





'''simple docstring'''






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
      pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

A__							:  Dict    ={
    '''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[Any]			=  '''align_text_model'''




      def __init__(     self     :					Any     ,     __snake_case     :					List[str]=3_05_22     ,     __snake_case     :					Any=7_68     ,     __snake_case     :					Any=12     ,     __snake_case     :					Dict=12     ,     __snake_case     :					Dict=30_72     ,     __snake_case     :					str="gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					str=5_12     ,     __snake_case     :					List[str]=2     ,     __snake_case     :					int=0.02     ,     __snake_case     :					Tuple=1E-1_2     ,     __snake_case     :					Dict=0     ,     __snake_case     :					List[Any]="absolute"     ,     __snake_case     :					Any=True     ,     **__snake_case     :					List[str]     ,     )							-> Union[str, Any]:
            super().__init__(**__snake_case    )

            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				hidden_size
            _lowerCAmelCase					=				num_hidden_layers
            _lowerCAmelCase					=				num_attention_heads
            _lowerCAmelCase					=				hidden_act
            _lowerCAmelCase					=				intermediate_size
            _lowerCAmelCase					=				hidden_dropout_prob
            _lowerCAmelCase					=				attention_probs_dropout_prob
            _lowerCAmelCase					=				max_position_embeddings
            _lowerCAmelCase					=				type_vocab_size
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				layer_norm_eps
            _lowerCAmelCase					=				position_embedding_type
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				pad_token_id




      @classmethod
      def 							lowercase__       (     cls     :					List[str]     ,     __snake_case     :					Union[str, os.PathLike]     ,     **__snake_case     :					int    )							-> "PretrainedConfig":
            cls._set_token_in_kwargs(__snake_case    )

            _lowerCAmelCase	,				_lowerCAmelCase					=				cls.get_config_dict(__snake_case     ,     **__snake_case    )

            # get the text config dict if we are loading from AlignConfig
            if config_dict.get("""model_type"""    ) == "align":
                  _lowerCAmelCase					=				config_dict["""text_config"""]

            if "model_type" in config_dict and hasattr(cls     ,     """model_type"""    ) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                      f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."    )

            return cls.from_dict(__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[Any]			=  '''align_vision_model'''




      def __init__(     self     :					Dict     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 6_00     ,     __snake_case     :					float = 2.0     ,     __snake_case     :					float = 3.1     ,     __snake_case     :					int = 8     ,     __snake_case     :					List[int] = [3, 3, 5, 3, 5, 5, 3]     ,     __snake_case     :					List[int] = [32, 16, 24, 40, 80, 1_12, 1_92]     ,     __snake_case     :					List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20]     ,     __snake_case     :					List[int] = []     ,     __snake_case     :					List[int] = [1, 2, 2, 2, 1, 2, 1]     ,     __snake_case     :					List[int] = [1, 2, 2, 3, 3, 4, 1]     ,     __snake_case     :					List[int] = [1, 6, 6, 6, 6, 6, 6]     ,     __snake_case     :					float = 0.25     ,     __snake_case     :					str = "swish"     ,     __snake_case     :					int = 25_60     ,     __snake_case     :					str = "mean"     ,     __snake_case     :					float = 0.02     ,     __snake_case     :					float = 0.0_01     ,     __snake_case     :					float = 0.99     ,     __snake_case     :					float = 0.2     ,     **__snake_case     :					List[Any]     ,     )							-> Optional[int]:
            super().__init__(**__snake_case    )

            _lowerCAmelCase					=				num_channels
            _lowerCAmelCase					=				image_size
            _lowerCAmelCase					=				width_coefficient
            _lowerCAmelCase					=				depth_coefficient
            _lowerCAmelCase					=				depth_divisor
            _lowerCAmelCase					=				kernel_sizes
            _lowerCAmelCase					=				in_channels
            _lowerCAmelCase					=				out_channels
            _lowerCAmelCase					=				depthwise_padding
            _lowerCAmelCase					=				strides
            _lowerCAmelCase					=				num_block_repeats
            _lowerCAmelCase					=				expand_ratios
            _lowerCAmelCase					=				squeeze_expansion_ratio
            _lowerCAmelCase					=				hidden_act
            _lowerCAmelCase					=				hidden_dim
            _lowerCAmelCase					=				pooling_type
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				batch_norm_eps
            _lowerCAmelCase					=				batch_norm_momentum
            _lowerCAmelCase					=				drop_connect_rate
            _lowerCAmelCase					=				sum(__snake_case    ) * 4




      @classmethod
      def 							lowercase__       (     cls     :					List[Any]     ,     __snake_case     :					Union[str, os.PathLike]     ,     **__snake_case     :					int    )							-> "PretrainedConfig":
            cls._set_token_in_kwargs(__snake_case    )

            _lowerCAmelCase	,				_lowerCAmelCase					=				cls.get_config_dict(__snake_case     ,     **__snake_case    )

            # get the vision config dict if we are loading from AlignConfig
            if config_dict.get("""model_type"""    ) == "align":
                  _lowerCAmelCase					=				config_dict["""vision_config"""]

            if "model_type" in config_dict and hasattr(cls     ,     """model_type"""    ) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                      f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."    )

            return cls.from_dict(__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[int]			=  '''align'''
      _lowercase:     List[str]			=  True




      def __init__(     self     :					str     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					Dict=6_40     ,     __snake_case     :					List[Any]=1.0     ,     __snake_case     :					int=0.02     ,     **__snake_case     :					List[str]     ,     )							-> List[Any]:
            super().__init__(**__snake_case    )

            if text_config is None:
                  _lowerCAmelCase					=				{}
                  logger.info("""text_config is None. Initializing the AlignTextConfig with default values."""    )

            if vision_config is None:
                  _lowerCAmelCase					=				{}
                  logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values."""    )

            _lowerCAmelCase					=				AlignTextConfig(**__snake_case    )
            _lowerCAmelCase					=				AlignVisionConfig(**__snake_case    )

            _lowerCAmelCase					=				projection_dim
            _lowerCAmelCase					=				temperature_init_value
            _lowerCAmelCase					=				initializer_range




      @classmethod
      def 							lowercase__       (     cls     :					Optional[int]     ,     __snake_case     :					AlignTextConfig     ,     __snake_case     :					AlignVisionConfig     ,     **__snake_case     :					Optional[Any]    )							-> Tuple:
            return cls(text_config=text_config.to_dict()     ,     vision_config=vision_config.to_dict()     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[str]:
            _lowerCAmelCase					=				copy.deepcopy(self.__dict__    )
            _lowerCAmelCase					=				self.text_config.to_dict()
            _lowerCAmelCase					=				self.vision_config.to_dict()
            _lowerCAmelCase					=				self.__class__.model_type
            return output













'''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()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				DPTConfig()

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

      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				True

            _lowerCAmelCase					=				1_50
            _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_50, 4_80, 4_80]

      return config, expected_shape
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """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
      ):
            _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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """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)
            _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   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _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_80 if """ade""" in checkpoint_url else 3_84
      _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([[6.3_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:
            _lowerCAmelCase					=				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(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__":
      A__							:  Optional[int]    =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__							:  Optional[int]    =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






