


'''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()
UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		str       =			DPTConfig(embedding_type='hybrid'			)

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

 if "nyu" or "midas" in checkpoint_url:
  __A  :		Optional[int]       =			768
  __A  :		int       =			[1, 1, 1, 0.5]
  __A  :		List[Any]       =			[256, 512, 768, 768]
  __A  :		str       =			150
  __A  :		Dict       =			16
  __A  :		List[Any]       =			(1, 384, 384)
  __A  :		int       =			False
  __A  :		Tuple       =			'project'

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

 return config, expected_shape


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
 __A  :		Union[str, Any]       =			['pretrained.model.head.weight', 'pretrained.model.head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					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
 ):
  __A  :		List[str]       =			name.replace('pretrained.model',				'dpt.encoder'			)
 if "pretrained.model" in name:
  __A  :		Dict       =			name.replace('pretrained.model',				'dpt.embeddings'			)
 if "patch_embed" in name:
  __A  :		str       =			name.replace('patch_embed',				''			)
 if "pos_embed" in name:
  __A  :		Optional[int]       =			name.replace('pos_embed',				'position_embeddings'			)
 if "attn.proj" in name:
  __A  :		int       =			name.replace('attn.proj',				'attention.output.dense'			)
 if "proj" in name and "project" not in name:
  __A  :		int       =			name.replace('proj',				'projection'			)
 if "blocks" in name:
  __A  :		Optional[int]       =			name.replace('blocks',				'layer'			)
 if "mlp.fc1" in name:
  __A  :		Optional[Any]       =			name.replace('mlp.fc1',				'intermediate.dense'			)
 if "mlp.fc2" in name:
  __A  :		List[Any]       =			name.replace('mlp.fc2',				'output.dense'			)
 if "norm1" in name and "backbone" not in name:
  __A  :		Dict       =			name.replace('norm1',				'layernorm_before'			)
 if "norm2" in name and "backbone" not in name:
  __A  :		Optional[Any]       =			name.replace('norm2',				'layernorm_after'			)
 if "scratch.output_conv" in name:
  __A  :		List[Any]       =			name.replace('scratch.output_conv',				'head'			)
 if "scratch" in name:
  __A  :		int       =			name.replace('scratch',				'neck'			)
 if "layer1_rn" in name:
  __A  :		Tuple       =			name.replace('layer1_rn',				'convs.0'			)
 if "layer2_rn" in name:
  __A  :		Tuple       =			name.replace('layer2_rn',				'convs.1'			)
 if "layer3_rn" in name:
  __A  :		Union[str, Any]       =			name.replace('layer3_rn',				'convs.2'			)
 if "layer4_rn" in name:
  __A  :		Tuple       =			name.replace('layer4_rn',				'convs.3'			)
 if "refinenet" in name:
  __A  :		Any       =			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
  __A  :		Dict       =			name.replace(f"""refinenet{layer_idx}""",				f"""fusion_stage.layers.{abs(layer_idx-4			)}"""			)
 if "out_conv" in name:
  __A  :		List[str]       =			name.replace('out_conv',				'projection'			)
 if "resConfUnit1" in name:
  __A  :		str       =			name.replace('resConfUnit1',				'residual_layer1'			)
 if "resConfUnit2" in name:
  __A  :		List[str]       =			name.replace('resConfUnit2',				'residual_layer2'			)
 if "conv1" in name:
  __A  :		List[str]       =			name.replace('conv1',				'convolution1'			)
 if "conv2" in name:
  __A  :		List[Any]       =			name.replace('conv2',				'convolution2'			)
 # readout blocks
 if "pretrained.act_postprocess1.0.project.0" in name:
  __A  :		int       =			name.replace('pretrained.act_postprocess1.0.project.0',				'neck.reassemble_stage.readout_projects.0.0'			)
 if "pretrained.act_postprocess2.0.project.0" in name:
  __A  :		int       =			name.replace('pretrained.act_postprocess2.0.project.0',				'neck.reassemble_stage.readout_projects.1.0'			)
 if "pretrained.act_postprocess3.0.project.0" in name:
  __A  :		Dict       =			name.replace('pretrained.act_postprocess3.0.project.0',				'neck.reassemble_stage.readout_projects.2.0'			)
 if "pretrained.act_postprocess4.0.project.0" in name:
  __A  :		int       =			name.replace('pretrained.act_postprocess4.0.project.0',				'neck.reassemble_stage.readout_projects.3.0'			)

 # resize blocks
 if "pretrained.act_postprocess1.3" in name:
  __A  :		str       =			name.replace('pretrained.act_postprocess1.3',				'neck.reassemble_stage.layers.0.projection'			)
 if "pretrained.act_postprocess1.4" in name:
  __A  :		Tuple       =			name.replace('pretrained.act_postprocess1.4',				'neck.reassemble_stage.layers.0.resize'			)
 if "pretrained.act_postprocess2.3" in name:
  __A  :		Optional[int]       =			name.replace('pretrained.act_postprocess2.3',				'neck.reassemble_stage.layers.1.projection'			)
 if "pretrained.act_postprocess2.4" in name:
  __A  :		Tuple       =			name.replace('pretrained.act_postprocess2.4',				'neck.reassemble_stage.layers.1.resize'			)
 if "pretrained.act_postprocess3.3" in name:
  __A  :		Dict       =			name.replace('pretrained.act_postprocess3.3',				'neck.reassemble_stage.layers.2.projection'			)
 if "pretrained.act_postprocess4.3" in name:
  __A  :		Optional[Any]       =			name.replace('pretrained.act_postprocess4.3',				'neck.reassemble_stage.layers.3.projection'			)
 if "pretrained.act_postprocess4.4" in name:
  __A  :		int       =			name.replace('pretrained.act_postprocess4.4',				'neck.reassemble_stage.layers.3.resize'			)
 if "pretrained" in name:
  __A  :		Tuple       =			name.replace('pretrained',				'dpt'			)
 if "bn" in name:
  __A  :		Tuple       =			name.replace('bn',				'batch_norm'			)
 if "head" in name:
  __A  :		Optional[Any]       =			name.replace('head',				'head.head'			)
 if "encoder.norm" in name:
  __A  :		Dict       =			name.replace('encoder.norm',				'layernorm'			)
 if "auxlayer" in name:
  __A  :		Any       =			name.replace('auxlayer',				'auxiliary_head.head'			)
 if "backbone" in name:
  __A  :		List[str]       =			name.replace('backbone',				'backbone.bit.encoder'			)

 if ".." in name:
  __A  :		Optional[int]       =			name.replace('..',				'.'			)

 if "stem.conv" in name:
  __A  :		Dict       =			name.replace('stem.conv',				'bit.embedder.convolution'			)
 if "blocks" in name:
  __A  :		Optional[Any]       =			name.replace('blocks',				'layers'			)
 if "convolution" in name and "backbone" in name:
  __A  :		Tuple       =			name.replace('convolution',				'conv'			)
 if "layer" in name and "backbone" in name:
  __A  :		List[str]       =			name.replace('layer',				'layers'			)
 if "backbone.bit.encoder.bit" in name:
  __A  :		List[Any]       =			name.replace('backbone.bit.encoder.bit',				'backbone.bit'			)
 if "embedder.conv" in name:
  __A  :		List[str]       =			name.replace('embedder.conv',				'embedder.convolution'			)
 if "backbone.bit.encoder.stem.norm" in name:
  __A  :		Union[str, Any]       =			name.replace('backbone.bit.encoder.stem.norm',				'backbone.bit.embedder.norm'			)
 return name


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Tuple			):
 for i in range(config.num_hidden_layers			):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __A  :		int       =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""			)
  __A  :		Tuple       =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""			)
  # next, add query, keys and values (in that order) to the state dict
  __A  :		Tuple       =			in_proj_weight[: config.hidden_size, :]
  __A  :		List[Any]       =			in_proj_bias[: config.hidden_size]
  __A  :		str       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		Union[str, Any]       =			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __A  :		List[Any]       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		Tuple       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				):
 __A  :		Union[str, Any]       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Dict       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


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

 # load HuggingFace model
 __A  :		Optional[int]       =			DPTForSemanticSegmentation(UpperCamelCase__			) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)
 model.eval()

 # Check outputs on an image
 __A  :		Any       =			480 if 'ade' in checkpoint_url else 384
 __A  :		Optional[Any]       =			DPTImageProcessor(size=UpperCamelCase__			)

 __A  :		Tuple       =			prepare_img()
 __A  :		Union[str, Any]       =			image_processor(UpperCamelCase__,				return_tensors='pt'			)

 # forward pass
 __A  :		List[str]       =			model(**UpperCamelCase__			).logits if 'ade' in checkpoint_url else model(**UpperCamelCase__			).predicted_depth

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

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

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
  image_processor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  model.push_to_hub('ybelkada/dpt-hybrid-midas'			)
  image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]								=      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',
 )

 UpperCAmelCase_ :     List[str]								=      parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )










'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






