





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()
snake_case_				=			logging.get_logger(__name__)






def lowerCamelCase__    (							snake_case_	:  Optional[int]       )      ->				List[str]:
   __snake_case							       =   DPTConfig(embedding_type='''hybrid'''       )

   if "large" in checkpoint_url:
      __snake_case							       =   1024
      __snake_case							       =   4096
      __snake_case							       =   24
      __snake_case							       =   16
      __snake_case							       =   [5, 11, 17, 23]
      __snake_case							       =   [256, 512, 1024, 1024]
      __snake_case							       =   (1, 384, 384)

   if "nyu" or "midas" in checkpoint_url:
      __snake_case							       =   768
      __snake_case							       =   [1, 1, 1, 0.5]
      __snake_case							       =   [256, 512, 768, 768]
      __snake_case							       =   150
      __snake_case							       =   16
      __snake_case							       =   (1, 384, 384)
      __snake_case							       =   False
      __snake_case							       =   '''project'''

   if "ade" in checkpoint_url:
      __snake_case							       =   True
      __snake_case							       =   768
      __snake_case							       =   [1, 1, 1, 0.5]
      __snake_case							       =   150
      __snake_case							       =   16
      __snake_case							       =   '''huggingface/label-files'''
      __snake_case							       =   '''ade20k-id2label.json'''
      __snake_case							       =   json.load(open(cached_download(hf_hub_url(lowerCAmelCase_       ,      lowerCAmelCase_       ,      repo_type='''dataset'''       )       )       ,      '''r'''       )       )
      __snake_case							       =   {int(lowerCAmelCase_       ): v for k, v in idalabel.items()}
      __snake_case							       =   idalabel
      __snake_case							       =   {v: k for k, v in idalabel.items()}
      __snake_case							       =   [1, 150, 480, 480]

   return config, expected_shape






def lowerCamelCase__    (							snake_case_	:  Optional[int]       )      ->				int:
   __snake_case							       =   ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
   for k in ignore_keys:
      state_dict.pop(lowerCAmelCase_       ,      lowerCAmelCase_       )






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

   if ".." in name:
      __snake_case							       =   name.replace('''..'''       ,      '''.'''       )

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






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       )      ->				List[Any]:
   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							       =   state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""       )
      __snake_case							       =   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							       =   in_proj_weight[: config.hidden_size, :]
      __snake_case							       =   in_proj_bias[: config.hidden_size]
      __snake_case							       =   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __snake_case							       =   in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __snake_case							       =   in_proj_weight[
          -config.hidden_size :, :
      ]
      __snake_case							       =   in_proj_bias[-config.hidden_size :]






def lowerCamelCase__    (							)      ->				int:
   __snake_case							       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   __snake_case							       =   Image.open(requests.get(lowerCAmelCase_       ,      stream=lowerCAmelCase_       ).raw       )
   return im






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[Any]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  List[str]       ,      snake_case_	:  int       )      ->				List[Any]:
   __snake_case							       =   get_dpt_config(lowerCAmelCase_       )
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   __snake_case							       =   torch.load(lowerCAmelCase_       ,      map_location='''cpu'''       )
   # remove certain keys
   remove_ignore_keys_(lowerCAmelCase_       )
   # rename keys
   for key in state_dict.copy().keys():
      __snake_case							       =   state_dict.pop(lowerCAmelCase_       )
      __snake_case							       =   val
   # read in qkv matrices
   read_in_q_k_v(lowerCAmelCase_       ,      lowerCAmelCase_       )

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

   # Check outputs on an image
   __snake_case							       =   480 if '''ade''' in checkpoint_url else 384
   __snake_case							       =   DPTImageProcessor(size=lowerCAmelCase_       )

   __snake_case							       =   prepare_img()
   __snake_case							       =   image_processor(lowerCAmelCase_       ,      return_tensors='''pt'''       )

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

   if show_prediction:
      __snake_case							       =   (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1       )       ,      size=(image.size[1], image.size[0])       ,      mode='''bicubic'''       ,      align_corners=lowerCAmelCase_       ,      )
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 255       ).show()

   if pytorch_dump_folder_path is not None:
      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:
      model.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )
      image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )


if __name__ == "__main__":
    snake_case_				=			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',
    )

    snake_case_				=			parser.parse_args()
    convert_dpt_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
    )





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCAmelCase	:     Optional[int]   ="""src/transformers"""
_UpperCAmelCase	:     str   ="""docs/source/en"""
_UpperCAmelCase	:     Optional[int]   ="""."""



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       List[str]:
				with open(lowerCAmelCase_       ,      '''r'''       ,      encoding='''utf-8'''       ,      newline='''\n'''       ) as f:
								lowerCAmelCase_       :					int			 = f.readlines()
				# Find the start prompt.
				lowerCAmelCase_       :					List[Any]			 = 0
				while not lines[start_index].startswith(lowerCAmelCase_       ):
								start_index += 1
				start_index += 1

				lowerCAmelCase_       :					List[str]			 = start_index
				while not lines[end_index].startswith(lowerCAmelCase_       ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]       ) <= 1:
								start_index += 1
				while len(lines[end_index]       ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCAmelCase	:     Optional[Any]   ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase	:     Optional[int]   =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase	:     Dict   =re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCAmelCase	:     Optional[Any]   =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase	:     Optional[int]   =direct_transformers_import(TRANSFORMERS_PATH)



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					str			 = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,      lowerCAmelCase_       )
				return [m.group(0       ) for m in matches]



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Tuple			 = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_       )
				lowerCAmelCase_       :					int			 = (width - text_length) // 2
				lowerCAmelCase_       :					Union[str, Any]			 = width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def 	lowerCAmelCase       (						)->       str:
				lowerCAmelCase_       :					Any			 = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_       :					Dict			 = {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase_       :					List[Any]			 = {name: config.replace('''Config'''       ,      ''''''       ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				lowerCAmelCase_       :					Tuple			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[Any]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[int]			 = collections.defaultdict(lowerCAmelCase_       )
				lowerCAmelCase_       :					List[str]			 = collections.defaultdict(lowerCAmelCase_       )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(lowerCAmelCase_       ):
								lowerCAmelCase_       :					Optional[int]			 = None
								if attr_name.endswith('''Tokenizer'''       ):
												lowerCAmelCase_       :					Union[str, Any]			 = slow_tokenizers
												lowerCAmelCase_       :					List[str]			 = attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''       ):
												lowerCAmelCase_       :					int			 = fast_tokenizers
												lowerCAmelCase_       :					Union[str, Any]			 = attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Tuple			 = tf_models
												lowerCAmelCase_       :					str			 = _re_tf_models.match(lowerCAmelCase_       ).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Tuple			 = flax_models
												lowerCAmelCase_       :					Union[str, Any]			 = _re_flax_models.match(lowerCAmelCase_       ).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_       ) is not None:
												lowerCAmelCase_       :					Any			 = pt_models
												lowerCAmelCase_       :					List[Any]			 = _re_pt_models.match(lowerCAmelCase_       ).groups()[0]

								if lookup_dict is not None:
												while len(lowerCAmelCase_       ) > 0:
																if attr_name in model_name_to_prefix.values():
																				lowerCAmelCase_       :					Union[str, Any]			 = True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_       :					Any			 = ''''''.join(camel_case_split(lowerCAmelCase_       )[:-1]       )

    # Let's build that table!
				lowerCAmelCase_       :					int			 = list(model_name_to_config.keys()       )
				model_names.sort(key=str.lower       )
				lowerCAmelCase_       :					Tuple			 = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				lowerCAmelCase_       :					Union[str, Any]			 = [len(lowerCAmelCase_       ) + 2 for c in columns]
				lowerCAmelCase_       :					Optional[Any]			 = max([len(lowerCAmelCase_       ) for name in model_names]       ) + 2

				# Build the table per se
				lowerCAmelCase_       :					Dict			 = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_       ,      lowerCAmelCase_       ) for c, w in zip(lowerCAmelCase_       ,      lowerCAmelCase_       )]       ) + '''|\n'''
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]       ) + "|\n"

				lowerCAmelCase_       :					List[str]			 = {True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_       :					List[Any]			 = model_name_to_prefix[name]
								lowerCAmelCase_       :					Union[str, Any]			 = [
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCAmelCase_       ,      lowerCAmelCase_       ) for l, w in zip(lowerCAmelCase_       ,      lowerCAmelCase_       )]       ) + "|\n"
				return table



def 	lowerCAmelCase       (						lowerCAmelCase_=False       )->       Tuple:
				lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_       :					str			 = _find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_       ,      '''index.md'''       )       ,      start_prompt='''<!--This table is updated automatically from the auto modules'''       ,      end_prompt='''<!-- End table-->'''       ,      )
				lowerCAmelCase_       :					Tuple			 = get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_       ,      '''index.md'''       )       ,      '''w'''       ,      encoding='''utf-8'''       ,      newline='''\n'''       ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
								else:
												raise ValueError(
												    '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''       )


if __name__ == "__main__":
						_UpperCAmelCase	:     List[Any]   =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase	:     Tuple   =parser.parse_args()

						check_model_table(args.fix_and_overwrite)