


'''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()
__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							A_			    =		DPTConfig(embedding_type="hybrid" )

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

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

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

							return config, expected_shape







def       a_				(	UpperCamelCase_ ):
							A_			    =		["pretrained.model.head.weight", "pretrained.model.head.bias"]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase_						,  UpperCamelCase_ )







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

							# resize blocks
							if "pretrained.act_postprocess1.3" in name:
														A_			    =		name.replace("pretrained.act_postprocess1.3"						,  "neck.reassemble_stage.layers.0.projection" )
							if "pretrained.act_postprocess1.4" in name:
														A_			    =		name.replace("pretrained.act_postprocess1.4"						,  "neck.reassemble_stage.layers.0.resize" )
							if "pretrained.act_postprocess2.3" in name:
														A_			    =		name.replace("pretrained.act_postprocess2.3"						,  "neck.reassemble_stage.layers.1.projection" )
							if "pretrained.act_postprocess2.4" in name:
														A_			    =		name.replace("pretrained.act_postprocess2.4"						,  "neck.reassemble_stage.layers.1.resize" )
							if "pretrained.act_postprocess3.3" in name:
														A_			    =		name.replace("pretrained.act_postprocess3.3"						,  "neck.reassemble_stage.layers.2.projection" )
							if "pretrained.act_postprocess4.3" in name:
														A_			    =		name.replace("pretrained.act_postprocess4.3"						,  "neck.reassemble_stage.layers.3.projection" )
							if "pretrained.act_postprocess4.4" in name:
														A_			    =		name.replace("pretrained.act_postprocess4.4"						,  "neck.reassemble_stage.layers.3.resize" )
							if "pretrained" in name:
														A_			    =		name.replace("pretrained"						,  "dpt" )
							if "bn" in name:
														A_			    =		name.replace("bn"						,  "batch_norm" )
							if "head" in name:
														A_			    =		name.replace("head"						,  "head.head" )
							if "encoder.norm" in name:
														A_			    =		name.replace("encoder.norm"						,  "layernorm" )
							if "auxlayer" in name:
														A_			    =		name.replace("auxlayer"						,  "auxiliary_head.head" )
							if "backbone" in name:
														A_			    =		name.replace("backbone"						,  "backbone.bit.encoder" )

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

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							for i in range(config.num_hidden_layers ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														A_			    =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight" )
														A_			    =		state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias" )
														# next, add query, keys and values (in that order) to the state dict
														A_			    =		in_proj_weight[: config.hidden_size, :]
														A_			    =		in_proj_bias[: config.hidden_size]
														A_			    =		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														A_			    =		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														A_			    =		in_proj_weight[
														    -config.hidden_size :, :
														]
														A_			    =		in_proj_bias[-config.hidden_size :]







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_    ,					A_			    =		get_dpt_config(UpperCamelCase_ )
							# load original state_dict from URL
							# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							# remove certain keys
							remove_ignore_keys_(UpperCamelCase_ )
							# rename keys
							for key in state_dict.copy().keys():
														A_			    =		state_dict.pop(UpperCamelCase_ )
														A_			    =		val
							# read in qkv matrices
							read_in_q_k_v(UpperCamelCase_						,  UpperCamelCase_ )

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

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

							A_			    =		prepare_img()
							A_			    =		image_processor(UpperCamelCase_						,  return_tensors="pt" )

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

							if show_prediction:
														A_			    =		(
														    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()) * 2_5_5 ).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__":
							__SCREAMING_SNAKE_CASE					:				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=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''',
							)

							__SCREAMING_SNAKE_CASE					:				str									=		parser.parse_args()
							convert_dpt_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
							)









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


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						="gptsan-japanese"
						_UpperCAmelCase		:       Union[str, Any]   						=[
						    "past_key_values",
						]
						_UpperCAmelCase		:       int   						={
						    "hidden_size": "d_model",
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      Dict=3_60_00 ,  lowerCAmelCase							:      Union[str, Any]=12_80 ,  lowerCAmelCase							:      Tuple=10_24 ,  lowerCAmelCase							:      Any=81_92 ,  lowerCAmelCase							:      List[str]=40_96 ,  lowerCAmelCase							:      Dict=1_28 ,  lowerCAmelCase							:      int=10 ,  lowerCAmelCase							:      str=0 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      str=1_28 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      List[str]=1e-5 ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      int=0.0 ,  lowerCAmelCase							:      Tuple="float32" ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      int=False ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      Optional[Any]=0.0_0_2 ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      Optional[int]=3_59_98 ,  lowerCAmelCase							:      Optional[Any]=3_59_95 ,  lowerCAmelCase							:      Tuple=3_59_99 ,  **lowerCAmelCase							:      Dict ,  ):
													A_			    =		vocab_size
													A_			    =		max_position_embeddings
													A_			    =		d_model
													A_			    =		d_ff
													A_			    =		d_ext
													A_			    =		d_spout
													A_			    =		num_switch_layers
													A_			    =		num_ext_layers
													A_			    =		num_switch_layers + num_ext_layers
													A_			    =		num_heads
													A_			    =		num_experts
													A_			    =		expert_capacity
													A_			    =		dropout_rate
													A_			    =		layer_norm_epsilon
													A_			    =		router_bias
													A_			    =		router_jitter_noise
													A_			    =		router_dtype
													A_			    =		router_ignore_padding_tokens
													A_			    =		output_hidden_states
													A_			    =		output_attentions
													A_			    =		initializer_factor
													A_			    =		output_router_logits
													A_			    =		use_cache

													super().__init__(
													    separator_token_id=lowerCAmelCase ,  pad_token_id=lowerCAmelCase ,  eos_token_id=lowerCAmelCase ,  **lowerCAmelCase ,  )





