





'''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()
lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Union[str, Any]    			= DPTConfig()

   if "large" in checkpoint_url:
      lowercase_			:Tuple    			= 10_24
      lowercase_			:Optional[Any]    			= 40_96
      lowercase_			:Optional[Any]    			= 24
      lowercase_			:Optional[int]    			= 16
      lowercase_			:List[Any]    			= [5, 11, 17, 23]
      lowercase_			:Union[str, Any]    			= [2_56, 5_12, 10_24, 10_24]
      lowercase_			:Dict    			= (1, 3_84, 3_84)

   if "ade" in checkpoint_url:
      lowercase_			:Optional[int]    			= True

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

   return config, expected_shape
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Union[str, Any]    			= ["pretrained.model.head.weight", "pretrained.model.head.bias"]
   for k in ignore_keys:
      state_dict.pop(__lowerCamelCase	,__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      lowercase_			:List[Any]    			= name.replace("pretrained.model"	,"dpt.encoder" )
   if "pretrained.model" in name:
      lowercase_			:Dict    			= name.replace("pretrained.model"	,"dpt.embeddings" )
   if "patch_embed" in name:
      lowercase_			:List[Any]    			= name.replace("patch_embed"	,"patch_embeddings" )
   if "pos_embed" in name:
      lowercase_			:Dict    			= name.replace("pos_embed"	,"position_embeddings" )
   if "attn.proj" in name:
      lowercase_			:Optional[Any]    			= name.replace("attn.proj"	,"attention.output.dense" )
   if "proj" in name and "project" not in name:
      lowercase_			:int    			= name.replace("proj"	,"projection" )
   if "blocks" in name:
      lowercase_			:Dict    			= name.replace("blocks"	,"layer" )
   if "mlp.fc1" in name:
      lowercase_			:str    			= name.replace("mlp.fc1"	,"intermediate.dense" )
   if "mlp.fc2" in name:
      lowercase_			:Dict    			= name.replace("mlp.fc2"	,"output.dense" )
   if "norm1" in name:
      lowercase_			:str    			= name.replace("norm1"	,"layernorm_before" )
   if "norm2" in name:
      lowercase_			:List[Any]    			= name.replace("norm2"	,"layernorm_after" )
   if "scratch.output_conv" in name:
      lowercase_			:Dict    			= name.replace("scratch.output_conv"	,"head" )
   if "scratch" in name:
      lowercase_			:str    			= name.replace("scratch"	,"neck" )
   if "layer1_rn" in name:
      lowercase_			:Dict    			= name.replace("layer1_rn"	,"convs.0" )
   if "layer2_rn" in name:
      lowercase_			:Optional[Any]    			= name.replace("layer2_rn"	,"convs.1" )
   if "layer3_rn" in name:
      lowercase_			:int    			= name.replace("layer3_rn"	,"convs.2" )
   if "layer4_rn" in name:
      lowercase_			:List[Any]    			= name.replace("layer4_rn"	,"convs.3" )
   if "refinenet" in name:
      lowercase_			:List[str]    			= 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
      lowercase_			:Optional[int]    			= name.replace(F'refinenet{layer_idx}'	,F'fusion_stage.layers.{abs(layer_idx-4 )}' )
   if "out_conv" in name:
      lowercase_			:str    			= name.replace("out_conv"	,"projection" )
   if "resConfUnit1" in name:
      lowercase_			:Any    			= name.replace("resConfUnit1"	,"residual_layer1" )
   if "resConfUnit2" in name:
      lowercase_			:List[str]    			= name.replace("resConfUnit2"	,"residual_layer2" )
   if "conv1" in name:
      lowercase_			:str    			= name.replace("conv1"	,"convolution1" )
   if "conv2" in name:
      lowercase_			:Optional[int]    			= name.replace("conv2"	,"convolution2" )
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      lowercase_			:Tuple    			= name.replace("pretrained.act_postprocess1.0.project.0"	,"neck.reassemble_stage.readout_projects.0.0" )
   if "pretrained.act_postprocess2.0.project.0" in name:
      lowercase_			:Optional[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:
      lowercase_			:Tuple    			= name.replace("pretrained.act_postprocess3.0.project.0"	,"neck.reassemble_stage.readout_projects.2.0" )
   if "pretrained.act_postprocess4.0.project.0" in name:
      lowercase_			:List[Any]    			= name.replace("pretrained.act_postprocess4.0.project.0"	,"neck.reassemble_stage.readout_projects.3.0" )
   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      lowercase_			:str    			= name.replace("pretrained.act_postprocess1.3"	,"neck.reassemble_stage.layers.0.projection" )
   if "pretrained.act_postprocess1.4" in name:
      lowercase_			:List[Any]    			= name.replace("pretrained.act_postprocess1.4"	,"neck.reassemble_stage.layers.0.resize" )
   if "pretrained.act_postprocess2.3" in name:
      lowercase_			:Optional[int]    			= name.replace("pretrained.act_postprocess2.3"	,"neck.reassemble_stage.layers.1.projection" )
   if "pretrained.act_postprocess2.4" in name:
      lowercase_			:Tuple    			= name.replace("pretrained.act_postprocess2.4"	,"neck.reassemble_stage.layers.1.resize" )
   if "pretrained.act_postprocess3.3" in name:
      lowercase_			:List[str]    			= name.replace("pretrained.act_postprocess3.3"	,"neck.reassemble_stage.layers.2.projection" )
   if "pretrained.act_postprocess4.3" in name:
      lowercase_			:Tuple    			= name.replace("pretrained.act_postprocess4.3"	,"neck.reassemble_stage.layers.3.projection" )
   if "pretrained.act_postprocess4.4" in name:
      lowercase_			:List[str]    			= name.replace("pretrained.act_postprocess4.4"	,"neck.reassemble_stage.layers.3.resize" )
   if "pretrained" in name:
      lowercase_			:List[Any]    			= name.replace("pretrained"	,"dpt" )
   if "bn" in name:
      lowercase_			:Tuple    			= name.replace("bn"	,"batch_norm" )
   if "head" in name:
      lowercase_			:Any    			= name.replace("head"	,"head.head" )
   if "encoder.norm" in name:
      lowercase_			:str    			= name.replace("encoder.norm"	,"layernorm" )
   if "auxlayer" in name:
      lowercase_			:Dict    			= name.replace("auxlayer"	,"auxiliary_head.head" )

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any	,__lowerCamelCase					:					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)
      lowercase_			:Optional[Any]    			= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
      lowercase_			:str    			= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
      # next, add query, keys and values (in that order) to the state dict
      lowercase_			:Optional[Any]    			= in_proj_weight[: config.hidden_size, :]
      lowercase_			:Optional[Any]    			= in_proj_bias[: config.hidden_size]
      lowercase_			:Tuple    			= in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase_			:Tuple    			= in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowercase_			:Optional[int]    			= in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase_			:int    			= in_proj_bias[-config.hidden_size :]
def 							UpperCAmelCase_  (						):
   lowercase_			:List[str]    			= "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_			:List[str]    			= Image.open(requests.get(__lowerCamelCase	,stream=__lowerCamelCase ).raw )
   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					int ):
   lowercase_			,							lowercase_			:Optional[int]    			= get_dpt_config(__lowerCamelCase )
   # load original state_dict from URL
   lowercase_			:Optional[int]    			= 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():
      lowercase_			:Optional[int]    			= state_dict.pop(__lowerCamelCase )
      lowercase_			:Optional[int]    			= val
   # read in qkv matrices
   read_in_q_k_v(__lowerCamelCase	,__lowerCamelCase )

   # load HuggingFace model
   lowercase_			:int    			= DPTForSemanticSegmentation(__lowerCamelCase ) if "ade" in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase )
   model.load_state_dict(__lowerCamelCase )
   model.eval()

   # Check outputs on an image
   lowercase_			:int    			= 4_80 if "ade" in checkpoint_url else 3_84
   lowercase_			:Any    			= DPTImageProcessor(size=__lowerCamelCase )

   lowercase_			:Union[str, Any]    			= prepare_img()
   lowercase_			:Tuple    			= image_processor(__lowerCamelCase	,return_tensors="pt" )

   # forward pass
   lowercase_			:Tuple    			= model(**__lowerCamelCase ).logits if "ade" in checkpoint_url else model(**__lowerCamelCase ).predicted_depth

   # Assert logits
   lowercase_			:List[str]    			= 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:
      lowercase_			:Dict    			= 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__":
 lowerCAmelCase					:     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=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.''',
 )

 lowerCAmelCase					:     List[Any]												=parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''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       a_   (				unittest.TestCase							):





     def __init__( self		:					List[str]	,      lowercase		:					str	,      lowercase		:					Union[str, Any]=13	,      lowercase		:					int=7	,      lowercase		:					List[str]=True	,      lowercase		:					int=True	,      lowercase		:					str=True	,      lowercase		:					Any=True	,      lowercase		:					List[str]=99	,      lowercase		:					Union[str, Any]=32	,      lowercase		:					Optional[Any]=5	,      lowercase		:					Dict=4	,      lowercase		:					Dict=37	,      lowercase		:					Dict="gelu"	,      lowercase		:					Optional[int]=0.1	,      lowercase		:					str=0.1	,      lowercase		:					List[Any]=512	,      lowercase		:					str=16	,      lowercase		:					Dict=2	,      lowercase		:					Any=0.02	,      lowercase		:					Any=4	,      ):





        """simple docstring"""




        lowercase_			:List[str]    			= parent
        lowercase_			:Any    			= batch_size
        lowercase_			:Dict    			= seq_length
        lowercase_			:Union[str, Any]    			= is_training
        lowercase_			:Optional[int]    			= use_attention_mask
        lowercase_			:Any    			= use_token_type_ids
        lowercase_			:Union[str, Any]    			= use_labels
        lowercase_			:Dict    			= vocab_size
        lowercase_			:Tuple    			= hidden_size
        lowercase_			:Tuple    			= num_hidden_layers
        lowercase_			:Optional[int]    			= num_attention_heads
        lowercase_			:Optional[Any]    			= intermediate_size
        lowercase_			:str    			= hidden_act
        lowercase_			:Tuple    			= hidden_dropout_prob
        lowercase_			:Optional[Any]    			= attention_probs_dropout_prob
        lowercase_			:Tuple    			= max_position_embeddings
        lowercase_			:Any    			= type_vocab_size
        lowercase_			:int    			= type_sequence_label_size
        lowercase_			:Tuple    			= initializer_range
        lowercase_			:Optional[Any]    			= num_choices





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:int    			= ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

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

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

        lowercase_			:Optional[Any]    			= 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=lowercase	,      initializer_range=self.initializer_range	,      )

        return config, input_ids, token_type_ids, attention_mask





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Tuple    			= config_and_inputs
        lowercase_			:Tuple    			= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict







     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Any    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Union[str, Any]    			= config_and_inputs

        lowercase_			:Dict    			= True
        lowercase_			:Optional[int]    			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        lowercase_			:str    			= 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       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			True

     __A      =			(
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= FlaxBertModelTester(self						)







     @slow
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:List[str]    			= FlaxBertModel.from_pretrained("bert-base-cased"						)
        lowercase_			:str    			= model(np.ones((1, 1)						)						)
        self.assertIsNotNone(lowercase						)


