





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_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  DPTConfig(embedding_type='''hybrid'''			)

				if "large" in checkpoint_url:
								snake_case_						:   Optional[Any]           =  1_024
								snake_case_						:   Union[str, Any]           =  4_096
								snake_case_						:   str           =  24
								snake_case_						:   Optional[int]           =  16
								snake_case_						:   Optional[int]           =  [5, 11, 17, 23]
								snake_case_						:   Optional[int]           =  [256, 512, 1_024, 1_024]
								snake_case_						:   Any           =  (1, 384, 384)

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

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

				return config, expected_shape






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						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
				):
								snake_case_						:   int           =  name.replace('''pretrained.model'''			,    '''dpt.encoder'''			)
				if "pretrained.model" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''pretrained.model'''			,    '''dpt.embeddings'''			)
				if "patch_embed" in name:
								snake_case_						:   str           =  name.replace('''patch_embed'''			,    ''''''			)
				if "pos_embed" in name:
								snake_case_						:   Tuple           =  name.replace('''pos_embed'''			,    '''position_embeddings'''			)
				if "attn.proj" in name:
								snake_case_						:   Dict           =  name.replace('''attn.proj'''			,    '''attention.output.dense'''			)
				if "proj" in name and "project" not in name:
								snake_case_						:   int           =  name.replace('''proj'''			,    '''projection'''			)
				if "blocks" in name:
								snake_case_						:   Tuple           =  name.replace('''blocks'''			,    '''layer'''			)
				if "mlp.fc1" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''mlp.fc1'''			,    '''intermediate.dense'''			)
				if "mlp.fc2" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''mlp.fc2'''			,    '''output.dense'''			)
				if "norm1" in name and "backbone" not in name:
								snake_case_						:   Tuple           =  name.replace('''norm1'''			,    '''layernorm_before'''			)
				if "norm2" in name and "backbone" not in name:
								snake_case_						:   Optional[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_						:   Optional[Any]           =  name.replace('''scratch'''			,    '''neck'''			)
				if "layer1_rn" in name:
								snake_case_						:   int           =  name.replace('''layer1_rn'''			,    '''convs.0'''			)
				if "layer2_rn" in name:
								snake_case_						:   Any           =  name.replace('''layer2_rn'''			,    '''convs.1'''			)
				if "layer3_rn" in name:
								snake_case_						:   str           =  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_						:   Any           =  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_						:   Union[str, Any]           =  name.replace('''out_conv'''			,    '''projection'''			)
				if "resConfUnit1" in name:
								snake_case_						:   Dict           =  name.replace('''resConfUnit1'''			,    '''residual_layer1'''			)
				if "resConfUnit2" in name:
								snake_case_						:   Optional[int]           =  name.replace('''resConfUnit2'''			,    '''residual_layer2'''			)
				if "conv1" in name:
								snake_case_						:   Any           =  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_						:   Union[str, Any]           =  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_						:   Dict           =  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_						:   Any           =  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_						:   List[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_						:   Union[str, Any]           =  name.replace('''pretrained.act_postprocess1.3'''			,    '''neck.reassemble_stage.layers.0.projection'''			)
				if "pretrained.act_postprocess1.4" in name:
								snake_case_						:   Optional[int]           =  name.replace('''pretrained.act_postprocess1.4'''			,    '''neck.reassemble_stage.layers.0.resize'''			)
				if "pretrained.act_postprocess2.3" in name:
								snake_case_						:   str           =  name.replace('''pretrained.act_postprocess2.3'''			,    '''neck.reassemble_stage.layers.1.projection'''			)
				if "pretrained.act_postprocess2.4" in name:
								snake_case_						:   str           =  name.replace('''pretrained.act_postprocess2.4'''			,    '''neck.reassemble_stage.layers.1.resize'''			)
				if "pretrained.act_postprocess3.3" in name:
								snake_case_						:   int           =  name.replace('''pretrained.act_postprocess3.3'''			,    '''neck.reassemble_stage.layers.2.projection'''			)
				if "pretrained.act_postprocess4.3" in name:
								snake_case_						:   List[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_						:   Dict           =  name.replace('''pretrained'''			,    '''dpt'''			)
				if "bn" in name:
								snake_case_						:   List[Any]           =  name.replace('''bn'''			,    '''batch_norm'''			)
				if "head" in name:
								snake_case_						:   List[Any]           =  name.replace('''head'''			,    '''head.head'''			)
				if "encoder.norm" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''encoder.norm'''			,    '''layernorm'''			)
				if "auxlayer" in name:
								snake_case_						:   int           =  name.replace('''auxlayer'''			,    '''auxiliary_head.head'''			)
				if "backbone" in name:
								snake_case_						:   Dict           =  name.replace('''backbone'''			,    '''backbone.bit.encoder'''			)

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						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)
								snake_case_						:   int           =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
								snake_case_						:   List[Any]           =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
								# next, add query, keys and values (in that order) to the state dict
								snake_case_						:   int           =  in_proj_weight[: config.hidden_size, :]
								snake_case_						:   List[Any]           =  in_proj_bias[: config.hidden_size]
								snake_case_						:   List[Any]           =  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case_						:   Optional[Any]           =  in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								snake_case_						:   Optional[int]           =  in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case_						:   int           =  in_proj_bias[-config.hidden_size :]






def 					lowerCamelCase_						(    )		->						str:






				"""simple docstring"""


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






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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

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

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

				snake_case_						:   Optional[Any]           =  prepare_img()
				snake_case_						:   Any           =  image_processor(_UpperCamelCase			,    return_tensors='''pt'''			)

				# forward pass
				snake_case_						:   Any           =  model(**_UpperCamelCase			).logits if '''ade''' in checkpoint_url else model(**_UpperCamelCase			).predicted_depth

				if show_prediction:
								snake_case_						:   List[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__":
	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
	)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
