





"""simple docstring"""





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		GPTaTokenizer
				__snake_case   :       int	 =		GPTaTokenizerFast
				__snake_case   :       Optional[Any]	 =		True
				__snake_case   :       Optional[Any]	 =		{"""add_prefix_space""": True}
				__snake_case   :       Union[str, Any]	 =		False




				def 					__lowercase  (     self  :Dict			):
											super().setUp()

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

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




				def 					__lowercase  (     self  :List[Any]						,    **__lowercase  :str			):
											kwargs.update(self.special_tokens_map			)
											return GPTaTokenizer.from_pretrained(self.tmpdirname						,    **__lowercase			)




				def 					__lowercase  (     self  :Tuple						,    **__lowercase  :Union[str, Any]			):
											kwargs.update(self.special_tokens_map			)
											return GPTaTokenizerFast.from_pretrained(self.tmpdirname						,    **__lowercase			)




				def 					__lowercase  (     self  :str						,    __lowercase  :List[Any]			):
											__lowerCamelCase      :      Optional[Any]         ='''lower newer'''
											__lowerCamelCase      :      Optional[int]         ='''lower newer'''
											return input_text, output_text




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =GPTaTokenizer(self.vocab_file						,    self.merges_file						,    **self.special_tokens_map			)
											__lowerCamelCase      :      Optional[int]         ='''lower newer'''
											__lowerCamelCase      :      Any         =['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
											__lowerCamelCase      :      Optional[int]         =tokenizer.tokenize(__lowercase						,    add_prefix_space=__lowercase			)
											self.assertListEqual(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      List[str]         =tokens + [tokenizer.unk_token]
											__lowerCamelCase      :      Any         =[14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase			)						,    __lowercase			)




				def 					__lowercase  (     self  :str			):

											if not self.test_rust_tokenizer:
																		return

											__lowerCamelCase      :      List[Any]         =self.get_tokenizer()
											__lowerCamelCase      :      List[Any]         =self.get_rust_tokenizer(add_prefix_space=__lowercase			)

											__lowerCamelCase      :      Union[str, Any]         ='''lower newer'''

											# Testing tokenization
											__lowerCamelCase      :      str         =tokenizer.tokenize(__lowercase						,    add_prefix_space=__lowercase			)
											__lowerCamelCase      :      int         =rust_tokenizer.tokenize(__lowercase			)
											self.assertListEqual(__lowercase						,    __lowercase			)

											# Testing conversion to ids without special tokens
											__lowerCamelCase      :      Optional[Any]         =tokenizer.encode(__lowercase						,    add_special_tokens=__lowercase						,    add_prefix_space=__lowercase			)
											__lowerCamelCase      :      str         =rust_tokenizer.encode(__lowercase						,    add_special_tokens=__lowercase			)
											self.assertListEqual(__lowercase						,    __lowercase			)

											# Testing conversion to ids with special tokens
											__lowerCamelCase      :      Tuple         =self.get_rust_tokenizer(add_prefix_space=__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =tokenizer.encode(__lowercase						,    add_prefix_space=__lowercase			)
											__lowerCamelCase      :      Dict         =rust_tokenizer.encode(__lowercase			)
											self.assertListEqual(__lowercase						,    __lowercase			)

											# Testing the unknown token
											__lowerCamelCase      :      Tuple         =tokens + [rust_tokenizer.unk_token]
											__lowerCamelCase      :      Dict         =[14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase			)						,    __lowercase			)




				def 					__lowercase  (     self  :Tuple						,    *__lowercase  :Union[str, Any]						,    **__lowercase  :Tuple			):
											pass




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :Tuple=15			):

											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
																									__lowerCamelCase      :      Dict         =self.rust_tokenizer_class.from_pretrained(__lowercase						,    **__lowercase			)

																									# Simple input
																									__lowerCamelCase      :      Any         ='''This is a simple input'''
																									__lowerCamelCase      :      List[Any]         =['''This is a simple input 1''', '''This is a simple input 2''']
																									__lowerCamelCase      :      Any         =('''This is a simple input''', '''This is a pair''')
																									__lowerCamelCase      :      Optional[Any]         =[
																									    ('''This is a simple input 1''', '''This is a simple input 2'''),
																									    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																									]

																									# Simple input tests
																									self.assertRaises(__lowercase						,    tokenizer_r.encode						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''			)

																									# Simple input
																									self.assertRaises(__lowercase						,    tokenizer_r.encode_plus						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''			)

																									# Simple input
																									self.assertRaises(
																									    __lowercase						,    tokenizer_r.batch_encode_plus						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''						,    )

																									# Pair input
																									self.assertRaises(__lowercase						,    tokenizer_r.encode						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''			)

																									# Pair input
																									self.assertRaises(__lowercase						,    tokenizer_r.encode_plus						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''			)

																									# Pair input
																									self.assertRaises(
																									    __lowercase						,    tokenizer_r.batch_encode_plus						,    __lowercase						,    max_length=__lowercase						,    padding='''max_length'''						,    )




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Tuple         =GPTaTokenizer.from_pretrained(self.tmpdirname						,    pad_token='''<pad>'''			)

											# Simple input
											__lowerCamelCase      :      Dict         ='''This is a simple input'''
											__lowerCamelCase      :      str         =['''This is a simple input looooooooong''', '''This is a simple input''']
											__lowerCamelCase      :      Tuple         =('''This is a simple input''', '''This is a pair''')
											__lowerCamelCase      :      Union[str, Any]         =[
											    ('''This is a simple input loooooong''', '''This is a simple input'''),
											    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
											]

											__lowerCamelCase      :      Tuple         =tokenizer.pad_token_id

											__lowerCamelCase      :      Any         =tokenizer(__lowercase						,    padding='''max_length'''						,    max_length=30						,    return_tensors='''np'''			)
											__lowerCamelCase      :      str         =tokenizer(__lowercase						,    padding=__lowercase						,    truncate=__lowercase						,    return_tensors='''np'''			)
											__lowerCamelCase      :      Union[str, Any]         =tokenizer(*__lowercase						,    padding='''max_length'''						,    max_length=60						,    return_tensors='''np'''			)
											__lowerCamelCase      :      List[str]         =tokenizer(__lowercase						,    padding=__lowercase						,    truncate=__lowercase						,    return_tensors='''np'''			)

											# s
											# test single string max_length padding
											self.assertEqual(out_s['''input_ids'''].shape[-1]						,    30			)
											self.assertTrue(pad_token_id in out_s['''input_ids''']			)
											self.assertTrue(0 in out_s['''attention_mask''']			)

											# s2
											# test automatic padding
											self.assertEqual(out_sa['''input_ids'''].shape[-1]						,    33			)
											# long slice doesn't have padding
											self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]			)
											self.assertFalse(0 in out_sa['''attention_mask'''][0]			)
											# short slice does have padding
											self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]			)
											self.assertTrue(0 in out_sa['''attention_mask'''][1]			)

											# p
											# test single pair max_length padding
											self.assertEqual(out_p['''input_ids'''].shape[-1]						,    60			)
											self.assertTrue(pad_token_id in out_p['''input_ids''']			)
											self.assertTrue(0 in out_p['''attention_mask''']			)

											# p2
											# test automatic padding pair
											self.assertEqual(out_pa['''input_ids'''].shape[-1]						,    52			)
											# long slice pair doesn't have padding
											self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]			)
											self.assertFalse(0 in out_pa['''attention_mask'''][0]			)
											# short slice pair does have padding
											self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]			)
											self.assertTrue(0 in out_pa['''attention_mask'''][1]			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Tuple         ='''$$$'''
											__lowerCamelCase      :      Optional[Any]         =GPTaTokenizer.from_pretrained(self.tmpdirname						,    bos_token=__lowercase						,    add_bos_token=__lowercase			)

											__lowerCamelCase      :      Tuple         ='''This is a simple input'''
											__lowerCamelCase      :      List[str]         =['''This is a simple input 1''', '''This is a simple input 2''']

											__lowerCamelCase      :      Tuple         =tokenizer.bos_token_id

											__lowerCamelCase      :      int         =tokenizer(__lowercase			)
											__lowerCamelCase      :      int         =tokenizer(__lowercase			)

											self.assertEqual(out_s.input_ids[0]						,    __lowercase			)
											self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids			)			)

											__lowerCamelCase      :      List[Any]         =tokenizer.decode(out_s.input_ids			)
											__lowerCamelCase      :      int         =tokenizer.batch_decode(out_sa.input_ids			)

											self.assertEqual(decode_s.split()[0]						,    __lowercase			)
											self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa			)			)




				def 					__lowercase  (     self  :Dict			):
											pass




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =[self.get_tokenizer(do_lower_case=__lowercase						,    add_bos_token=__lowercase			)]
											for tokenizer in tokenizers:
																		with self.subTest(f'{tokenizer.__class__.__name__}'			):
																									__lowerCamelCase      :      Tuple         ='''Encode this.'''
																									__lowerCamelCase      :      int         ='''This one too please.'''
																									__lowerCamelCase      :      Tuple         =tokenizer.encode(__lowercase						,    add_special_tokens=__lowercase			)
																									encoded_sequence += tokenizer.encode(__lowercase						,    add_special_tokens=__lowercase			)
																									__lowerCamelCase      :      Any         =tokenizer.encode_plus(
																									    __lowercase						,    __lowercase						,    add_special_tokens=__lowercase						,    return_special_tokens_mask=__lowercase						,    )
																									__lowerCamelCase      :      List[Any]         =encoded_sequence_dict['''input_ids''']
																									__lowerCamelCase      :      Tuple         =encoded_sequence_dict['''special_tokens_mask''']
																									self.assertEqual(len(__lowercase			)						,    len(__lowercase			)			)

																									__lowerCamelCase      :      List[Any]         =[
																									    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowercase			)
																									]
																									__lowerCamelCase      :      Dict         =[x for x in filtered_sequence if x is not None]
																									self.assertEqual(__lowercase						,    __lowercase			)

@require_tokenizers
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      List[str]         =AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,    from_slow=__lowercase			)
											__lowerCamelCase      :      str         ='''A photo of a cat'''

											__lowerCamelCase      :      List[Any]         =tokenizer.encode(
											    __lowercase						,    )
											self.assertEqual(__lowercase						,    [2, 250, 1345, 9, 10, 4758]			)
											tokenizer.save_pretrained('''test_opt'''			)

											__lowerCamelCase      :      Dict         =AutoTokenizer.from_pretrained('''./test_opt'''			)
											__lowerCamelCase      :      str         =tokenizer.encode(
											    __lowercase						,    )
											self.assertEqual(__lowercase						,    [2, 250, 1345, 9, 10, 4758]			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Dict         =AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,    use_slow=__lowercase			)
											__lowerCamelCase      :      Optional[Any]         ='''A photo of a cat'''

											__lowerCamelCase      :      int         =tokenizer.encode(
											    __lowercase						,    )
											# Same as above
											self.assertEqual(__lowercase						,    [2, 250, 1345, 9, 10, 4758]			)




				@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''			)
				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Optional[Any]         =AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,    from_slow=__lowercase			)

											__lowerCamelCase      :      List[str]         ='''bos'''
											__lowerCamelCase      :      Any         =tokenizer.get_vocab()['''bos''']

											__lowerCamelCase      :      int         ='''A photo of a cat'''
											__lowerCamelCase      :      Optional[Any]         =tokenizer.encode(
											    __lowercase						,    )
											# We changed the bos token
											self.assertEqual(__lowercase						,    [3_1957, 250, 1345, 9, 10, 4758]			)
											tokenizer.save_pretrained('''./tok'''			)
											__lowerCamelCase      :      List[str]         =AutoTokenizer.from_pretrained('''./tok'''			)
											self.assertTrue(tokenizer.is_fast			)
											__lowerCamelCase      :      List[Any]         =tokenizer.encode(
											    __lowercase						,    )
											self.assertEqual(__lowercase						,    [3_1957, 250, 1345, 9, 10, 4758]			)











"""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()
_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =DPTConfig()

							if "large" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =1024
														__lowerCamelCase      :      Dict         =4096
														__lowerCamelCase      :      Tuple         =24
														__lowerCamelCase      :      str         =16
														__lowerCamelCase      :      Optional[Any]         =[5, 11, 17, 23]
														__lowerCamelCase      :      List[Any]         =[256, 512, 1024, 1024]
														__lowerCamelCase      :      List[str]         =(1, 384, 384)

							if "ade" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         =True

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

							return config, expected_shape



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







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

							return name



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							for i in range(config.num_hidden_layers   ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__lowerCamelCase      :      int         =state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'   )
														__lowerCamelCase      :      Union[str, 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
														__lowerCamelCase      :      Tuple         =in_proj_weight[: config.hidden_size, :]
														__lowerCamelCase      :      List[str]         =in_proj_bias[: config.hidden_size]
														__lowerCamelCase      :      Optional[Any]         =in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowerCamelCase      :      Dict         =in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__lowerCamelCase      :      List[Any]         =in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowerCamelCase      :      Union[str, Any]         =in_proj_bias[-config.hidden_size :]



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase      :      Optional[Any]         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =get_dpt_config(SCREAMING_SNAKE_CASE   )
							# load original state_dict from URL
							__lowerCamelCase      :      List[str]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''   )
							# remove certain keys
							remove_ignore_keys_(SCREAMING_SNAKE_CASE   )
							# rename keys
							for key in state_dict.copy().keys():
														__lowerCamelCase      :      str         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      Any         =val
							# read in qkv matrices
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE   ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE   )
							model.load_state_dict(SCREAMING_SNAKE_CASE   )
							model.eval()

							# Check outputs on an image
							__lowerCamelCase      :      Union[str, Any]         =480 if '''ade''' in checkpoint_url else 384
							__lowerCamelCase      :      Dict         =DPTImageProcessor(size=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[int]         =prepare_img()
							__lowerCamelCase      :      int         =image_processor(SCREAMING_SNAKE_CASE    ,						return_tensors='''pt'''   )

							# forward pass
							__lowerCamelCase      :      Tuple         =model(**SCREAMING_SNAKE_CASE   ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE   ).predicted_depth

							# Assert logits
							__lowerCamelCase      :      Optional[Any]         =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:
														__lowerCamelCase      :      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(SCREAMING_SNAKE_CASE   )
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-4   )
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]    ,						SCREAMING_SNAKE_CASE   )
							)

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

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


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

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




