__    =      [np.asarray(lowercase_							) for x in speech_inputs]

					return speech_inputs






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
	__lowercase       		=    SpeechTaFeatureExtractor
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
					A__    =      SpeechTaFeatureExtractionTester(self							)
	def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Tuple							)->							Dict:
					self.assertTrue(np.all(np.mean(lowercase_						,	axis=0							) < 1E-3							)							)
					self.assertTrue(np.all(np.abs(np.var(lowercase_						,	axis=0							) - 1							) < 1E-3							)							)
	def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Union[str, Any]:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					# create three inputs of length 800, 1000, and 1200
					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]
					A__    =      [np.asarray(lowercase_							) for speech_input in speech_inputs]

					# Test not batched input
					A__    =      feat_extract(speech_inputs[0]						,	return_tensors="np"							).input_values
					A__    =      feat_extract(np_speech_inputs[0]						,	return_tensors="np"							).input_values
					self.assertTrue(np.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)

					# Test batched
					A__    =      feat_extract(lowercase_						,	return_tensors="np"							).input_values
					A__    =      feat_extract(lowercase_						,	return_tensors="np"							).input_values
					for enc_seq_a, enc_seq_a in zip(lowercase_						,	lowercase_							):
									self.assertTrue(np.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)
	def 					UpperCAmelCase_							(    self				:int							)->							List[Any]:
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]

					A__    =      ['''longest''', '''max_length''', '''do_not_pad''']
					A__    =      [None, 16_00, None]
					for max_length, padding in zip(lowercase_						,	lowercase_							):
									A__    =      feat_extract(lowercase_						,	padding=lowercase_						,	max_length=lowercase_						,	return_tensors="np"							)
									A__    =      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]							)
									self.assertTrue(input_values[0][8_00:].sum() < 1E-6							)
									self._check_zero_mean_unit_variance(input_values[1][:10_00]							)
									self.assertTrue(input_values[0][10_00:].sum() < 1E-6							)
									self._check_zero_mean_unit_variance(input_values[2][:12_00]							)
	def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					A__    =      range(8_00						,	14_00						,	2_00							)
					A__    =      [floats_list((1, x)							)[0] for x in lengths]

					A__    =      ['''longest''', '''max_length''', '''do_not_pad''']
					A__    =      [None, 16_00, None]

					for max_length, padding in zip(lowercase_						,	lowercase_							):
									A__    =      feat_extract(lowercase_						,	max_length=lowercase_						,	padding=lowercase_							)
									A__    =      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]							)
									self._check_zero_mean_unit_variance(input_values[1][:10_00]							)
									self._check_zero_mean_unit_variance(input_values[2][:12_00]							)
	def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]
					A__    =      feat_extract(
					    lowercase_						,	truncation=lowercase_						,	max_length=10_00						,	padding="max_length"						,	return_tensors="np"							)
					A__    =      processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
					self._check_zero_mean_unit_variance(input_values[1]							)
					self._check_zero_mean_unit_variance(input_values[2]							)
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]
					A__    =      feat_extract(
					    lowercase_						,	truncation=lowercase_						,	max_length=10_00						,	padding="longest"						,	return_tensors="np"							)
					A__    =      processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]							)
					self._check_zero_mean_unit_variance(input_values[2]							)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertTrue(input_values.shape == (3, 10_00)							)

					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]
					A__    =      feat_extract(
					    lowercase_						,	truncation=lowercase_						,	max_length=20_00						,	padding="longest"						,	return_tensors="np"							)
					A__    =      processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]							)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]							)
					self._check_zero_mean_unit_variance(input_values[2]							)

					# make sure that if max_length > longest -> then pad to longest
					self.assertTrue(input_values.shape == (3, 12_00)							)
	def 					UpperCAmelCase_							(    self				:str							)->							Optional[Any]:
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					A__    =      np.random.rand(1_00							).astype(np.floataa							)
					A__    =      np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
									A__    =      feature_extractor.pad([{"input_values": inputs}]						,	return_tensors="np"							)
									self.assertTrue(np_processed.input_values.dtype == np.floataa							)
									A__    =      feature_extractor.pad([{"input_values": inputs}]						,	return_tensors="pt"							)
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)
	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[Any]:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A__    =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
					# create three inputs of length 800, 1000, and 1200
					A__    =      [floats_list((1, x)							)[0] for x in range(8_00						,	14_00						,	2_00							)]
					A__    =      [np.asarray(lowercase_							) for speech_input in speech_inputs]

					# Test feature size
					A__    =      feature_extractor(audio_target=lowercase_						,	padding=lowercase_						,	return_tensors="np"							).input_values
					self.assertTrue(input_values.ndim == 3							)
					self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins							)

					# Test not batched input
					A__    =      feature_extractor(speech_inputs[0]						,	return_tensors="np"							).input_values
					A__    =      feature_extractor(np_speech_inputs[0]						,	return_tensors="np"							).input_values
					self.assertTrue(np.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)

					# Test batched
					A__    =      feature_extractor(lowercase_						,	return_tensors="np"							).input_values
					A__    =      feature_extractor(lowercase_						,	return_tensors="np"							).input_values
					for enc_seq_a, enc_seq_a in zip(lowercase_						,	lowercase_							):
									self.assertTrue(np.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)

					# Test 2-D numpy arrays are batched.
					A__    =      [floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
					A__    =      np.asarray(lowercase_							)
					A__    =      feature_extractor(lowercase_						,	return_tensors="np"							).input_values
					A__    =      feature_extractor(lowercase_						,	return_tensors="np"							).input_values
					for enc_seq_a, enc_seq_a in zip(lowercase_						,	lowercase_							):
									self.assertTrue(np.allclose(lowercase_						,	lowercase_						,	atol=1E-3							)							)
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
					A__    =      self.feat_extract_tester.prepare_inputs_for_target()
					A__    =      self.feature_extraction_class(**self.feat_extract_dict							)
					A__    =      feat_extract.model_input_names[0]

					A__    =      BatchFeature({input_name: speech_inputs}							)

					self.assertTrue(all(len(lowercase_							) == len(lowercase_							) for x, y in zip(lowercase_						,	processed_features[input_name]							)							)							)

					A__    =      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_							)
					A__    =      BatchFeature({input_name: speech_inputs}						,	tensor_type="np"							)

					A__    =      processed_features[input_name]

					if len(batch_features_input.shape							) < 3:
									A__    =      batch_features_input[:, :, None]

					self.assertTrue(
					    batch_features_input.shape
					    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)
	@require_torch
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
					A__    =      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_							)
					A__    =      self.feature_extraction_class(**self.feat_extract_dict							)
					A__    =      feat_extract.model_input_names[0]

					A__    =      BatchFeature({input_name: speech_inputs}						,	tensor_type="pt"							)

					A__    =      processed_features[input_name]

					if len(batch_features_input.shape							) < 3:
									A__    =      batch_features_input[:, :, None]

					self.assertTrue(
					    batch_features_input.shape
					    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)
	@require_torch
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Dict:
					A__    =      self.feature_extraction_class(**self.feat_extract_dict							)
					A__    =      self.feat_extract_tester.prepare_inputs_for_target()
					A__    =      feat_extract.model_input_names[0]

					A__    =      BatchFeature({input_name: speech_inputs}							)

					A__    =      feat_extract.num_mel_bins  # hack!

					A__    =      feat_extract.pad(lowercase_						,	padding="longest"						,	return_tensors="np"							)[input_name]
					A__    =      feat_extract.pad(lowercase_						,	padding="longest"						,	return_tensors="pt"							)[input_name]

					self.assertTrue(abs(input_np.astype(np.floataa							).sum() - input_pt.numpy().astype(np.floataa							).sum()							) < 1E-2							)
	def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
					A__    =      self.feat_extract_dict
					A__    =      True
					A__    =      self.feature_extraction_class(**lowercase_							)
					A__    =      self.feat_extract_tester.prepare_inputs_for_target()
					A__    =      [len(lowercase_							) for x in speech_inputs]
					A__    =      feat_extract.model_input_names[0]

					A__    =      BatchFeature({input_name: speech_inputs}							)

					A__    =      feat_extract.num_mel_bins  # hack!

					A__    =      feat_extract.pad(lowercase_						,	padding="longest"						,	return_tensors="np"							)
					self.assertIn("attention_mask"						,	lowercase_							)
					self.assertListEqual(list(processed.attention_mask.shape							)						,	list(processed[input_name].shape[:2]							)							)
					self.assertListEqual(processed.attention_mask.sum(-1							).tolist()						,	lowercase_							)
	def 					UpperCAmelCase_							(    self				:Tuple							)->							Optional[int]:
					A__    =      self.feat_extract_dict
					A__    =      True
					A__    =      self.feature_extraction_class(**lowercase_							)
					A__    =      self.feat_extract_tester.prepare_inputs_for_target()
					A__    =      [len(lowercase_							) for x in speech_inputs]
					A__    =      feat_extract.model_input_names[0]

					A__    =      BatchFeature({input_name: speech_inputs}							)
					A__    =      min(lowercase_							)

					A__    =      feat_extract.num_mel_bins  # hack!

					A__    =      feat_extract.pad(
					    lowercase_						,	padding="max_length"						,	max_length=lowercase_						,	truncation=lowercase_						,	return_tensors="np"							)
					self.assertIn("attention_mask"						,	lowercase_							)
					self.assertListEqual(
					    list(processed_pad.attention_mask.shape							)						,	[processed_pad[input_name].shape[0], max_length]							)
					self.assertListEqual(
					    processed_pad.attention_mask[:, :max_length].sum(-1							).tolist()						,	[max_length for x in speech_inputs]							)
	def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:List[Any]							)->							int:
					from datasets import load_dataset

					A__    =      load_dataset("hf-internal-testing/librispeech_asr_dummy"						,	"clean"						,	split="validation"							)
					# automatic decoding with librispeech
					A__    =      ds.sort("id"							).select(range(lowercase_							)							)[:num_samples]['''audio''']

					return [x["array"] for x in speech_samples]
	def 					UpperCAmelCase_							(    self				:int							)->							Dict:
					# fmt: off
					A__    =      torch.tensor(
					    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
					     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
					     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
					     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
					     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
					     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]							)
					# fmt: on

					A__    =      self._load_datasamples(1							)
					A__    =      SpeechTaFeatureExtractor()
					A__    =      feature_extractor(lowercase_						,	return_tensors="pt"							).input_values
					self.assertEquals(input_values.shape						,	(1, 9_36_80)							)
					self.assertTrue(torch.allclose(input_values[0, :30]						,	lowercase_						,	atol=1E-6							)							)



	def 					UpperCAmelCase_							(    self				:Dict							)->							Dict:
					# fmt: off
					A__    =      torch.tensor(
					    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
					     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
					     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
					     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]							)
					# fmt: on

					A__    =      self._load_datasamples(1							)
					A__    =      SpeechTaFeatureExtractor()
					A__    =      feature_extractor(audio_target=lowercase_						,	return_tensors="pt"							).input_values
					self.assertEquals(input_values.shape						,	(1, 3_66, 80)							)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]						,	lowercase_						,	atol=1E-4							)							)










'''simple docstring'''

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()
__lowerCAmelCase							:     Tuple		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : Tuple	):
    A__    =      DPTConfig(embedding_type="hybrid"	)

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

    if "nyu" or "midas" in checkpoint_url:
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      [2_56, 5_12, 7_68, 7_68]
        A__    =      1_50
        A__    =      16
        A__    =      (1, 3_84, 3_84)
        A__    =      False
        A__    =      "project"

    if "ade" in checkpoint_url:
        A__    =      True
        A__    =      7_68
        A__    =      [1, 1, 1, 0.5]
        A__    =      1_50
        A__    =      16
        A__    =      "huggingface/label-files"
        A__    =      "ade20k-id2label.json"
        A__    =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)	)						,	"r"	)	)
        A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}
        A__    =      idalabel
        A__    =      {v: k for k, v in idalabel.items()}
        A__    =      [1, 1_50, 4_80, 4_80]

    return config, expected_shape





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    A__    =      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        A__    =      name.replace("pretrained.model"						,	"dpt.encoder"	)
    if "pretrained.model" in name:
        A__    =      name.replace("pretrained.model"						,	"dpt.embeddings"	)
    if "patch_embed" in name:
        A__    =      name.replace("patch_embed"						,	""	)
    if "pos_embed" in name:
        A__    =      name.replace("pos_embed"						,	"position_embeddings"	)
    if "attn.proj" in name:
        A__    =      name.replace("attn.proj"						,	"attention.output.dense"	)
    if "proj" in name and "project" not in name:
        A__    =      name.replace("proj"						,	"projection"	)
    if "blocks" in name:
        A__    =      name.replace("blocks"						,	"layer"	)
    if "mlp.fc1" in name:
        A__    =      name.replace("mlp.fc1"						,	"intermediate.dense"	)
    if "mlp.fc2" in name:
        A__    =      name.replace("mlp.fc2"						,	"output.dense"	)
    if "norm1" in name and "backbone" not in name:
        A__    =      name.replace("norm1"						,	"layernorm_before"	)
    if "norm2" in name and "backbone" not in name:
        A__    =      name.replace("norm2"						,	"layernorm_after"	)
    if "scratch.output_conv" in name:
        A__    =      name.replace("scratch.output_conv"						,	"head"	)
    if "scratch" in name:
        A__    =      name.replace("scratch"						,	"neck"	)
    if "layer1_rn" in name:
        A__    =      name.replace("layer1_rn"						,	"convs.0"	)
    if "layer2_rn" in name:
        A__    =      name.replace("layer2_rn"						,	"convs.1"	)
    if "layer3_rn" in name:
        A__    =      name.replace("layer3_rn"						,	"convs.2"	)
    if "layer4_rn" in name:
        A__    =      name.replace("layer4_rn"						,	"convs.3"	)
    if "refinenet" in name:
        A__    =      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
        A__    =      name.replace(F"refinenet{layer_idx}"						,	F"fusion_stage.layers.{abs(layer_idx-4	)}"	)
    if "out_conv" in name:
        A__    =      name.replace("out_conv"						,	"projection"	)
    if "resConfUnit1" in name:
        A__    =      name.replace("resConfUnit1"						,	"residual_layer1"	)
    if "resConfUnit2" in name:
        A__    =      name.replace("resConfUnit2"						,	"residual_layer2"	)
    if "conv1" in name:
        A__    =      name.replace("conv1"						,	"convolution1"	)
    if "conv2" in name:
        A__    =      name.replace("conv2"						,	"convolution2"	)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        A__    =      name.replace("pretrained.act_postprocess1.0.project.0"						,	"neck.reassemble_stage.readout_projects.0.0"	)
    if "pretrained.act_postprocess2.0.project.0" in name:
        A__    =      name.replace("pretrained.act_postprocess2.0.project.0"						,	"neck.reassemble_stage.readout_projects.1.0"	)
    if "pretrained.act_postprocess3.0.project.0" in name:
        A__    =      name.replace("pretrained.act_postprocess3.0.project.0"						,	"neck.reassemble_stage.readout_projects.2.0"	)
    if "pretrained.act_postprocess4.0.project.0" in name:
        A__    =      name.replace("pretrained.act_postprocess4.0.project.0"						,	"neck.reassemble_stage.readout_projects.3.0"	)

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        A__    =      name.replace("pretrained.act_postprocess1.3"						,	"neck.reassemble_stage.layers.0.projection"	)
    if "pretrained.act_postprocess1.4" in name:
        A__    =      name.replace("pretrained.act_postprocess1.4"						,	"neck.reassemble_stage.layers.0.resize"	)
    if "pretrained.act_postprocess2.3" in name:
        A__    =      name.replace("pretrained.act_postprocess2.3"						,	"neck.reassemble_stage.layers.1.projection"	)
    if "pretrained.act_postprocess2.4" in name:
        A__    =      name.replace("pretrained.act_postprocess2.4"						,	"neck.reassemble_stage.layers.1.resize"	)
    if "pretrained.act_postprocess3.3" in name:
        A__    =      name.replace("pretrained.act_postprocess3.3"						,	"neck.reassemble_stage.layers.2.projection"	)
    if "pretrained.act_postprocess4.3" in name:
        A__    =      name.replace("pretrained.act_postprocess4.3"						,	"neck.reassemble_stage.layers.3.projection"	)
    if "pretrained.act_postprocess4.4" in name:
        A__    =      name.replace("pretrained.act_postprocess4.4"						,	"neck.reassemble_stage.layers.3.resize"	)
    if "pretrained" in name:
        A__    =      name.replace("pretrained"						,	"dpt"	)
    if "bn" in name:
        A__    =      name.replace("bn"						,	"batch_norm"	)
    if "head" in name:
        A__    =      name.replace("head"						,	"head.head"	)
    if "encoder.norm" in name:
        A__    =      name.replace("encoder.norm"						,	"layernorm"	)
    if "auxlayer" in name:
        A__    =      name.replace("auxlayer"						,	"auxiliary_head.head"	)
    if "backbone" in name:
        A__    =      name.replace("backbone"						,	"backbone.bit.encoder"	)

    if ".." in name:
        A__    =      name.replace(".."						,	"."	)

    if "stem.conv" in name:
        A__    =      name.replace("stem.conv"						,	"bit.embedder.convolution"	)
    if "blocks" in name:
        A__    =      name.replace("blocks"						,	"layers"	)
    if "convolution" in name and "backbone" in name:
        A__    =      name.replace("convolution"						,	"conv"	)
    if "layer" in name and "backbone" in name:
        A__    =      name.replace("layer"						,	"layers"	)
    if "backbone.bit.encoder.bit" in name:
        A__    =      name.replace("backbone.bit.encoder.bit"						,	"backbone.bit"	)
    if "embedder.conv" in name:
        A__    =      name.replace("embedder.conv"						,	"embedder.convolution"	)
    if "backbone.bit.encoder.stem.norm" in name:
        A__    =      name.replace("backbone.bit.encoder.stem.norm"						,	"backbone.bit.embedder.norm"	)
    return name





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





def 						UpperCamelCase    (							):
    A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__    =      Image.open(requests.get(_lowerCamelCase						,	stream=_lowerCamelCase	).raw	)
    return im





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Any						,	_lowerCamelCase : str	):
    A__,     A__    =      get_dpt_config(_lowerCamelCase	)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    # remove certain keys
    remove_ignore_keys_(_lowerCamelCase	)
    # rename keys
    for key in state_dict.copy().keys():
        A__    =      state_dict.pop(_lowerCamelCase	)
        A__    =      val
    # read in qkv matrices
    read_in_q_k_v(_lowerCamelCase						,	_lowerCamelCase	)

    # load HuggingFace model
    A__    =      DPTForSemanticSegmentation(_lowerCamelCase	) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase	)
    model.load_state_dict(_lowerCamelCase	)
    model.eval()

    # Check outputs on an image
    A__    =      4_80 if "ade" in checkpoint_url else 3_84
    A__    =      DPTImageProcessor(size=_lowerCamelCase	)

    A__    =      prepare_img()
    A__    =      image_processor(_lowerCamelCase						,	return_tensors="pt"	)

    # forward pass
    A__    =      model(**_lowerCamelCase	).logits if "ade" in checkpoint_url else model(**_lowerCamelCase	).predicted_depth

    if show_prediction:
        A__    =      (
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1	)						,	size=(image.size[1], image.size[0])						,	mode="bicubic"						,	align_corners=_lowerCamelCase						,	)
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 2_55	).show()

    if pytorch_dump_folder_path is not None:
        Path(_lowerCamelCase	).mkdir(exist_ok=_lowerCamelCase	)
        print(F"Saving model to {pytorch_dump_folder_path}"	)
        model.save_pretrained(_lowerCamelCase	)
        print(F"Saving image processor to {pytorch_dump_folder_path}"	)
        image_processor.save_pretrained(_lowerCamelCase	)

    if push_to_hub:
        model.push_to_hub("ybelkada/dpt-hybrid-midas"	)
        image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Tuple		     =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=False,
      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.",
  )
  parser.add_argument(
      "--show_prediction",
      action="store_true",
  )

  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )



