






import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    SqueezeBertForMaskedLM,
					    SqueezeBertForMultipleChoice,
					    SqueezeBertForQuestionAnswering,
					    SqueezeBertForSequenceClassification,
					    SqueezeBertForTokenClassification,
					    SqueezeBertModel,
					)
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=1_3							,       __lowerCAmelCase=7							,       __lowerCAmelCase=True							,       __lowerCAmelCase=True							,       __lowerCAmelCase=False							,       __lowerCAmelCase=True							,       __lowerCAmelCase=9_9							,       __lowerCAmelCase=3_2							,       __lowerCAmelCase=5							,       __lowerCAmelCase=4							,       __lowerCAmelCase=6_4							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=5_1_2							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=2							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=3							,       __lowerCAmelCase=4							,       __lowerCAmelCase=None							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=2							,       __lowerCAmelCase=4							,       __lowerCAmelCase=1							,       ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   parent
								__magic_name__		:int   =   batch_size
								__magic_name__		:int   =   seq_length
								__magic_name__		:List[Any]   =   is_training
								__magic_name__		:Any   =   use_input_mask
								__magic_name__		:List[Any]   =   use_token_type_ids
								__magic_name__		:Any   =   use_labels
								__magic_name__		:str   =   vocab_size
								__magic_name__		:Tuple   =   hidden_size
								__magic_name__		:List[str]   =   num_hidden_layers
								__magic_name__		:Dict   =   num_attention_heads
								__magic_name__		:str   =   intermediate_size
								__magic_name__		:Optional[int]   =   hidden_act
								__magic_name__		:List[Any]   =   hidden_dropout_prob
								__magic_name__		:List[str]   =   attention_probs_dropout_prob
								__magic_name__		:List[str]   =   max_position_embeddings
								__magic_name__		:Union[str, Any]   =   type_vocab_size
								__magic_name__		:int   =   type_sequence_label_size
								__magic_name__		:Optional[Any]   =   initializer_range
								__magic_name__		:int   =   num_labels
								__magic_name__		:Tuple   =   num_choices
								__magic_name__		:Optional[int]   =   scope
								__magic_name__		:Tuple   =   q_groups
								__magic_name__		:List[str]   =   k_groups
								__magic_name__		:str   =   v_groups
								__magic_name__		:Optional[Any]   =   post_attention_groups
								__magic_name__		:List[str]   =   intermediate_groups
								__magic_name__		:int   =   output_groups







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size  )

								__magic_name__		:List[Any]   =   None
								if self.use_input_mask:
												__magic_name__		:Dict   =   random_attention_mask([self.batch_size, self.seq_length]  )

								__magic_name__		:int   =   None
								__magic_name__		:Optional[Any]   =   None
								__magic_name__		:Union[str, Any]   =   None
								if self.use_labels:
												__magic_name__		:Optional[int]   =   ids_tensor([self.batch_size]							,       self.type_sequence_label_size  )
												__magic_name__		:List[str]   =   ids_tensor([self.batch_size, self.seq_length]							,       self.num_labels  )
												__magic_name__		:Dict   =   ids_tensor([self.batch_size]							,       self.num_choices  )

								__magic_name__		:Optional[int]   =   self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







				def       A						(    self  ):
								"""simple docstring"""
								return SqueezeBertConfig(
								    embedding_size=self.hidden_size							,       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							,       attention_probs_dropout_prob=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       initializer_range=self.initializer_range							,       q_groups=self.q_groups							,       k_groups=self.k_groups							,       v_groups=self.v_groups							,       post_attention_groups=self.post_attention_groups							,       intermediate_groups=self.intermediate_groups							,       output_groups=self.output_groups							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   SqueezeBertModel(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:str   =   model(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   model(__lowerCAmelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   SqueezeBertForMaskedLM(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Optional[int]   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   SqueezeBertForQuestionAnswering(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Union[str, Any]   =   model(
								    __lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       start_positions=__lowerCAmelCase							,       end_positions=__lowerCAmelCase  )
								self.parent.assertEqual(result.start_logits.shape							,       (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape							,       (self.batch_size, self.seq_length)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.num_labels
								__magic_name__		:int   =   SqueezeBertForSequenceClassification(__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Optional[Any]   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.num_labels
								__magic_name__		:Union[str, Any]   =   SqueezeBertForTokenClassification(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()

								__magic_name__		:Tuple   =   model(__lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase  )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels)  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   self.num_choices
								__magic_name__		:Dict   =   SqueezeBertForMultipleChoice(config=__lowerCAmelCase  )
								model.to(__lowerCAmelCase  )
								model.eval()
								__magic_name__		:Any   =   input_ids.unsqueeze(1  ).expand(-1							,       self.num_choices							,       -1  ).contiguous()
								__magic_name__		:Tuple   =   input_mask.unsqueeze(1  ).expand(-1							,       self.num_choices							,       -1  ).contiguous()
								__magic_name__		:Optional[int]   =   model(
								    __lowerCAmelCase							,       attention_mask=__lowerCAmelCase							,       labels=__lowerCAmelCase							,       )
								self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_choices)  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.prepare_config_and_inputs()
								((__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__)  ,					(__magic_name__))		:Tuple   =   config_and_inputs
								__magic_name__		:Dict   =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict







@require_torch
class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				a__       							=	(
				    {
				        '''feature-extraction''': SqueezeBertModel,
				        '''fill-mask''': SqueezeBertForMaskedLM,
				        '''question-answering''': SqueezeBertForQuestionAnswering,
				        '''text-classification''': SqueezeBertForSequenceClassification,
				        '''token-classification''': SqueezeBertForTokenClassification,
				        '''zero-shot''': SqueezeBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				a__       							=	False
				a__       							=	True
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   SqueezeBertModelTester(self  )
								__magic_name__		:Optional[int]   =   ConfigTester(self							,       config_class=__lowerCAmelCase							,       dim=3_7  )







				def       A						(    self  ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_model(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCAmelCase  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:int   =   SqueezeBertModel.from_pretrained(__lowerCAmelCase  )
												self.assertIsNotNone(__lowerCAmelCase  )







@require_sentencepiece
@require_tokenizers
@require_torch
class     lowerCamelCase_					(   unittest.TestCase			):







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''  )

								__magic_name__		:Optional[int]   =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]  )
								__magic_name__		:Tuple   =   model(__lowerCAmelCase  )[0]
								__magic_name__		:Any   =   torch.Size((1, 3)  )
								self.assertEqual(output.shape							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   torch.tensor([[0.6401, -0.0349, -0.6041]]  )
								self.assertTrue(torch.allclose(__lowerCAmelCase							,       __lowerCAmelCase							,       atol=1E-4  )  )













"""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__						= logging.get_logger(__name__)




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							DPTConfig(embedding_type="hybrid"      )

      if "large" in checkpoint_url:
            _UpperCamelCase   :						Optional[Any]    =							1_024
            _UpperCamelCase   :						Optional[int]    =							4_096
            _UpperCamelCase   :						List[Any]    =							24
            _UpperCamelCase   :						List[Any]    =							16
            _UpperCamelCase   :						Optional[Any]    =							[5, 11, 17, 23]
            _UpperCamelCase   :						Tuple    =							[256, 512, 1_024, 1_024]
            _UpperCamelCase   :						str    =							(1, 384, 384)

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

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

      return config, expected_shape




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_      )       ->		int:




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

      if ".." in name:
            _UpperCamelCase   :						List[str]    =							name.replace(".."		,"."      )

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """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   :						Any    =							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''      )
            _UpperCamelCase   :						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
            _UpperCamelCase   :						Tuple    =							in_proj_weight[: config.hidden_size, :]
            _UpperCamelCase   :						Dict    =							in_proj_bias[: config.hidden_size]
            _UpperCamelCase   :						Optional[int]    =							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _UpperCamelCase   :						Dict    =							in_proj_weight[
                -config.hidden_size :, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[-config.hidden_size :]




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Dict    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						str    =							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   :						Optional[int]    =							torch.load(lowercase_		,map_location="cpu"      )
      # remove certain keys
      remove_ignore_keys_(lowercase_      )
      # rename keys
      for key in state_dict.copy().keys():
            _UpperCamelCase   :						Union[str, Any]    =							state_dict.pop(lowercase_      )
            _UpperCamelCase   :						Tuple    =							val
      # read in qkv matrices
      read_in_q_k_v(lowercase_		,lowercase_      )

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

      # Check outputs on an image
      _UpperCamelCase   :						List[Any]    =							480 if "ade" in checkpoint_url else 384
      _UpperCamelCase   :						List[str]    =							DPTImageProcessor(size=lowercase_      )

      _UpperCamelCase   :						int    =							prepare_img()
      _UpperCamelCase   :						List[str]    =							image_processor(lowercase_		,return_tensors="pt"      )

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

      if show_prediction:
            _UpperCamelCase   :						Optional[int]    =							(
                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
  )
