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()
__a  :     str       =  logging.get_logger(__name__)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							DPTConfig()

				if "large" in checkpoint_url:
								__lowercase 			=							1024
								__lowercase 			=							4096
								__lowercase 			=							24
								__lowercase 			=							16
								__lowercase 			=							[5, 11, 17, 23]
								__lowercase 			=							[256, 512, 1024, 1024]
								__lowercase 			=							(1, 384, 384)

				if "ade" in checkpoint_url:
								__lowercase 			=							True

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

				return config, expected_shape





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowercase							,			lowercase							)





def 				UpperCAmelCase						(    lowercase							):







				"""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
				):
								__lowercase 			=							name.replace('''pretrained.model'''							,			'''dpt.encoder'''							)
				if "pretrained.model" in name:
								__lowercase 			=							name.replace('''pretrained.model'''							,			'''dpt.embeddings'''							)
				if "patch_embed" in name:
								__lowercase 			=							name.replace('''patch_embed'''							,			'''patch_embeddings'''							)
				if "pos_embed" in name:
								__lowercase 			=							name.replace('''pos_embed'''							,			'''position_embeddings'''							)
				if "attn.proj" in name:
								__lowercase 			=							name.replace('''attn.proj'''							,			'''attention.output.dense'''							)
				if "proj" in name and "project" not in name:
								__lowercase 			=							name.replace('''proj'''							,			'''projection'''							)
				if "blocks" in name:
								__lowercase 			=							name.replace('''blocks'''							,			'''layer'''							)
				if "mlp.fc1" in name:
								__lowercase 			=							name.replace('''mlp.fc1'''							,			'''intermediate.dense'''							)
				if "mlp.fc2" in name:
								__lowercase 			=							name.replace('''mlp.fc2'''							,			'''output.dense'''							)
				if "norm1" in name:
								__lowercase 			=							name.replace('''norm1'''							,			'''layernorm_before'''							)
				if "norm2" in name:
								__lowercase 			=							name.replace('''norm2'''							,			'''layernorm_after'''							)
				if "scratch.output_conv" in name:
								__lowercase 			=							name.replace('''scratch.output_conv'''							,			'''head'''							)
				if "scratch" in name:
								__lowercase 			=							name.replace('''scratch'''							,			'''neck'''							)
				if "layer1_rn" in name:
								__lowercase 			=							name.replace('''layer1_rn'''							,			'''convs.0'''							)
				if "layer2_rn" in name:
								__lowercase 			=							name.replace('''layer2_rn'''							,			'''convs.1'''							)
				if "layer3_rn" in name:
								__lowercase 			=							name.replace('''layer3_rn'''							,			'''convs.2'''							)
				if "layer4_rn" in name:
								__lowercase 			=							name.replace('''layer4_rn'''							,			'''convs.3'''							)
				if "refinenet" in name:
								__lowercase 			=							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
								__lowercase 			=							name.replace(F"refinenet{layer_idx}"							,			F"fusion_stage.layers.{abs(layer_idx-4							)}"							)
				if "out_conv" in name:
								__lowercase 			=							name.replace('''out_conv'''							,			'''projection'''							)
				if "resConfUnit1" in name:
								__lowercase 			=							name.replace('''resConfUnit1'''							,			'''residual_layer1'''							)
				if "resConfUnit2" in name:
								__lowercase 			=							name.replace('''resConfUnit2'''							,			'''residual_layer2'''							)
				if "conv1" in name:
								__lowercase 			=							name.replace('''conv1'''							,			'''convolution1'''							)
				if "conv2" in name:
								__lowercase 			=							name.replace('''conv2'''							,			'''convolution2'''							)
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess1.0.project.0'''							,			'''neck.reassemble_stage.readout_projects.0.0'''							)
				if "pretrained.act_postprocess2.0.project.0" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess2.0.project.0'''							,			'''neck.reassemble_stage.readout_projects.1.0'''							)
				if "pretrained.act_postprocess3.0.project.0" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess3.0.project.0'''							,			'''neck.reassemble_stage.readout_projects.2.0'''							)
				if "pretrained.act_postprocess4.0.project.0" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess4.0.project.0'''							,			'''neck.reassemble_stage.readout_projects.3.0'''							)
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess1.3'''							,			'''neck.reassemble_stage.layers.0.projection'''							)
				if "pretrained.act_postprocess1.4" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess1.4'''							,			'''neck.reassemble_stage.layers.0.resize'''							)
				if "pretrained.act_postprocess2.3" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess2.3'''							,			'''neck.reassemble_stage.layers.1.projection'''							)
				if "pretrained.act_postprocess2.4" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess2.4'''							,			'''neck.reassemble_stage.layers.1.resize'''							)
				if "pretrained.act_postprocess3.3" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess3.3'''							,			'''neck.reassemble_stage.layers.2.projection'''							)
				if "pretrained.act_postprocess4.3" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess4.3'''							,			'''neck.reassemble_stage.layers.3.projection'''							)
				if "pretrained.act_postprocess4.4" in name:
								__lowercase 			=							name.replace('''pretrained.act_postprocess4.4'''							,			'''neck.reassemble_stage.layers.3.resize'''							)
				if "pretrained" in name:
								__lowercase 			=							name.replace('''pretrained'''							,			'''dpt'''							)
				if "bn" in name:
								__lowercase 			=							name.replace('''bn'''							,			'''batch_norm'''							)
				if "head" in name:
								__lowercase 			=							name.replace('''head'''							,			'''head.head'''							)
				if "encoder.norm" in name:
								__lowercase 			=							name.replace('''encoder.norm'''							,			'''layernorm'''							)
				if "auxlayer" in name:
								__lowercase 			=							name.replace('''auxlayer'''							,			'''auxiliary_head.head'''							)

				return name





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""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)
								__lowercase 			=							state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"							)
								__lowercase 			=							state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"							)
								# next, add query, keys and values (in that order) to the state dict
								__lowercase 			=							in_proj_weight[: config.hidden_size, :]
								__lowercase 			=							in_proj_bias[: config.hidden_size]
								__lowercase 			=							in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__lowercase 			=							in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__lowercase 			=							in_proj_weight[
								    -config.hidden_size :, :
								]
								__lowercase 			=							in_proj_bias[-config.hidden_size :]





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase 			=							Image.open(requests.get(lowercase							,			stream=lowercase							).raw							)
				return im





@torch.no_grad()
def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase			,     __lowercase 			=							get_dpt_config(lowercase							)
				# load original state_dict from URL
				__lowercase 			=							torch.hub.load_state_dict_from_url(lowercase							,			map_location='''cpu'''							)
				# remove certain keys
				remove_ignore_keys_(lowercase							)
				# rename keys
				for key in state_dict.copy().keys():
								__lowercase 			=							state_dict.pop(lowercase							)
								__lowercase 			=							val
				# read in qkv matrices
				read_in_q_k_v(lowercase							,			lowercase							)

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

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

				__lowercase 			=							prepare_img()
				__lowercase 			=							image_processor(lowercase							,			return_tensors='''pt'''							)

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

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

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

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


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

		__a  :     List[Any]       =  parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a  :     str       =  logging.get_logger(__name__)

__a  :     Optional[int]       =  {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Tuple			 =						'''vivit'''





			def __init__(   self      ,   lowerCAmelCase__=2_24      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=[2, 16, 16]      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu_fast"      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-06      ,   lowerCAmelCase__=True      ,   **lowerCAmelCase__      ,   )     ->					int:

							'''simple docstring'''


							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps

							__lowercase 			=							image_size
							__lowercase 			=							num_frames
							__lowercase 			=							tubelet_size
							__lowercase 			=							num_channels
							__lowercase 			=							qkv_bias

							super().__init__(**lowerCAmelCase__						)