
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()
lowerCamelCase_							=     logging.get_logger(__name__)






def 						__magic_name__ (    __a			:       List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=				DPTConfig(embedding_type="""hybrid"""						)

     if "large" in checkpoint_url:
          UpperCamelCase__				=				1_024
          UpperCamelCase__				=				4_096
          UpperCamelCase__				=				24
          UpperCamelCase__				=				16
          UpperCamelCase__				=				[5, 11, 17, 23]
          UpperCamelCase__				=				[256, 512, 1_024, 1_024]
          UpperCamelCase__				=				(1, 384, 384)

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

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

     return config, expected_shape






def 						__magic_name__ (    __a			:       Optional[int]						):



     '''simple docstring'''




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






def 						__magic_name__ (    __a			:       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
     ):
          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"""       ,		""""""						)
     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 and "backbone" not in name:
          UpperCamelCase__				=				name.replace("""norm1"""       ,		"""layernorm_before"""						)
     if "norm2" in name and "backbone" not 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"""						)
     if "backbone" in name:
          UpperCamelCase__				=				name.replace("""backbone"""       ,		"""backbone.bit.encoder"""						)

     if ".." in name:
          UpperCamelCase__				=				name.replace(""".."""       ,		"""."""						)

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






def 						__magic_name__ (    __a			:       str       ,		__a			:       Tuple						):



     '''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 						__magic_name__ (    ):



     '''simple docstring'''




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






@torch.no_grad()
def 						__magic_name__ (    __a			:       Tuple       ,		__a			:       str       ,		__a			:       Tuple       ,		__a			:       Tuple       ,		__a			:       str						):



     '''simple docstring'''




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

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

     # Check outputs on an image
     UpperCamelCase__				=				480 if """ade""" in checkpoint_url else 384
     UpperCamelCase__				=				DPTImageProcessor(size=_lowercase						)

     UpperCamelCase__				=				prepare_img()
     UpperCamelCase__				=				image_processor(_lowercase       ,		return_tensors="""pt"""						)

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

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

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

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

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


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

    lowerCamelCase_							=     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 unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							List[str]						,     snake_case:							Optional[Any]=13						,     snake_case:							List[str]=7						,     snake_case:							Dict=True						,     snake_case:							List[str]=True						,     snake_case:							Optional[int]=True						,     snake_case:							Any=True						,     snake_case:							Optional[Any]=99						,     snake_case:							Tuple=32						,     snake_case:							Tuple=5						,     snake_case:							Dict=4						,     snake_case:							Optional[Any]=37						,     snake_case:							Union[str, Any]="gelu"						,     snake_case:							Tuple=0.1						,     snake_case:							List[Any]=0.1						,     snake_case:							List[str]=512						,     snake_case:							Optional[int]=16						,     snake_case:							int=2						,     snake_case:							List[Any]=0.0_2						,     snake_case:							Union[str, Any]=4						,     )   ->	List[str]:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :Any						=			seq_length
											snake_case_ :List[str]						=			is_training
											snake_case_ :Optional[Any]						=			use_attention_mask
											snake_case_ :Dict						=			use_token_type_ids
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :str						=			vocab_size
											snake_case_ :int						=			hidden_size
											snake_case_ :List[str]						=			num_hidden_layers
											snake_case_ :Dict						=			num_attention_heads
											snake_case_ :Any						=			intermediate_size
											snake_case_ :Tuple						=			hidden_act
											snake_case_ :int						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Any						=			max_position_embeddings
											snake_case_ :Union[str, Any]						=			type_vocab_size
											snake_case_ :Optional[int]						=			type_sequence_label_size
											snake_case_ :Union[str, Any]						=			initializer_range
											snake_case_ :Tuple						=			num_choices






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	str:
											snake_case_ :Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

											snake_case_ :Union[str, Any]						=			None
											if self.use_attention_mask:
																	snake_case_ :str						=			random_attention_mask([self.batch_size, self.seq_length]						)

											snake_case_ :Any						=			None
											if self.use_token_type_ids:
																	snake_case_ :List[str]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

											snake_case_ :int						=			BertConfig(
											    vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=snake_case						,     initializer_range=self.initializer_range						,     )

											return config, input_ids, token_type_ids, attention_mask






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											snake_case_ :str						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Optional[int]						=			config_and_inputs
											snake_case_ :Union[str, Any]						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Any:
											snake_case_ :int						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Dict						=			config_and_inputs

											snake_case_ :Union[str, Any]						=			True
											snake_case_ :Optional[int]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											snake_case_ :Tuple						=			ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2						)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)

@require_flax
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     True

					_A		:			Dict						=     (
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			FlaxBertModelTester(self						)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											snake_case_ :Dict						=			FlaxBertModel.from_pretrained("""bert-base-cased"""						)
											snake_case_ :Dict						=			model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)
