


'''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()
__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				DPTConfig()

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

					if "ade" in checkpoint_url:
										UpperCamelCase__     :Union[str, Any]							      =				True

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

					return config, expected_shape


def     a   (    __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(__a				,      __a     )


def     a   (    __a     )						->			List[str]:



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

					return name


def     a   (    __a				,      __a     )						->			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)
										UpperCamelCase__     :Optional[int]							      =				state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''     )
										UpperCamelCase__     :Optional[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
										UpperCamelCase__     :Optional[int]							      =				in_proj_weight[: config.hidden_size, :]
										UpperCamelCase__     :str							      =				in_proj_bias[: config.hidden_size]
										UpperCamelCase__     :List[str]							      =				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										UpperCamelCase__     :Optional[int]							      =				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										UpperCamelCase__     :str							      =				in_proj_weight[
										    -config.hidden_size :, :
										]
										UpperCamelCase__     :List[Any]							      =				in_proj_bias[-config.hidden_size :]


def     a   (    )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__     :Any							      =				Image.open(requests.get(__a				,      stream=__a     ).raw     )
					return im


@torch.no_grad()
def     a   (    __a				,      __a				,      __a				,      __a     )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :Union[str, Any]							      =				get_dpt_config(__a     )
					# load original state_dict from URL
					UpperCamelCase__     :List[str]							      =				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():
										UpperCamelCase__     :Union[str, Any]							      =				state_dict.pop(__a     )
										UpperCamelCase__     :Dict							      =				val
					# read in qkv matrices
					read_in_q_k_v(__a				,      __a     )

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

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

					UpperCamelCase__     :str							      =				prepare_img()
					UpperCamelCase__     :int							      =				image_processor(__a				,      return_tensors='''pt'''     )

					# forward pass
					UpperCamelCase__     :Optional[int]							      =				model(**__a     ).logits if '''ade''' in checkpoint_url else model(**__a     ).predicted_depth

					# Assert logits
					UpperCamelCase__     :str							      =				torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]     )
					if "ade" in checkpoint_url:
										UpperCamelCase__     :Dict							      =				torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]     )
					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__":
		__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.''',
		)

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



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'transfo-xl'
		_a									=       ['mems']
		_a									=       {
		    'n_token': 'vocab_size',
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}




		def __init__(	self					,     UpperCamelCase_=267735					,     UpperCamelCase_=[20000, 40000, 200000]					,     UpperCamelCase_=1024					,     UpperCamelCase_=1024					,     UpperCamelCase_=16					,     UpperCamelCase_=64					,     UpperCamelCase_=4096					,     UpperCamelCase_=4					,     UpperCamelCase_=False					,     UpperCamelCase_=18					,     UpperCamelCase_=1600					,     UpperCamelCase_=1000					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=0					,     UpperCamelCase_=-1					,     UpperCamelCase_=True					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=True					,     UpperCamelCase_="normal"					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=0					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				vocab_size
							UpperCamelCase__     :Optional[int]							      =				[]
							self.cutoffs.extend(UpperCamelCase_							)
							if proj_share_all_but_first:
												UpperCamelCase__     :Any							      =				[False] + [True] * len(self.cutoffs							)
							else:
												UpperCamelCase__     :Dict							      =				[False] + [False] * len(self.cutoffs							)
							UpperCamelCase__     :Union[str, Any]							      =				d_model
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Any							      =				d_head
							UpperCamelCase__     :Tuple							      =				d_inner
							UpperCamelCase__     :int							      =				div_val
							UpperCamelCase__     :int							      =				pre_lnorm
							UpperCamelCase__     :Any							      =				n_layer
							UpperCamelCase__     :int							      =				n_head
							UpperCamelCase__     :Tuple							      =				mem_len
							UpperCamelCase__     :List[str]							      =				same_length
							UpperCamelCase__     :Any							      =				attn_type
							UpperCamelCase__     :List[str]							      =				clamp_len
							UpperCamelCase__     :Union[str, Any]							      =				sample_softmax
							UpperCamelCase__     :List[Any]							      =				adaptive
							UpperCamelCase__     :List[Any]							      =				dropout
							UpperCamelCase__     :Optional[Any]							      =				dropatt
							UpperCamelCase__     :str							      =				untie_r
							UpperCamelCase__     :List[Any]							      =				init
							UpperCamelCase__     :Optional[Any]							      =				init_range
							UpperCamelCase__     :int							      =				proj_init_std
							UpperCamelCase__     :List[str]							      =				init_std
							UpperCamelCase__     :Optional[int]							      =				layer_norm_epsilon
							super().__init__(eos_token_id=UpperCamelCase_					,     **UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)
							return -1




		@max_position_embeddings.setter
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)