
"""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()
__lowercase						:     Dict							      =		logging.get_logger(__name__)


def lowerCamelCase_				(     _lowerCamelCase			:	int				):
       lowerCamelCase_       							=		DPTConfig()

       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 "ade" in checkpoint_url:
              lowerCamelCase_       							=		True

              lowerCamelCase_       							=		1_5_0
              lowerCamelCase_       							=		'''huggingface/label-files'''
              lowerCamelCase_       							=		'''ade20k-id2label.json'''
              lowerCamelCase_       							=		json.load(open(cached_download(hf_hub_url(lowerCamelCase__					,				lowerCamelCase__					,				repo_type='''dataset'''				)				)					,				'''r'''				)				)
              lowerCamelCase_       							=		{int(lowerCamelCase__				): 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_				(     _lowerCamelCase			:	Union[str, Any]				):
       lowerCamelCase_       							=		['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowerCamelCase__					,				lowerCamelCase__				)


def lowerCamelCase_				(     _lowerCamelCase			:	Optional[int]				):
       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'''					,				'''patch_embeddings'''				)
       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:
              lowerCamelCase_       							=		name.replace('''norm1'''					,				'''layernorm_before'''				)
       if "norm2" 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'''				)

       return name


def lowerCamelCase_				(     _lowerCamelCase			:	int					,				_lowerCamelCase			:	str				):
       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_				(     ):
       lowerCamelCase_       							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowerCamelCase_       							=		Image.open(requests.get(lowerCamelCase__					,				stream=lowerCamelCase__				).raw				)
       return im


@torch.no_grad()
def lowerCamelCase_				(     _lowerCamelCase			:	Dict					,				_lowerCamelCase			:	List[Any]					,				_lowerCamelCase			:	Union[str, Any]					,				_lowerCamelCase			:	Optional[int]				):
       lowerCamelCase_							,							lowerCamelCase_       							=		get_dpt_config(lowerCamelCase__				)
       # load original state_dict from URL
       lowerCamelCase_       							=		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():
              lowerCamelCase_       							=		state_dict.pop(lowerCamelCase__				)
              lowerCamelCase_       							=		val
       # read in qkv matrices
       read_in_q_k_v(lowerCamelCase__					,				lowerCamelCase__				)

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

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

       lowerCamelCase_       							=		prepare_img()
       lowerCamelCase_       							=		image_processor(lowerCamelCase__					,				return_tensors='''pt'''				)

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

       # Assert logits
       lowerCamelCase_       							=		torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]				)
       if "ade" in checkpoint_url:
              lowerCamelCase_       							=		torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]				)
       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__":
 __lowercase						:     Tuple							      =		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.""",
 )

 __lowercase						:     Any							      =		parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


