





'''simple docstring'''






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
      from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


A__							:  List[Any]    =get_tests_dir('''fixtures/test_sentencepiece.model''')

A__							:  Union[str, Any]    ={'''target_lang''': '''fi''', '''source_lang''': '''en'''}
A__							:  List[Any]    ='''>>zh<<'''
A__							:  Dict    ='''Helsinki-NLP/'''

if is_torch_available():
      A__							:  Tuple    ='''pt'''
elif is_tf_available():
      A__							:  Union[str, Any]    ='''tf'''
else:
      A__							:  Any    ='''jax'''




@require_sentencepiece
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Union[str, Any]			=  MarianTokenizer
      _lowercase:     List[Any]			=  False
      _lowercase:     Tuple			=  True




      def 							lowercase__       (     self     :					Tuple    )							-> Tuple:
            super().setUp()
            _lowerCAmelCase					=				["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				Path(self.tmpdirname    )
            save_json(__snake_case     ,     save_dir / VOCAB_FILES_NAMES["""vocab"""]    )
            save_json(__snake_case     ,     save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]    )
            if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                  copyfile(__snake_case     ,     save_dir / VOCAB_FILES_NAMES["""source_spm"""]    )
                  copyfile(__snake_case     ,     save_dir / VOCAB_FILES_NAMES["""target_spm"""]    )

            _lowerCAmelCase					=				MarianTokenizer.from_pretrained(self.tmpdirname    )
            tokenizer.save_pretrained(self.tmpdirname    )




      def 							lowercase__       (     self     :					Dict     ,     **__snake_case     :					str    )							-> MarianTokenizer:
            return MarianTokenizer.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					str    )							-> int:
            return (
                "This is a test",
                "This is a test",
            )




      def 							lowercase__       (     self     :					List[str]    )							-> List[str]:
            _lowerCAmelCase					=				"""</s>"""
            _lowerCAmelCase					=				0

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case    )     ,     __snake_case    )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            _lowerCAmelCase					=				list(self.get_tokenizer().get_vocab().keys()    )

            self.assertEqual(vocab_keys[0]     ,     """</s>"""    )
            self.assertEqual(vocab_keys[1]     ,     """<unk>"""    )
            self.assertEqual(vocab_keys[-1]     ,     """<pad>"""    )
            self.assertEqual(len(__snake_case    )     ,     9    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Any:
            self.assertEqual(self.get_tokenizer().vocab_size     ,     9    )




      def 							lowercase__       (     self     :					List[Any]    )							-> str:
            _lowerCAmelCase					=				MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de"    )
            _lowerCAmelCase					=				en_de_tokenizer(["""I am a small frog"""]     ,     return_tensors=__snake_case    )
            self.assertIsInstance(__snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				[38, 1_21, 14, 6_97, 3_88_48, 0]
            self.assertListEqual(__snake_case     ,     batch.input_ids[0]    )

            _lowerCAmelCase					=				tempfile.mkdtemp()
            en_de_tokenizer.save_pretrained(__snake_case    )
            _lowerCAmelCase					=				[x.name for x in Path(__snake_case    ).glob("""*"""    )]
            self.assertIn("""source.spm"""     ,     __snake_case    )
            MarianTokenizer.from_pretrained(__snake_case    )




      def 							lowercase__       (     self     :					str    )							-> Optional[int]:
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				tok(
                ["""I am a small frog""" * 10_00, """I am a small frog"""]     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors=__snake_case    )
            self.assertIsInstance(__snake_case     ,     __snake_case    )
            self.assertEqual(batch.input_ids.shape     ,     (2, 5_12)    )




      def 							lowercase__       (     self     :					List[str]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tok(["""I am a tiny frog""", """I am a small frog"""]     ,     padding=__snake_case     ,     return_tensors=__snake_case    )
            self.assertIsInstance(__snake_case     ,     __snake_case    )
            self.assertEqual(batch_smaller.input_ids.shape     ,     (2, 10)    )




      @slow
      def 							lowercase__       (     self     :					Dict    )							-> Dict:
            # fmt: off
            _lowerCAmelCase					=				{"""input_ids""": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=__snake_case     ,     model_name="""Helsinki-NLP/opus-mt-en-de"""     ,     revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""     ,     decode_kwargs={"""use_source_tokenizer""": True}     ,     )




      def 							lowercase__       (     self     :					Tuple    )							-> str:
            _lowerCAmelCase					=				MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""    )

            _lowerCAmelCase					=				"""Tämä on testi"""
            _lowerCAmelCase					=				"""This is a test"""

            _lowerCAmelCase					=				[76, 7, 20_47, 2]
            _lowerCAmelCase					=				[69, 12, 11, 9_40, 2]

            _lowerCAmelCase					=				tokenizer(__snake_case    ).input_ids
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokenizer(text_target=__snake_case    ).input_ids
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    )
            self.assertEqual(__snake_case     ,     __snake_case    )













'''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()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				DPTConfig()

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				[5, 11, 17, 23]
            _lowerCAmelCase					=				[2_56, 5_12, 10_24, 10_24]
            _lowerCAmelCase					=				(1, 3_84, 3_84)

      if "ade" in checkpoint_url:
            _lowerCAmelCase					=				True

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

      return config, expected_shape
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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





      """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
      ):
            _lowerCAmelCase					=				name.replace("""pretrained.model"""				,      """dpt.encoder"""    )
      if "pretrained.model" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.model"""				,      """dpt.embeddings"""    )
      if "patch_embed" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed"""				,      """patch_embeddings"""    )
      if "pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """position_embeddings"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "proj" in name and "project" not in name:
            _lowerCAmelCase					=				name.replace("""proj"""				,      """projection"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """layer"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "scratch.output_conv" in name:
            _lowerCAmelCase					=				name.replace("""scratch.output_conv"""				,      """head"""    )
      if "scratch" in name:
            _lowerCAmelCase					=				name.replace("""scratch"""				,      """neck"""    )
      if "layer1_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer1_rn"""				,      """convs.0"""    )
      if "layer2_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer2_rn"""				,      """convs.1"""    )
      if "layer3_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer3_rn"""				,      """convs.2"""    )
      if "layer4_rn" in name:
            _lowerCAmelCase					=				name.replace("""layer4_rn"""				,      """convs.3"""    )
      if "refinenet" in name:
            _lowerCAmelCase					=				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
            _lowerCAmelCase					=				name.replace(f"refinenet{layer_idx}"				,      f"fusion_stage.layers.{abs(layer_idx-4    )}"    )
      if "out_conv" in name:
            _lowerCAmelCase					=				name.replace("""out_conv"""				,      """projection"""    )
      if "resConfUnit1" in name:
            _lowerCAmelCase					=				name.replace("""resConfUnit1"""				,      """residual_layer1"""    )
      if "resConfUnit2" in name:
            _lowerCAmelCase					=				name.replace("""resConfUnit2"""				,      """residual_layer2"""    )
      if "conv1" in name:
            _lowerCAmelCase					=				name.replace("""conv1"""				,      """convolution1"""    )
      if "conv2" in name:
            _lowerCAmelCase					=				name.replace("""conv2"""				,      """convolution2"""    )
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.0.project.0"""				,      """neck.reassemble_stage.readout_projects.0.0"""    )
      if "pretrained.act_postprocess2.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.0.project.0"""				,      """neck.reassemble_stage.readout_projects.1.0"""    )
      if "pretrained.act_postprocess3.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess3.0.project.0"""				,      """neck.reassemble_stage.readout_projects.2.0"""    )
      if "pretrained.act_postprocess4.0.project.0" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.0.project.0"""				,      """neck.reassemble_stage.readout_projects.3.0"""    )
      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.3"""				,      """neck.reassemble_stage.layers.0.projection"""    )
      if "pretrained.act_postprocess1.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess1.4"""				,      """neck.reassemble_stage.layers.0.resize"""    )
      if "pretrained.act_postprocess2.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.3"""				,      """neck.reassemble_stage.layers.1.projection"""    )
      if "pretrained.act_postprocess2.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess2.4"""				,      """neck.reassemble_stage.layers.1.resize"""    )
      if "pretrained.act_postprocess3.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess3.3"""				,      """neck.reassemble_stage.layers.2.projection"""    )
      if "pretrained.act_postprocess4.3" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.3"""				,      """neck.reassemble_stage.layers.3.projection"""    )
      if "pretrained.act_postprocess4.4" in name:
            _lowerCAmelCase					=				name.replace("""pretrained.act_postprocess4.4"""				,      """neck.reassemble_stage.layers.3.resize"""    )
      if "pretrained" in name:
            _lowerCAmelCase					=				name.replace("""pretrained"""				,      """dpt"""    )
      if "bn" in name:
            _lowerCAmelCase					=				name.replace("""bn"""				,      """batch_norm"""    )
      if "head" in name:
            _lowerCAmelCase					=				name.replace("""head"""				,      """head.head"""    )
      if "encoder.norm" in name:
            _lowerCAmelCase					=				name.replace("""encoder.norm"""				,      """layernorm"""    )
      if "auxlayer" in name:
            _lowerCAmelCase					=				name.replace("""auxlayer"""				,      """auxiliary_head.head"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """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)
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"    )
            _lowerCAmelCase					=				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"    )
            # next, add query, keys and values (in that order) to the state dict
            _lowerCAmelCase					=				in_proj_weight[: config.hidden_size, :]
            _lowerCAmelCase					=				in_proj_bias[: config.hidden_size]
            _lowerCAmelCase					=				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCAmelCase					=				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCAmelCase					=				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCAmelCase					=				in_proj_bias[-config.hidden_size :]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				get_dpt_config(lowerCAmelCase    )
      # load original state_dict from URL
      _lowerCAmelCase					=				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():
            _lowerCAmelCase					=				state_dict.pop(lowerCAmelCase    )
            _lowerCAmelCase					=				val
      # read in qkv matrices
      read_in_q_k_v(lowerCAmelCase				,      lowerCAmelCase    )

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

      # Check outputs on an image
      _lowerCAmelCase					=				4_80 if """ade""" in checkpoint_url else 3_84
      _lowerCAmelCase					=				DPTImageProcessor(size=lowerCAmelCase    )

      _lowerCAmelCase					=				prepare_img()
      _lowerCAmelCase					=				image_processor(lowerCAmelCase				,      return_tensors="""pt"""    )

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

      # Assert logits
      _lowerCAmelCase					=				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:
            _lowerCAmelCase					=				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__":
      A__							:  Optional[int]    =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.''',
      )

      A__							:  Optional[int]    =parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






