



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__  :  List[str]								=					'''▁'''

A__  :  Tuple								=					get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
class 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):
							_a			      = BertGenerationTokenizer
							_a			      = False
							_a			      = True




							def    UpperCAmelCase__			(       self     :       Any):
									super().setUp()

									lowerCAmelCase_			:     Any									= BertGenerationTokenizer(A_				,				keep_accents=A_)
									tokenizer.save_pretrained(self.tmpdirname)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Optional[int]									= '''<s>'''
									lowerCAmelCase_			:     Tuple									= 1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_)				,				A_)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_)				,				A_)




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     int									= list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]				,				'''<unk>''')
									self.assertEqual(vocab_keys[1]				,				'''<s>''')
									self.assertEqual(vocab_keys[-1]				,				'''<pad>''')
									self.assertEqual(len(A_)				,				1_0_0_2)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									self.assertEqual(self.get_tokenizer().vocab_size				,				1_0_0_0)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     int									= BertGenerationTokenizer(A_				,				keep_accents=A_)

									lowerCAmelCase_			:     Optional[Any]									= tokenizer.tokenize('''This is a test''')
									self.assertListEqual(A_				,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(A_)				,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]				,				)

									lowerCAmelCase_			:     str									= tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
									self.assertListEqual(
									    A_				,				[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]				,				)
									lowerCAmelCase_			:     Optional[int]									= tokenizer.convert_tokens_to_ids(A_)
									self.assertListEqual(
									    A_				,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]				,				)

									lowerCAmelCase_			:     int									= tokenizer.convert_ids_to_tokens(A_)
									self.assertListEqual(
									    A_				,				[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]				,				)




							@cached_property
							def    UpperCAmelCase__			(       self     :       Dict):
									return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')




							@slow
							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Tuple									= '''Hello World!'''
									lowerCAmelCase_			:     Any									= [1_8_5_3_6, 2_2_6_0, 1_0_1]

									self.assertListEqual(A_				,				self.big_tokenizer.encode(A_))




							@slow
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     str									= (
									    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
									    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
									)
									lowerCAmelCase_			:     List[Any]									= [
									    8_7_1,
									    4_1_9,
									    3_5_8,
									    9_4_6,
									    9_9_1,
									    2_5_2_1,
									    4_5_2,
									    3_5_8,
									    1_3_5_7,
									    3_8_7,
									    7_7_5_1,
									    3_5_3_6,
									    1_1_2,
									    9_8_5,
									    4_5_6,
									    1_2_6,
									    8_6_5,
									    9_3_8,
									    5_4_0_0,
									    5_7_3_4,
									    4_5_8,
									    1_3_6_8,
									    4_6_7,
									    7_8_6,
									    2_4_6_2,
									    5_2_4_6,
									    1_1_5_9,
									    6_3_3,
									    8_6_5,
									    4_5_1_9,
									    4_5_7,
									    5_8_2,
									    8_5_2,
									    2_5_5_7,
									    4_2_7,
									    9_1_6,
									    5_0_8,
									    4_0_5,
									    3_4_3_2_4,
									    4_9_7,
									    3_9_1,
									    4_0_8,
									    1_1_3_4_2,
									    1_2_4_4,
									    3_8_5,
									    1_0_0,
									    9_3_8,
									    9_8_5,
									    4_5_6,
									    5_7_4,
									    3_6_2,
									    1_2_5_9_7,
									    3_2_0_0,
									    3_1_2_9,
									    1_1_7_2,
									]

									self.assertListEqual(A_				,				self.big_tokenizer.encode(A_))




							@require_torch
							@slow
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									import torch

									from transformers import BertGenerationConfig, BertGenerationEncoder

									# Build sequence
									lowerCAmelCase_			:     List[str]									= list(self.big_tokenizer.get_vocab().keys())[:1_0]
									lowerCAmelCase_			:     List[str]									= ''' '''.join(A_)
									lowerCAmelCase_			:     Dict									= self.big_tokenizer.encode_plus(A_				,				return_tensors='''pt'''				,				return_token_type_ids=A_)
									lowerCAmelCase_			:     Optional[Any]									= self.big_tokenizer.batch_encode_plus(
									    [sequence + ''' ''' + sequence]				,				return_tensors='''pt'''				,				return_token_type_ids=A_)

									lowerCAmelCase_			:     Tuple									= BertGenerationConfig()
									lowerCAmelCase_			:     str									= BertGenerationEncoder(A_)

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
											model(**A_)
											model(**A_)







							@slow
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									# fmt: off
									lowerCAmelCase_			:     str									= {'''input_ids''': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=A_				,				model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''				,				revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''				,				)










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()
A__  :  List[Any]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Any									= DPTConfig(embedding_type='''hybrid''' )

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

		if "nyu" or "midas" in checkpoint_url:
				lowerCAmelCase_			:     Union[str, Any]									= 768
				lowerCAmelCase_			:     int									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     List[str]									= [256, 512, 768, 768]
				lowerCAmelCase_			:     int									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= (1, 384, 384)
				lowerCAmelCase_			:     List[str]									= False
				lowerCAmelCase_			:     Union[str, Any]									= '''project'''

		if "ade" in checkpoint_url:
				lowerCAmelCase_			:     Any									= True
				lowerCAmelCase_			:     Any									= 768
				lowerCAmelCase_			:     Optional[int]									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     Union[str, Any]									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Any									= '''ade20k-id2label.json'''
				lowerCAmelCase_			:     str									= json.load(open(cached_download(hf_hub_url(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' ) )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     Optional[Any]									= idalabel
				lowerCAmelCase_			:     Optional[int]									= {v: k for k, v in idalabel.items()}
				lowerCAmelCase_			:     List[Any]									= [1, 150, 480, 480]

		return config, expected_shape


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     int									= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(__UpperCamelCase     ,__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		int ):
		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				lowerCAmelCase_			:     Dict									= name.replace('''pretrained.model'''     ,'''dpt.encoder''' )
		if "pretrained.model" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''pretrained.model'''     ,'''dpt.embeddings''' )
		if "patch_embed" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''patch_embed'''     ,'''''' )
		if "pos_embed" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''pos_embed'''     ,'''position_embeddings''' )
		if "attn.proj" in name:
				lowerCAmelCase_			:     Any									= name.replace('''attn.proj'''     ,'''attention.output.dense''' )
		if "proj" in name and "project" not in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''proj'''     ,'''projection''' )
		if "blocks" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''blocks'''     ,'''layer''' )
		if "mlp.fc1" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''mlp.fc1'''     ,'''intermediate.dense''' )
		if "mlp.fc2" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''mlp.fc2'''     ,'''output.dense''' )
		if "norm1" in name and "backbone" not in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''norm1'''     ,'''layernorm_before''' )
		if "norm2" in name and "backbone" not in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''norm2'''     ,'''layernorm_after''' )
		if "scratch.output_conv" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''scratch.output_conv'''     ,'''head''' )
		if "scratch" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''scratch'''     ,'''neck''' )
		if "layer1_rn" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''layer1_rn'''     ,'''convs.0''' )
		if "layer2_rn" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''layer2_rn'''     ,'''convs.1''' )
		if "layer3_rn" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''layer3_rn'''     ,'''convs.2''' )
		if "layer4_rn" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''layer4_rn'''     ,'''convs.3''' )
		if "refinenet" in name:
				lowerCAmelCase_			:     List[str]									= 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_			:     int									= name.replace('''out_conv'''     ,'''projection''' )
		if "resConfUnit1" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''resConfUnit1'''     ,'''residual_layer1''' )
		if "resConfUnit2" in name:
				lowerCAmelCase_			:     str									= name.replace('''resConfUnit2'''     ,'''residual_layer2''' )
		if "conv1" in name:
				lowerCAmelCase_			:     str									= name.replace('''conv1'''     ,'''convolution1''' )
		if "conv2" in name:
				lowerCAmelCase_			:     Optional[Any]									= 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_			:     Optional[int]									= 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_			:     Tuple									= 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_			:     Dict									= 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_			:     Dict									= 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[str]									= name.replace('''pretrained.act_postprocess2.4'''     ,'''neck.reassemble_stage.layers.1.resize''' )
		if "pretrained.act_postprocess3.3" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''pretrained.act_postprocess3.3'''     ,'''neck.reassemble_stage.layers.2.projection''' )
		if "pretrained.act_postprocess4.3" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''pretrained.act_postprocess4.3'''     ,'''neck.reassemble_stage.layers.3.projection''' )
		if "pretrained.act_postprocess4.4" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''pretrained.act_postprocess4.4'''     ,'''neck.reassemble_stage.layers.3.resize''' )
		if "pretrained" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''pretrained'''     ,'''dpt''' )
		if "bn" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''bn'''     ,'''batch_norm''' )
		if "head" in name:
				lowerCAmelCase_			:     Any									= name.replace('''head'''     ,'''head.head''' )
		if "encoder.norm" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''encoder.norm'''     ,'''layernorm''' )
		if "auxlayer" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''auxlayer'''     ,'''auxiliary_head.head''' )
		if "backbone" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''backbone'''     ,'''backbone.bit.encoder''' )

		if ".." in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''..'''     ,'''.''' )

		if "stem.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''stem.conv'''     ,'''bit.embedder.convolution''' )
		if "blocks" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''blocks'''     ,'''layers''' )
		if "convolution" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''convolution'''     ,'''conv''' )
		if "layer" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''layer'''     ,'''layers''' )
		if "backbone.bit.encoder.bit" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''backbone.bit.encoder.bit'''     ,'''backbone.bit''' )
		if "embedder.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''embedder.conv'''     ,'''embedder.convolution''' )
		if "backbone.bit.encoder.stem.norm" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''backbone.bit.encoder.stem.norm'''     ,'''backbone.bit.embedder.norm''' )
		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[str] ):
		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_			:     Dict									= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
				lowerCAmelCase_			:     Tuple									= 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_			:     str									= in_proj_weight[: config.hidden_size, :]
				lowerCAmelCase_			:     str									= in_proj_bias[: config.hidden_size]
				lowerCAmelCase_			:     Optional[Any]									= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase_			:     int									= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase_			:     Any									= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase_			:     Dict									= in_proj_bias[-config.hidden_size :]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_			:     Dict									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		return im


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

		# load HuggingFace model
		lowerCAmelCase_			:     List[Any]									= DPTForSemanticSegmentation(__UpperCamelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )
		model.eval()

		# Check outputs on an image
		lowerCAmelCase_			:     Tuple									= 480 if '''ade''' in checkpoint_url else 384
		lowerCAmelCase_			:     Optional[int]									= DPTImageProcessor(size=__UpperCamelCase )

		lowerCAmelCase_			:     Union[str, Any]									= prepare_img()
		lowerCAmelCase_			:     str									= image_processor(__UpperCamelCase     ,return_tensors='''pt''' )

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

		if show_prediction:
				lowerCAmelCase_			:     Optional[Any]									= (
				    torch.nn.functional.interpolate(
				        outputs.unsqueeze(1 )     ,size=(image.size[1], image.size[0])     ,mode='''bicubic'''     ,align_corners=__UpperCamelCase     ,)
				    .squeeze()
				    .cpu()
				    .numpy()
				)

				Image.fromarray((prediction / prediction.max()) * 255 ).show()

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

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


if __name__ == "__main__":
						A__  :  Optional[int]								=					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''',
						)

						A__  :  Union[str, 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
						)





