





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()
_SCREAMING_SNAKE_CASE					=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Union[str, Any]											=	DPTConfig()

	if "large" in checkpoint_url:
		snake_case_		:			int											=	10_24
		snake_case_		:			Dict											=	40_96
		snake_case_		:			Optional[int]											=	24
		snake_case_		:			Optional[Any]											=	16
		snake_case_		:			Dict											=	[5, 11, 17, 23]
		snake_case_		:			Union[str, Any]											=	[2_56, 5_12, 10_24, 10_24]
		snake_case_		:			Union[str, Any]											=	(1, 3_84, 3_84)

	if "ade" in checkpoint_url:
		snake_case_		:			Tuple											=	True

		snake_case_		:			int											=	1_50
		snake_case_		:			Union[str, Any]											=	"""huggingface/label-files"""
		snake_case_		:			List[str]											=	"""ade20k-id2label.json"""
		snake_case_		:			Tuple											=	json.load(open(cached_download(hf_hub_url(__a   , __a   , repo_type='dataset'   )   )   , 'r'   )   )
		snake_case_		:			int											=	{int(__a   ): v for k, v in idalabel.items()}
		snake_case_		:			Optional[Any]											=	idalabel
		snake_case_		:			Dict											=	{v: k for k, v in idalabel.items()}
		snake_case_		:			Dict											=	[1, 1_50, 4_80, 4_80]

	return config, expected_shape


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Optional[Any]											=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(__a   , __a   )


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

	return name


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


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			str											=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	snake_case_		:			Optional[int]											=	Image.open(requests.get(__a   , stream=__a   ).raw   )
	return im


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

	# load HuggingFace model
	snake_case_		:			List[Any]											=	DPTForSemanticSegmentation(__a   ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__a   )
	model.load_state_dict(__a   )
	model.eval()

	# Check outputs on an image
	snake_case_		:			Dict											=	4_80 if """ade""" in checkpoint_url else 3_84
	snake_case_		:			List[str]											=	DPTImageProcessor(size=__a   )

	snake_case_		:			int											=	prepare_img()
	snake_case_		:			Union[str, Any]											=	image_processor(__a   , return_tensors='pt'   )

	# forward pass
	snake_case_		:			List[str]											=	model(**__a   ).logits if """ade""" in checkpoint_url else model(**__a   ).predicted_depth

	# Assert logits
	snake_case_		:			Optional[Any]											=	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   )
	if "ade" in checkpoint_url:
		snake_case_		:			int											=	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   )
	assert outputs.shape == torch.Size(__a   )
	assert (
	    torch.allclose(outputs[0, 0, :3, :3]   , __a   , atol=1E-4   )
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3]   , __a   )
	)

	Path(__a   ).mkdir(exist_ok=__a   )
	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(__a   )
	print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(__a   )

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE					=		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.""",
						)

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








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	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      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

