



'''simple docstring'''

import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    NezhaForMaskedLM,
	    NezhaForMultipleChoice,
	    NezhaForNextSentencePrediction,
	    NezhaForPreTraining,
	    NezhaForQuestionAnswering,
	    NezhaForSequenceClassification,
	    NezhaForTokenClassification,
	    NezhaModel,
	)
	from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class 						a_		:


							def __init__(				self   :						List[Any]      ,						a_   :						List[str]      ,						a_   :						Dict=1_3      ,						a_   :						List[str]=7      ,						a_   :						Union[str, Any]=True      ,						a_   :						Tuple=True      ,						a_   :						Optional[int]=True      ,						a_   :						Dict=True      ,						a_   :						Tuple=9_9      ,						a_   :						Any=3_2      ,						a_   :						Tuple=5      ,						a_   :						Optional[int]=4      ,						a_   :						Tuple=3_7      ,						a_   :						Union[str, Any]="gelu"      ,						a_   :						Optional[int]=0.1      ,						a_   :						int=0.1      ,						a_   :						int=1_2_8      ,						a_   :						int=3_2      ,						a_   :						int=1_6      ,						a_   :						str=2      ,						a_   :						Dict=0.0_2      ,						a_   :						Any=3      ,						a_   :						Any=4      ,						a_   :						List[Any]=None      ,						)			->					Tuple:
										snake_case:    Optional[int]      							=parent
										snake_case:    Optional[int]      							=batch_size
										snake_case:    Union[str, Any]      							=seq_length
										snake_case:    int      							=is_training
										snake_case:    Tuple      							=use_input_mask
										snake_case:    int      							=use_token_type_ids
										snake_case:    Any      							=use_labels
										snake_case:    Tuple      							=vocab_size
										snake_case:    Optional[int]      							=hidden_size
										snake_case:    Optional[int]      							=num_hidden_layers
										snake_case:    Union[str, Any]      							=num_attention_heads
										snake_case:    List[Any]      							=intermediate_size
										snake_case:    List[str]      							=hidden_act
										snake_case:    Optional[int]      							=hidden_dropout_prob
										snake_case:    Any      							=attention_probs_dropout_prob
										snake_case:    Tuple      							=max_position_embeddings
										snake_case:    Tuple      							=type_vocab_size
										snake_case:    Dict      							=type_sequence_label_size
										snake_case:    str      							=initializer_range
										snake_case:    Any      							=num_labels
										snake_case:    List[str]      							=num_choices
										snake_case:    Optional[Any]      							=scope


							def    UpperCamelCase     (				self   :						Dict			)			->					List[str]:
										snake_case:    List[str]      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size			)

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

										snake_case:    Dict      							=None
										if self.use_token_type_ids:
													snake_case:    Optional[Any]      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.type_vocab_size			)

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

										snake_case:    Any      							=self.get_config()

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


							def    UpperCamelCase     (				self   :						Dict			)			->					int:
										return NezhaConfig(
										    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      ,						)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[str]:
										(
										    (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  				
										):    Dict      							=self.prepare_config_and_inputs()

										snake_case:    Union[str, Any]      							=True
										snake_case:    List[Any]      							=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,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[Any]      ,						a_   :						Tuple      ,						a_   :						Tuple      ,						a_   :						List[str]      ,						a_   :						str      ,						a_   :						Optional[Any]			)			->					Optional[Any]:
										snake_case:    Optional[int]      							=NezhaModel(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    List[Any]      							=model(__snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case			)
										snake_case:    List[Any]      							=model(__snake_case      ,						token_type_ids=__snake_case			)
										snake_case:    Optional[Any]      							=model(__snake_case			)
										self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)			)
										self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.hidden_size)			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Tuple      ,						a_   :						int      ,						a_   :						Tuple      ,						a_   :						Dict      ,						a_   :						Union[str, Any]      ,						a_   :						Any      ,						a_   :						Optional[int]      ,						a_   :						int      ,						a_   :						List[Any]      ,						)			->					List[Any]:
										snake_case:    Optional[Any]      							=True
										snake_case:    int      							=NezhaModel(__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    List[str]      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						encoder_hidden_states=__snake_case      ,						encoder_attention_mask=__snake_case      ,						)
										snake_case:    Union[str, Any]      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						encoder_hidden_states=__snake_case      ,						)
										snake_case:    Dict      							=model(__snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case			)
										self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)			)
										self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.hidden_size)			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Dict      ,						a_   :						Tuple      ,						a_   :						Union[str, Any]      ,						a_   :						int      ,						a_   :						Dict      ,						a_   :						int      ,						a_   :						List[str]			)			->					Any:
										snake_case:    Optional[Any]      							=NezhaForMaskedLM(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    str      							=model(__snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						int      ,						a_   :						str      ,						a_   :						Optional[int]      ,						a_   :						Any      ,						a_   :						Dict      ,						a_   :						Optional[int]      ,						a_   :						Optional[int]			)			->					Optional[int]:
										snake_case:    Any      							=NezhaForNextSentencePrediction(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    List[str]      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case      ,						)
										self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, 2)			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Optional[Any]      ,						a_   :						Optional[Any]      ,						a_   :						Optional[Any]      ,						a_   :						Tuple      ,						a_   :						Any      ,						a_   :						Tuple      ,						a_   :						Union[str, Any]			)			->					Any:
										snake_case:    Optional[Any]      							=NezhaForPreTraining(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    Tuple      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case      ,						next_sentence_label=__snake_case      ,						)
										self.parent.assertEqual(result.prediction_logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)			)
										self.parent.assertEqual(result.seq_relationship_logits.shape      ,						(self.batch_size, 2)			)


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						List[str]      ,						a_   :						Optional[Any]      ,						a_   :						Union[str, Any]      ,						a_   :						str      ,						a_   :						List[str]      ,						a_   :						Tuple      ,						a_   :						Any			)			->					Optional[Any]:
										snake_case:    List[Any]      							=NezhaForQuestionAnswering(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    Union[str, Any]      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						start_positions=__snake_case      ,						end_positions=__snake_case      ,						)
										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    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Tuple      ,						a_   :						List[Any]      ,						a_   :						Tuple      ,						a_   :						Any      ,						a_   :						str      ,						a_   :						Tuple      ,						a_   :						List[Any]			)			->					List[str]:
										snake_case:    Optional[int]      							=self.num_labels
										snake_case:    Optional[Any]      							=NezhaForSequenceClassification(__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    List[str]      							=model(__snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Dict      ,						a_   :						Any      ,						a_   :						str      ,						a_   :						Optional[Any]      ,						a_   :						Tuple      ,						a_   :						Optional[Any]      ,						a_   :						Tuple			)			->					int:
										snake_case:    List[Any]      							=self.num_labels
										snake_case:    int      							=NezhaForTokenClassification(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    Optional[Any]      							=model(__snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels)			)


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						Optional[int]      ,						a_   :						Optional[int]      ,						a_   :						int      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Tuple      ,						a_   :						Union[str, Any]			)			->					Any:
										snake_case:    Union[str, Any]      							=self.num_choices
										snake_case:    int      							=NezhaForMultipleChoice(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()
										snake_case:    Optional[int]      							=input_ids.unsqueeze(1			).expand(-1      ,						self.num_choices      ,						-1			).contiguous()
										snake_case:    Dict      							=token_type_ids.unsqueeze(1			).expand(-1      ,						self.num_choices      ,						-1			).contiguous()
										snake_case:    List[Any]      							=input_mask.unsqueeze(1			).expand(-1      ,						self.num_choices      ,						-1			).contiguous()
										snake_case:    int      							=model(
										    __snake_case      ,						attention_mask=__snake_case      ,						token_type_ids=__snake_case      ,						labels=__snake_case      ,						)
										self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_choices)			)




							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    str      							=self.prepare_config_and_inputs()
										(
										    (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  				
										):    List[Any]      							=config_and_inputs
										snake_case:    Dict      							={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict






@require_torch
class 						a_		(     snake_case_    ,   snake_case_    ,   snake_case_    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Any        =				(
							    (
							        NezhaModel,
							        NezhaForMaskedLM,
							        NezhaForMultipleChoice,
							        NezhaForNextSentencePrediction,
							        NezhaForPreTraining,
							        NezhaForQuestionAnswering,
							        NezhaForSequenceClassification,
							        NezhaForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase			:		Tuple        =				(
							    {
							        '''feature-extraction''': NezhaModel,
							        '''fill-mask''': NezhaForMaskedLM,
							        '''question-answering''': NezhaForQuestionAnswering,
							        '''text-classification''': NezhaForSequenceClassification,
							        '''token-classification''': NezhaForTokenClassification,
							        '''zero-shot''': NezhaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCAmelCase			:		Optional[int]        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						str      ,						a_   :						Optional[int]      ,						a_   :						List[Any]=False			)			->					str:
										snake_case:    Optional[Any]      							=super()._prepare_for_class(__snake_case      ,						__snake_case      ,						return_labels=__snake_case			)

										if return_labels:
													if model_class in get_values(__snake_case			):
																snake_case:    Union[str, Any]      							=torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.seq_length)      ,						dtype=torch.long      ,						device=__snake_case			)
																snake_case:    List[str]      							=torch.zeros(
																    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=__snake_case			)
										return inputs_dict


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Any:
										snake_case:    Optional[Any]      							=NezhaModelTester(self			)
										snake_case:    List[Any]      							=ConfigTester(self      ,						config_class=__snake_case      ,						hidden_size=3_7			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Any:
										self.config_tester.run_common_tests()


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Optional[Any]:
										snake_case:    Any      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case			)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Tuple:
										snake_case:    Optional[int]      							=self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*__snake_case			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					str:
										# This regression test was failing with PyTorch < 1.3
										(
										    (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  (
										    snake_case				
										)							,  				
										):    Tuple      							=self.model_tester.prepare_config_and_inputs_for_decoder()

										snake_case:    Dict      							=None

										self.model_tester.create_and_check_model_as_decoder(
										    __snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Dict:
										snake_case:    Any      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Tuple:
										snake_case:    List[Any]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*__snake_case			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    List[str]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_next_sequence_prediction(*__snake_case			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Any:
										snake_case:    List[Any]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*__snake_case			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Union[str, Any]:
										snake_case:    List[Any]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__snake_case			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Dict      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__snake_case			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										snake_case:    Union[str, Any]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__snake_case			)


							@slow
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case:    Optional[int]      							=NezhaModel.from_pretrained(__snake_case			)
													self.assertIsNotNone(__snake_case			)




							@slow
							@require_torch_gpu
							def    UpperCamelCase     (				self   :						str			)			->					Optional[Any]:
										snake_case							,  snake_case:    Optional[int]      							=self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													# NezhaForMultipleChoice behaves incorrectly in JIT environments.
													if model_class == NezhaForMultipleChoice:
																return

													snake_case:    List[Any]      							=True
													snake_case:    Optional[int]      							=model_class(config=__snake_case			)

													snake_case:    Optional[Any]      							=self._prepare_for_class(__snake_case      ,						__snake_case			)
													snake_case:    Tuple      							=torch.jit.trace(
													    __snake_case      ,						(inputs_dict['input_ids'].to('cpu'			), inputs_dict['attention_mask'].to('cpu'			))			)

													with tempfile.TemporaryDirectory() as tmp:
																torch.jit.save(__snake_case      ,						os.path.join(__snake_case      ,						'bert.pt'			)			)
																snake_case:    int      							=torch.jit.load(os.path.join(__snake_case      ,						'bert.pt'			)      ,						map_location=__snake_case			)
																loaded(inputs_dict['input_ids'].to(__snake_case			)      ,						inputs_dict['attention_mask'].to(__snake_case			)			)






@require_torch
class 						a_		(     unittest.TestCase    ):


							@slow
							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Optional[Any]      							=NezhaModel.from_pretrained('sijunhe/nezha-cn-base'			)
										snake_case:    Union[str, Any]      							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
										snake_case:    Union[str, Any]      							=torch.tensor([[0, 1, 1, 1, 1, 1]]			)
										with torch.no_grad():
													snake_case:    Any      							=model(__snake_case      ,						attention_mask=__snake_case			)[0]
										snake_case:    Optional[int]      							=torch.Size((1, 6, 7_6_8)			)
										self.assertEqual(output.shape      ,						__snake_case			)
										snake_case:    Dict      							=torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]			)

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,						__snake_case      ,						atol=1E-4			)			)




							@slow
							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    int      							=NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'			)
										snake_case:    List[Any]      							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
										snake_case:    List[str]      							=torch.tensor([[1, 1, 1, 1, 1, 1]]			)
										with torch.no_grad():
													snake_case:    Tuple      							=model(__snake_case      ,						attention_mask=__snake_case			)[0]
										snake_case:    Any      							=torch.Size((1, 6, 2_1_1_2_8)			)
										self.assertEqual(output.shape      ,						__snake_case			)
										snake_case:    Tuple      							=torch.tensor(
										    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]			)

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,						__snake_case      ,						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()
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)






