






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __lowercase					(__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  LongformerTokenizer
	_lowerCamelCase              =  True
	_lowerCamelCase              =  LongformerTokenizerFast
	_lowerCamelCase              =  True

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			super().setUp()

			# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
			UpperCamelCase__				:					List[str]					=					[
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    '\u0120',
			    '\u0120l',
			    '\u0120n',
			    '\u0120lo',
			    '\u0120low',
			    'er',
			    '\u0120lowest',
			    '\u0120newer',
			    '\u0120wider',
			    '<unk>',
			]
			UpperCamelCase__				:					Optional[int]					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					str					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
			UpperCamelCase__				:					List[str]					=					{'unk_token': '<unk>'}

			UpperCamelCase__				:					Optional[Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					str					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file'])
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write(json.dumps(UpperCAmelCase_) + '\n')
			with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write('\n'.join(UpperCAmelCase_))

	def      __UpperCamelCase							(      self   :						int  ,					**UpperCAmelCase_   :						int):
			kwargs.update(self.special_tokens_map)
			return self.tokenizer_class.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Dict):
			kwargs.update(self.special_tokens_map)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					List[Any]					=					'lower newer'
			UpperCamelCase__				:					str					=					'lower newer'
			return input_text, output_text

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.tokenizer_class(self.vocab_file  ,					self.merges_file  ,					**self.special_tokens_map)
			UpperCamelCase__				:					List[Any]					=					'lower newer'
			UpperCamelCase__				:					Optional[int]					=					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
			UpperCamelCase__				:					int					=					tokenizer.tokenize(UpperCAmelCase_)  # , add_prefix_space=True)
			self.assertListEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Optional[int]					=					tokens + [tokenizer.unk_token]
			UpperCamelCase__				:					Tuple					=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					List[Any]					=					self.get_tokenizer()

			self.assertListEqual(tokenizer.encode('Hello world!'  ,					add_special_tokens=UpperCAmelCase_)  ,					[0, 31_414, 232, 328, 2])
			self.assertListEqual(
			    tokenizer.encode('Hello world! cécé herlolip 418'  ,					add_special_tokens=UpperCAmelCase_)  ,					[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]  ,					)

	@slow
	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[str]					=					self.tokenizer_class.from_pretrained('allenai/longformer-base-4096')

			UpperCamelCase__				:					str					=					tokenizer.encode('sequence builders'  ,					add_special_tokens=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					tokenizer.encode('multi-sequence build'  ,					add_special_tokens=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					tokenizer.encode(
			    'sequence builders'  ,					add_special_tokens=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					tokenizer.encode(
			    'sequence builders'  ,					'multi-sequence build'  ,					add_special_tokens=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
			UpperCamelCase__				:					List[Any]					=					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_  ,					UpperCAmelCase_)

			assert encoded_sentence == encoded_text_from_decode
			assert encoded_pair == encoded_pair_from_decode

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Tuple					=					self.get_tokenizer()

			UpperCamelCase__				:					Union[str, Any]					=					'Encode this sequence.'
			UpperCamelCase__				:					Union[str, Any]					=					tokenizer.byte_encoder[' '.encode('utf-8')[0]]

			# Testing encoder arguments
			UpperCamelCase__				:					List[Any]					=					tokenizer.encode(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
			self.assertNotEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					tokenizer.encode(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
			self.assertEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

			tokenizer.add_special_tokens({'bos_token': '<s>'})
			UpperCamelCase__				:					Dict					=					tokenizer.encode(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					tokenizer.convert_ids_to_tokens(encoded[1])[0]
			self.assertNotEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

			# Testing spaces after special tokens
			UpperCamelCase__				:					Tuple					=					'<mask>'
			tokenizer.add_special_tokens(
			    {'mask_token': AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_)})  # mask token has a left space
			UpperCamelCase__				:					Any					=					tokenizer.convert_tokens_to_ids(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					'Encode <mask> sequence'
			UpperCamelCase__				:					Optional[Any]					=					'Encode <mask>sequence'

			UpperCamelCase__				:					List[str]					=					tokenizer.encode(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					encoded.index(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
			self.assertEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					tokenizer.encode(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					encoded.index(UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
			self.assertNotEqual(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			pass

	def      __UpperCamelCase							(      self   :						Optional[int]):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
							UpperCamelCase__				:					Optional[Any]					=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_  ,					**UpperCAmelCase_)
							UpperCamelCase__				:					List[str]					=					self.tokenizer_class.from_pretrained(UpperCAmelCase_  ,					**UpperCAmelCase_)
							UpperCamelCase__				:					Union[str, Any]					=					'A, <mask> AllenNLP sentence.'
							UpperCamelCase__				:					Any					=					tokenizer_r.encode_plus(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_token_type_ids=UpperCAmelCase_)
							UpperCamelCase__				:					List[Any]					=					tokenizer_p.encode_plus(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_token_type_ids=UpperCAmelCase_)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])  ,					sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])  ,					sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])  ,					)

							UpperCamelCase__				:					List[str]					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							UpperCamelCase__				:					Optional[Any]					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']  ,					[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']  ,					[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

							self.assertSequenceEqual(
							    UpperCAmelCase_  ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    UpperCAmelCase_  ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])

	def      __UpperCamelCase							(      self   :						Tuple):
			for trim_offsets, add_prefix_space in itertools.product([True, False]  ,					repeat=2):
					UpperCamelCase__				:					Any					=					self.rust_tokenizer_class.from_pretrained(
					    self.tmpdirname  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)

					UpperCamelCase__				:					int					=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
					UpperCamelCase__				:					Optional[int]					=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

					self.assertEqual(pre_tokenizer_state['add_prefix_space']  ,					UpperCAmelCase_)

					self.assertEqual(post_processor_state['add_prefix_space']  ,					UpperCAmelCase_)
					self.assertEqual(post_processor_state['trim_offsets']  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
			# `trim_offsets`
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
							UpperCamelCase__				:					Optional[int]					=					'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
							UpperCamelCase__				:					str					=					F'{text_of_1_token} {text_of_1_token}'

							UpperCamelCase__				:					Optional[Any]					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Any					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(len(UpperCAmelCase_) + 1, len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					Any					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					str					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(len(UpperCAmelCase_) + 1, len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					Any					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Union[str, Any]					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(len(UpperCAmelCase_), len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					Optional[int]					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Optional[int]					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(len(UpperCAmelCase_), len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					Dict					=					F' {text}'

							# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
							#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
							# )
							# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
							# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
							# self.assertEqual(
							#     encoding.offset_mapping[1],
							#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
							# )

							UpperCamelCase__				:					int					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Any					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(1, 1 + len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(1 + len(UpperCAmelCase_) + 1, 1 + len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					List[str]					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Dict					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, 1 + len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(1 + len(UpperCAmelCase_), 1 + len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)

							UpperCamelCase__				:					List[Any]					=					self.rust_tokenizer_class.from_pretrained(
							    UpperCAmelCase_  ,					use_fast=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					trim_offsets=UpperCAmelCase_)
							UpperCamelCase__				:					Optional[int]					=					tokenizer_r(UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_)
							self.assertEqual(encoding.offset_mapping[0]  ,					(0, 1 + len(UpperCAmelCase_)))
							self.assertEqual(
							    encoding.offset_mapping[1]  ,					(1 + len(UpperCAmelCase_), 1 + len(UpperCAmelCase_) + 1 + len(UpperCAmelCase_))  ,					)












'''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__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		UpperCamelCase__				:					Dict					=					DPTConfig()

		if "large" in checkpoint_url:
				UpperCamelCase__				:					List[str]					=					1_024
				UpperCamelCase__				:					List[str]					=					4_096
				UpperCamelCase__				:					Optional[int]					=					24
				UpperCamelCase__				:					List[str]					=					16
				UpperCamelCase__				:					List[str]					=					[5, 11, 17, 23]
				UpperCamelCase__				:					str					=					[256, 512, 1_024, 1_024]
				UpperCamelCase__				:					Union[str, Any]					=					(1, 384, 384)

		if "ade" in checkpoint_url:
				UpperCamelCase__				:					int					=					True

				UpperCamelCase__				:					Optional[Any]					=					150
				UpperCamelCase__				:					int					=					'huggingface/label-files'
				UpperCamelCase__				:					List[Any]					=					'ade20k-id2label.json'
				UpperCamelCase__				:					List[Any]					=					json.load(open(cached_download(hf_hub_url(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset'))     ,      'r'))
				UpperCamelCase__				:					int					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}
				UpperCamelCase__				:					Union[str, Any]					=					idalabel
				UpperCamelCase__				:					List[str]					=					{v: k for k, v in idalabel.items()}
				UpperCamelCase__				:					Any					=					[1, 150, 480, 480]

		return config, expected_shape







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_     ,      lowerCamelCase_)







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

		return name







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







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					List[Any]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__,	UpperCamelCase__				:					Any					=					get_dpt_config(lowerCamelCase_)
		# load original state_dict from URL
		UpperCamelCase__				:					Tuple					=					torch.hub.load_state_dict_from_url(lowerCamelCase_     ,      map_location='cpu')
		# remove certain keys
		remove_ignore_keys_(lowerCamelCase_)
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase__				:					str					=					state_dict.pop(lowerCamelCase_)
				UpperCamelCase__				:					List[str]					=					val
		# read in qkv matrices
		read_in_q_k_v(lowerCamelCase_     ,      lowerCamelCase_)

		# load HuggingFace model
		UpperCamelCase__				:					str					=					DPTForSemanticSegmentation(lowerCamelCase_) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_)
		model.load_state_dict(lowerCamelCase_)
		model.eval()

		# Check outputs on an image
		UpperCamelCase__				:					Any					=					480 if 'ade' in checkpoint_url else 384
		UpperCamelCase__				:					List[Any]					=					DPTImageProcessor(size=lowerCamelCase_)

		UpperCamelCase__				:					int					=					prepare_img()
		UpperCamelCase__				:					Optional[Any]					=					image_processor(lowerCamelCase_     ,      return_tensors='pt')

		# forward pass
		UpperCamelCase__				:					Any					=					model(**lowerCamelCase_).logits if 'ade' in checkpoint_url else model(**lowerCamelCase_).predicted_depth

		# Assert logits
		UpperCamelCase__				:					Tuple					=					torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]])
		if "ade" in checkpoint_url:
				UpperCamelCase__				:					List[str]					=					torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]])
		assert outputs.shape == torch.Size(lowerCamelCase_)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]     ,      lowerCamelCase_     ,      atol=1e-4)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]     ,      lowerCamelCase_)
		)

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


if __name__ == "__main__":
		lowerCAmelCase__          =						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.',
		)

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




