

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()
lowercase        =  logging.get_logger(__name__)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    List[Any]:
							lowerCamelCase_							  =					DPTConfig(embedding_type="""hybrid"""       )

							if "large" in checkpoint_url:
														lowerCamelCase_							  =					1_0_2_4
														lowerCamelCase_							  =					4_0_9_6
														lowerCamelCase_							  =					2_4
														lowerCamelCase_							  =					1_6
														lowerCamelCase_							  =					[5, 1_1, 1_7, 2_3]
														lowerCamelCase_							  =					[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
														lowerCamelCase_							  =					(1, 3_8_4, 3_8_4)

							if "nyu" or "midas" in checkpoint_url:
														lowerCamelCase_							  =					7_6_8
														lowerCamelCase_							  =					[1, 1, 1, 0.5]
														lowerCamelCase_							  =					[2_5_6, 5_1_2, 7_6_8, 7_6_8]
														lowerCamelCase_							  =					1_5_0
														lowerCamelCase_							  =					1_6
														lowerCamelCase_							  =					(1, 3_8_4, 3_8_4)
														lowerCamelCase_							  =					False
														lowerCamelCase_							  =					"project"

							if "ade" in checkpoint_url:
														lowerCamelCase_							  =					True
														lowerCamelCase_							  =					7_6_8
														lowerCamelCase_							  =					[1, 1, 1, 0.5]
														lowerCamelCase_							  =					1_5_0
														lowerCamelCase_							  =					1_6
														lowerCamelCase_							  =					"huggingface/label-files"
														lowerCamelCase_							  =					"ade20k-id2label.json"
														lowerCamelCase_							  =					json.load(open(cached_download(hf_hub_url(_A			,				_A			,				repo_type="""dataset"""       )       )			,				"""r"""       )       )
														lowerCamelCase_							  =					{int(_A       ): v for k, v in idalabel.items()}
														lowerCamelCase_							  =					idalabel
														lowerCamelCase_							  =					{v: k for k, v in idalabel.items()}
														lowerCamelCase_							  =					[1, 1_5_0, 4_8_0, 4_8_0]

							return config, expected_shape



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]       )  ->    int:
							lowerCamelCase_							  =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
							for k in ignore_keys:
														state_dict.pop(_A			,				_A       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]       )  ->    str:




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

							if ".." in name:
														lowerCamelCase_							  =					name.replace(""".."""			,				"""."""       )

							if "stem.conv" in name:
														lowerCamelCase_							  =					name.replace("""stem.conv"""			,				"""bit.embedder.convolution"""       )
							if "blocks" in name:
														lowerCamelCase_							  =					name.replace("""blocks"""			,				"""layers"""       )
							if "convolution" in name and "backbone" in name:
														lowerCamelCase_							  =					name.replace("""convolution"""			,				"""conv"""       )
							if "layer" in name and "backbone" in name:
														lowerCamelCase_							  =					name.replace("""layer"""			,				"""layers"""       )
							if "backbone.bit.encoder.bit" in name:
														lowerCamelCase_							  =					name.replace("""backbone.bit.encoder.bit"""			,				"""backbone.bit"""       )
							if "embedder.conv" in name:
														lowerCamelCase_							  =					name.replace("""embedder.conv"""			,				"""embedder.convolution"""       )
							if "backbone.bit.encoder.stem.norm" in name:
														lowerCamelCase_							  =					name.replace("""backbone.bit.encoder.stem.norm"""			,				"""backbone.bit.embedder.norm"""       )
							return name



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Dict       )  ->    Tuple:




							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_							  =					state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
														lowerCamelCase_							  =					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_							  =					in_proj_weight[: config.hidden_size, :]
														lowerCamelCase_							  =					in_proj_bias[: config.hidden_size]
														lowerCamelCase_							  =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														lowerCamelCase_							  =					in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														lowerCamelCase_							  =					in_proj_weight[
														    -config.hidden_size :, :
														]
														lowerCamelCase_							  =					in_proj_bias[-config.hidden_size :]



def      __lowerCAmelCase						(							)  ->    Any:
							lowerCamelCase_							  =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							lowerCamelCase_							  =					Image.open(requests.get(_A			,				stream=_A       ).raw       )
							return im



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[str]       )  ->    Tuple:
							lowerCamelCase_							  =					get_dpt_config(_A       )
							# load original state_dict from URL
							# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
							lowerCamelCase_							  =					torch.load(_A			,				map_location="""cpu"""       )
							# remove certain keys
							remove_ignore_keys_(_A       )
							# rename keys
							for key in state_dict.copy().keys():
														lowerCamelCase_							  =					state_dict.pop(_A       )
														lowerCamelCase_							  =					val
							# read in qkv matrices
							read_in_q_k_v(_A			,				_A       )

							# load HuggingFace model
							lowerCamelCase_							  =					DPTForSemanticSegmentation(_A       ) if "ade" in checkpoint_url else DPTForDepthEstimation(_A       )
							model.load_state_dict(_A       )
							model.eval()

							# Check outputs on an image
							lowerCamelCase_							  =					4_8_0 if "ade" in checkpoint_url else 3_8_4
							lowerCamelCase_							  =					DPTImageProcessor(size=_A       )

							lowerCamelCase_							  =					prepare_img()
							lowerCamelCase_							  =					image_processor(_A			,				return_tensors="""pt"""       )

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

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

														Image.fromarray((prediction / prediction.max()) * 2_5_5       ).show()

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


if __name__ == "__main__":
		lowercase        =  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''',
		)

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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase        =  {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

lowercase        =  {'''mobilebert-uncased''': 5_1_2}


lowercase        =  {}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    MobileBertTokenizer

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Any="[UNK]"   ,					__UpperCamelCase					:     str="[SEP]"   ,					__UpperCamelCase					:     Dict="[PAD]"   ,					__UpperCamelCase					:     List[str]="[CLS]"   ,					__UpperCamelCase					:     Any="[MASK]"   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     int=None   ,					**__UpperCamelCase					:     Dict   ,					):
												super().__init__(
												    __UpperCamelCase   ,					tokenizer_file=__UpperCamelCase   ,					do_lower_case=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					sep_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					cls_token=__UpperCamelCase   ,					mask_token=__UpperCamelCase   ,					tokenize_chinese_chars=__UpperCamelCase   ,					strip_accents=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
												if (
												    normalizer_state.get("""lowercase"""   ,					__UpperCamelCase   ) != do_lower_case
												    or normalizer_state.get("""strip_accents"""   ,					__UpperCamelCase   ) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""   ,					__UpperCamelCase   ) != tokenize_chinese_chars
												):
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					normalizer_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					do_lower_case
																			lowerCamelCase_							  =					strip_accents
																			lowerCamelCase_							  =					tokenize_chinese_chars
																			lowerCamelCase_							  =					normalizer_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					do_lower_case

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[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   ) * [0] + len(token_ids_a + sep   ) * [1]



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )





