
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			:							List[Any]         =     logging.get_logger(__name__)






def 			a_						(			__lowercase  :      int					)					->    Optional[Any]:
			_snake_case            =     DPTConfig()

			if "large" in checkpoint_url:
						_snake_case            =     1_024
						_snake_case            =     4_096
						_snake_case            =     24
						_snake_case            =     16
						_snake_case            =     [5, 11, 17, 23]
						_snake_case            =     [256, 512, 1_024, 1_024]
						_snake_case            =     (1, 384, 384)

			if "ade" in checkpoint_url:
						_snake_case            =     True

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

			return config, expected_shape






def 			a_						(			__lowercase  :      int					)					->    int:
			_snake_case            =     ['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__			,   lowerCAmelCase__					)






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

			return name






def 			a_						(			__lowercase  :      Union[str, Any]			,   __lowercase  :      Optional[Any]					)					->    Union[str, Any]:
			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            =     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''					)
						_snake_case            =     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            =     in_proj_weight[: config.hidden_size, :]
						_snake_case            =     in_proj_bias[: config.hidden_size]
						_snake_case            =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						_snake_case            =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						_snake_case            =     in_proj_weight[
						    -config.hidden_size :, :
						]
						_snake_case            =     in_proj_bias[-config.hidden_size :]






def 			a_						(			)					->    Tuple:
			_snake_case            =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case            =     Image.open(requests.get(lowerCAmelCase__			,   stream=lowerCAmelCase__					).raw					)
			return im






@torch.no_grad()
def 			a_						(			__lowercase  :      Tuple			,   __lowercase  :      Dict			,   __lowercase  :      Union[str, Any]			,   __lowercase  :      Union[str, Any]					)					->    List[str]:
			_snake_case            =     get_dpt_config(lowerCAmelCase__					)
			# load original state_dict from URL
			_snake_case            =     torch.hub.load_state_dict_from_url(lowerCAmelCase__			,   map_location='cpu'					)
			# remove certain keys
			remove_ignore_keys_(lowerCAmelCase__					)
			# rename keys
			for key in state_dict.copy().keys():
						_snake_case            =     state_dict.pop(lowerCAmelCase__					)
						_snake_case            =     val
			# read in qkv matrices
			read_in_q_k_v(lowerCAmelCase__			,   lowerCAmelCase__					)

			# load HuggingFace model
			_snake_case            =     DPTForSemanticSegmentation(lowerCAmelCase__					) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase__					)
			model.load_state_dict(lowerCAmelCase__					)
			model.eval()

			# Check outputs on an image
			_snake_case            =     480 if 'ade' in checkpoint_url else 384
			_snake_case            =     DPTImageProcessor(size=lowerCAmelCase__					)

			_snake_case            =     prepare_img()
			_snake_case            =     image_processor(lowerCAmelCase__			,   return_tensors='pt'					)

			# forward pass
			_snake_case            =     model(**lowerCAmelCase__					).logits if 'ade' in checkpoint_url else model(**lowerCAmelCase__					).predicted_depth

			# Assert logits
			_snake_case            =     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:
						_snake_case            =     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(lowerCAmelCase__					)
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]			,   lowerCAmelCase__			,   atol=1E-4					)
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]			,   lowerCAmelCase__					)
			)

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

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


if __name__ == "__main__":
			_lowerCamelCase			:							List[Any]         =     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.''',
			)

			_lowerCamelCase			:							int         =     parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
