



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__			= get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =XLMRobertaTokenizer
							__lowerCamelCase	:	Union[str, Any]    =XLMRobertaTokenizerFast
							__lowerCamelCase	:	Optional[int]    =True
							__lowerCamelCase	:	int    =True





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											super().setUp()

											# We have a SentencePiece fixture for testing
											__a       =		XLMRobertaTokenizer(__lowercase      ,				keep_accents=__lowercase	)
											tokenizer.save_pretrained(self.tmpdirname	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		"""<pad>"""
											__a       =		1

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





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]      ,				"""<s>"""	)
											self.assertEqual(vocab_keys[1]      ,				"""<pad>"""	)
											self.assertEqual(vocab_keys[-1]      ,				"""<mask>"""	)
											self.assertEqual(len(__lowercase	)      ,				1002	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											self.assertEqual(self.get_tokenizer().vocab_size      ,				1002	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		XLMRobertaTokenizer(__lowercase      ,				keep_accents=__lowercase	)

											__a       =		tokenizer.tokenize("""This is a test"""	)
											self.assertListEqual(__lowercase      ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(__lowercase	)      ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,				)

											__a       =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
											self.assertListEqual(
											    __lowercase      ,				[
											        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""",
											        """.""",
											    ]      ,				)
											__a       =		tokenizer.convert_tokens_to_ids(__lowercase	)
											self.assertListEqual(
											    __lowercase      ,				[
											        value + tokenizer.fairseq_offset
											        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
											        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
											    ]      ,				)

											__a       =		tokenizer.convert_ids_to_tokens(__lowercase	)
											self.assertListEqual(
											    __lowercase      ,				[
											        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>""",
											        """.""",
											    ]      ,				)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											if not self.test_slow_tokenizer:
															# as we don't have a slow version, we can't compare the outputs between slow and fast versions
															return

											__a       =		(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
																			__a       =		self.rust_tokenizer_class.from_pretrained(__lowercase      ,				**__lowercase	)
																			__a       =		self.tokenizer_class.from_pretrained(__lowercase      ,				**__lowercase	)

																			__a       =		tempfile.mkdtemp()

																			__a       =		tokenizer_r.save_pretrained(__lowercase	)
																			__a       =		tokenizer_p.save_pretrained(__lowercase	)

																			# Checks it save with the same files + the tokenizer.json file for the fast one
																			self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files	)	)
																			__a       =		tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f	)
																			self.assertSequenceEqual(__lowercase      ,				__lowercase	)

																			# Checks everything loads correctly in the same way
																			__a       =		tokenizer_r.from_pretrained(__lowercase	)
																			__a       =		tokenizer_p.from_pretrained(__lowercase	)

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																							self.assertTrue(hasattr(__lowercase      ,				__lowercase	)	)
																							# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																							# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(__lowercase	)

																			# Save tokenizer rust, legacy_format=True
																			__a       =		tempfile.mkdtemp()

																			__a       =		tokenizer_r.save_pretrained(__lowercase      ,				legacy_format=__lowercase	)
																			__a       =		tokenizer_p.save_pretrained(__lowercase	)

																			# Checks it save with the same files
																			self.assertSequenceEqual(__lowercase      ,				__lowercase	)

																			# Checks everything loads correctly in the same way
																			__a       =		tokenizer_r.from_pretrained(__lowercase	)
																			__a       =		tokenizer_p.from_pretrained(__lowercase	)

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																							self.assertTrue(hasattr(__lowercase      ,				__lowercase	)	)

																			shutil.rmtree(__lowercase	)

																			# Save tokenizer rust, legacy_format=False
																			__a       =		tempfile.mkdtemp()

																			__a       =		tokenizer_r.save_pretrained(__lowercase      ,				legacy_format=__lowercase	)
																			__a       =		tokenizer_p.save_pretrained(__lowercase	)

																			# Checks it saved the tokenizer.json file
																			self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files	)	)

																			# Checks everything loads correctly in the same way
																			__a       =		tokenizer_r.from_pretrained(__lowercase	)
																			__a       =		tokenizer_p.from_pretrained(__lowercase	)

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																							self.assertTrue(hasattr(__lowercase      ,				__lowercase	)	)

																			shutil.rmtree(__lowercase	)





							@cached_property
							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(__lowercase      ,				f.name	)
															__a       =		XLMRobertaTokenizer(f.name      ,				keep_accents=__lowercase	)
															__a       =		pickle.dumps(__lowercase	)
											pickle.loads(__lowercase	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											if not self.test_rust_tokenizer:
															return

											__a       =		self.get_tokenizer()
											__a       =		self.get_rust_tokenizer()

											__a       =		"""I was born in 92000, and this is falsé."""

											__a       =		tokenizer.tokenize(__lowercase	)
											__a       =		rust_tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		self.get_rust_tokenizer()
											__a       =		tokenizer.encode(__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)





							@slow
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		"""Hello World!"""
											__a       =		[0, 35378, 6661, 38, 2]
											# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
											# xlmr.eval()
											# xlmr.encode(symbols)

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





							@slow
							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		(
											    """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"""
											)
											__a       =		[
											    0,
											    3293,
											    83,
											    10,
											    4552,
											    4989,
											    7986,
											    678,
											    10,
											    5915,
											    111,
											    179459,
											    124850,
											    4,
											    6044,
											    237,
											    12,
											    6,
											    5,
											    6,
											    4,
											    6780,
											    705,
											    15,
											    1388,
											    44,
											    378,
											    10114,
											    711,
											    152,
											    20,
											    6,
											    5,
											    22376,
											    642,
											    1221,
											    15190,
											    34153,
											    450,
											    5608,
											    959,
											    1119,
											    57702,
											    136,
											    186,
											    47,
											    1098,
											    29367,
											    47,
											    # 4426, # What fairseq tokenizes from "<unk>": "_<"
											    # 3678, # What fairseq tokenizes from "<unk>": "unk"
											    # 2740, # What fairseq tokenizes from "<unk>": ">"
											    3,  # What we tokenize from "<unk>": "<unk>"
											    6,  # Residue from the tokenization: an extra sentencepiece underline
											    4,
											    6044,
											    237,
											    6284,
											    50901,
											    528,
											    31,
											    90,
											    34,
											    927,
											    2,
											]
											# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
											# xlmr.eval()
											# xlmr.encode(symbols)

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







							@slow
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											# fmt: off
											__a       =		{"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], """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, 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, 0, 0, 0, 0, 0], [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, 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=__lowercase      ,				model_name="""xlm-roberta-base"""      ,				revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""      ,				)





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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				__a       =		DPTConfig(embedding_type="""hybrid"""    )

				if "large" in checkpoint_url:
								__a       =		1024
								__a       =		4096
								__a       =		24
								__a       =		16
								__a       =		[5, 11, 17, 23]
								__a       =		[256, 512, 1024, 1024]
								__a       =		(1, 384, 384)

				if "nyu" or "midas" in checkpoint_url:
								__a       =		768
								__a       =		[1, 1, 1, 0.5]
								__a       =		[256, 512, 768, 768]
								__a       =		150
								__a       =		16
								__a       =		(1, 384, 384)
								__a       =		False
								__a       =		"""project"""

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

				return config, expected_shape

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__a       =		["""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       :			Tuple    ):
				"""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
				):
								__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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[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)
								__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   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a       =		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       :			int				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a     ,					__a       =		get_dpt_config(_SCREAMING_SNAKE_CASE    )
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				__a       =		torch.load(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )
				# remove certain keys
				remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
				# rename keys
				for key in state_dict.copy().keys():
								__a       =		state_dict.pop(_SCREAMING_SNAKE_CASE    )
								__a       =		val
				# read in qkv matrices
				read_in_q_k_v(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# load HuggingFace model
				__a       =		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
				__a       =		480 if """ade""" in checkpoint_url else 384
				__a       =		DPTImageProcessor(size=_SCREAMING_SNAKE_CASE    )

				__a       =		prepare_img()
				__a       =		image_processor(_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""    )

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

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

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

				if pytorch_dump_folder_path is not None:
								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:
								model.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )
								image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""    )


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