

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







def       lowercase					(	__A :					Any		)				->			Dict:





    '''simple docstring'''




    snake_case							:		int        =		DPTConfig()

    if "large" in checkpoint_url:
        snake_case							:		Optional[Any]        =		1024
        snake_case							:		List[str]        =		4096
        snake_case							:		Optional[int]        =		24
        snake_case							:		Dict        =		16
        snake_case							:		Optional[Any]        =		[5, 11, 17, 23]
        snake_case							:		int        =		[256, 512, 1024, 1024]
        snake_case							:		int        =		(1, 384, 384)

    if "ade" in checkpoint_url:
        snake_case							:		str        =		True

        snake_case							:		Optional[int]        =		150
        snake_case							:		List[str]        =		"""huggingface/label-files"""
        snake_case							:		Optional[int]        =		"""ade20k-id2label.json"""
        snake_case							:		Optional[int]        =		json.load(open(cached_download(hf_hub_url(__A ,    __A ,    repo_type="""dataset"""		)		) ,    """r"""		)		)
        snake_case							:		Tuple        =		{int(__A		): v for k, v in idalabel.items()}
        snake_case							:		Any        =		idalabel
        snake_case							:		Any        =		{v: k for k, v in idalabel.items()}
        snake_case							:		Dict        =		[1, 150, 480, 480]

    return config, expected_shape







def       lowercase					(	__A :					int		)				->			int:





    '''simple docstring'''




    snake_case							:		List[Any]        =		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
    for k in ignore_keys:
        state_dict.pop(__A ,    __A		)







def       lowercase					(	__A :					Tuple		)				->			str:





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

    return name







def       lowercase					(	__A :					Dict ,    __A :					Tuple		)				->			Dict:





    '''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)
        snake_case							:		Any        =		state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""		)
        snake_case							:		List[str]        =		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							:		Optional[int]        =		in_proj_weight[: config.hidden_size, :]
        snake_case							:		Optional[Any]        =		in_proj_bias[: config.hidden_size]
        snake_case							:		Union[str, Any]        =		in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case							:		str        =		in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case							:		List[str]        =		in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case							:		Tuple        =		in_proj_bias[-config.hidden_size :]







def       lowercase					(	)				->			Any:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case							:		List[Any]        =		Image.open(requests.get(__A ,    stream=__A		).raw		)
    return im







@torch.no_grad()
def       lowercase					(	__A :					Tuple ,    __A :					Optional[int] ,    __A :					Optional[Any] ,    __A :					Optional[Any]		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case      ,		snake_case							:		Union[str, Any]        =		get_dpt_config(__A		)
    # load original state_dict from URL
    snake_case							:		Union[str, Any]        =		torch.hub.load_state_dict_from_url(__A ,    map_location="""cpu"""		)
    # remove certain keys
    remove_ignore_keys_(__A		)
    # rename keys
    for key in state_dict.copy().keys():
        snake_case							:		Optional[Any]        =		state_dict.pop(__A		)
        snake_case							:		List[str]        =		val
    # read in qkv matrices
    read_in_q_k_v(__A ,    __A		)

    # load HuggingFace model
    snake_case							:		int        =		DPTForSemanticSegmentation(__A		) if """ade""" in checkpoint_url else DPTForDepthEstimation(__A		)
    model.load_state_dict(__A		)
    model.eval()

    # Check outputs on an image
    snake_case							:		int        =		480 if """ade""" in checkpoint_url else 384
    snake_case							:		Optional[int]        =		DPTImageProcessor(size=__A		)

    snake_case							:		List[Any]        =		prepare_img()
    snake_case							:		Optional[Any]        =		image_processor(__A ,    return_tensors="""pt"""		)

    # forward pass
    snake_case							:		Any        =		model(**__A		).logits if """ade""" in checkpoint_url else model(**__A		).predicted_depth

    # Assert logits
    snake_case							:		int        =		torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]		)
    if "ade" in checkpoint_url:
        snake_case							:		List[str]        =		torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]		)
    assert outputs.shape == torch.Size(__A		)
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,    __A ,    atol=1E-4		)
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,    __A		)
    )

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


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     : 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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

												super().__init__(
												    pad_token_id=_A ,			bos_token_id=_A ,			eos_token_id=_A ,			decoder_start_token_id=_A ,			**_A ,			)
