'''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_:					List[str]  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			List[Any]      )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       DPTConfig()

     if "large" in checkpoint_url:
          UpperCAmelCase_	   =       10_24
          UpperCAmelCase_	   =       40_96
          UpperCAmelCase_	   =       24
          UpperCAmelCase_	   =       16
          UpperCAmelCase_	   =       [5, 11, 17, 23]
          UpperCAmelCase_	   =       [2_56, 5_12, 10_24, 10_24]
          UpperCAmelCase_	   =       (1, 3_84, 3_84)

     if "ade" in checkpoint_url:
          UpperCAmelCase_	   =       True

          UpperCAmelCase_	   =       1_50
          UpperCAmelCase_	   =       "huggingface/label-files"
          UpperCAmelCase_	   =       "ade20k-id2label.json"
          UpperCAmelCase_	   =       json.load(open(cached_download(hf_hub_url(snake_case_ ,   snake_case_ ,   repo_type="dataset"      )      ) ,   "r"      )      )
          UpperCAmelCase_	   =       {int(snake_case_      ): v for k, v in idalabel.items()}
          UpperCAmelCase_	   =       idalabel
          UpperCAmelCase_	   =       {v: k for k, v in idalabel.items()}
          UpperCAmelCase_	   =       [1, 1_50, 4_80, 4_80]

     return config, expected_shape





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       ["pretrained.model.head.weight", "pretrained.model.head.bias"]
     for k in ignore_keys:
          state_dict.pop(snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[Any]      )   ->				Optional[int]:


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

     return name





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			str      )   ->				List[str]:


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





def      lowerCAmelCase_    ( )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_	   =       Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )
     return im





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Tuple ,   snake_case_	:			Tuple ,   snake_case_	:			List[Any] ,   snake_case_	:			int      )   ->				Optional[int]:


     '''simple docstring'''




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

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

     # Check outputs on an image
     UpperCAmelCase_	   =       4_80 if "ade" in checkpoint_url else 3_84
     UpperCAmelCase_	   =       DPTImageProcessor(size=snake_case_      )

     UpperCAmelCase_	   =       prepare_img()
     UpperCAmelCase_	   =       image_processor(snake_case_ ,   return_tensors="pt"      )

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

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

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

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					List[str]  =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.',
    )

    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =parser.parse_args()
    convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  """t5"""
    a__	:						List[str]	       =  ["""past_key_values"""]
    a__	:						Union[str, Any]	       =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self   :   List[str]       ,      __a   :   Any=32128       ,      __a   :   Optional[int]=512       ,      __a   :   Union[str, Any]=64       ,      __a   :   List[str]=2048       ,      __a   :   Optional[int]=6       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=8       ,      __a   :   Dict=32       ,      __a   :   List[Any]=128       ,      __a   :   Optional[Any]=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   str=1.0       ,      __a   :   Dict="relu"       ,      __a   :   int=True       ,      __a   :   int=True       ,      __a   :   Optional[Any]=0       ,      __a   :   Dict=1       ,      **__a   :   List[Any]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       d_kv
         UpperCAmelCase_	   =       d_ff
         UpperCAmelCase_	   =       num_layers
         UpperCAmelCase_	   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       relative_attention_num_buckets
         UpperCAmelCase_	   =       relative_attention_max_distance
         UpperCAmelCase_	   =       dropout_rate
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_factor
         UpperCAmelCase_	   =       feed_forward_proj
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       self.feed_forward_proj.split("-"			)
         UpperCAmelCase_	   =       act_info[-1]
         UpperCAmelCase_	   =       act_info[0] == "gated"

         if len(__a			) > 1 and act_info[0] != "gated" or len(__a			) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"			)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_	   =       "gelu_new"

         super().__init__(
             pad_token_id=__a       ,      eos_token_id=__a       ,      is_encoder_decoder=__a       ,      **__a       ,      )




class 	__A				(			UpperCamelCase__     ):



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_	   =       "past_encoder_sequence + sequence"
              UpperCAmelCase_	   =       {0: "batch"}
              UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)

         return common_inputs






    @property
    def  _lowercase						(self   :   Optional[int]			):
         return 13




