






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE__      :			Dict							=		{"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class 						lowerCamelCase_				(      unittest.TestCase ):
				a__					       =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				a__					       =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

				if model_mapping is not None:
								a__					       =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
				if tf_model_mapping is not None:
								a__					       =  {
								    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
								}







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      ZeroShotClassificationPipeline(
								    model=lowerCAmelCase__       ,					tokenizer=lowerCAmelCase__       ,					candidate_labels=['''polics''', '''health''']     )
								return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Any        =      classifier('''Who are you voting for in 2020?'''       ,					candidate_labels='''politics'''     )
								self.assertEqual(lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     )]}     )

								# No kwarg
								__magic_name__		:Optional[Any]        =      classifier('''Who are you voting for in 2020?'''       ,					['''politics''']     )
								self.assertEqual(lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     )]}     )

								__magic_name__		:Union[str, Any]        =      classifier('''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''']     )
								self.assertEqual(lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     )]}     )

								__magic_name__		:int        =      classifier('''Who are you voting for in 2020?'''       ,					candidate_labels='''politics, public health'''     )
								self.assertEqual(
								    lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )]}     )
								self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']     )     )       ,					1.0     )

								__magic_name__		:List[str]        =      classifier('''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''', '''public health''']     )
								self.assertEqual(
								    lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )]}     )
								self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']     )     )       ,					1.0     )

								__magic_name__		:Optional[int]        =      classifier(
								    '''Who are you voting for in 2020?'''       ,					candidate_labels='''politics'''       ,					hypothesis_template='''This text is about {}'''     )
								self.assertEqual(lowerCAmelCase__       ,					{'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     )]}     )

								# https://github.com/huggingface/transformers/issues/13846
								__magic_name__		:Any        =      classifier(['''I am happy''']       ,					['''positive''', '''negative''']     )
								self.assertEqual(
								    lowerCAmelCase__       ,					[
								        {'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )]}
								        for i in range(1     )
								    ]       ,					)
								__magic_name__		:Optional[int]        =      classifier(['''I am happy''', '''I am sad''']       ,					['''positive''', '''negative''']     )
								self.assertEqual(
								    lowerCAmelCase__       ,					[
								        {'''sequence''': ANY(lowerCAmelCase__     ), '''labels''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )], '''scores''': [ANY(lowerCAmelCase__     ), ANY(lowerCAmelCase__     )]}
								        for i in range(2     )
								    ]       ,					)

								with self.assertRaises(lowerCAmelCase__     ):
												classifier(''''''       ,					candidate_labels='''politics'''     )

								with self.assertRaises(lowerCAmelCase__     ):
												classifier(lowerCAmelCase__       ,					candidate_labels='''politics'''     )

								with self.assertRaises(lowerCAmelCase__     ):
												classifier('''Who are you voting for in 2020?'''       ,					candidate_labels=''''''     )

								with self.assertRaises(lowerCAmelCase__     ):
												classifier('''Who are you voting for in 2020?'''       ,					candidate_labels=lowerCAmelCase__     )

								with self.assertRaises(lowerCAmelCase__     ):
												classifier(
												    '''Who are you voting for in 2020?'''       ,					candidate_labels='''politics'''       ,					hypothesis_template='''Not formatting template'''       ,					)

								with self.assertRaises(lowerCAmelCase__     ):
												classifier(
												    '''Who are you voting for in 2020?'''       ,					candidate_labels='''politics'''       ,					hypothesis_template=lowerCAmelCase__       ,					)

								self.run_entailment_id(lowerCAmelCase__     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      zero_shot_classifier.model.config
								__magic_name__		:Optional[int]        =      config.labelaid
								__magic_name__		:Tuple        =      zero_shot_classifier.entailment_id

								__magic_name__		:Optional[int]        =      {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
								self.assertEqual(zero_shot_classifier.entailment_id       ,					-1     )

								__magic_name__		:int        =      {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
								self.assertEqual(zero_shot_classifier.entailment_id       ,					0     )

								__magic_name__		:List[str]        =      {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
								self.assertEqual(zero_shot_classifier.entailment_id       ,					0     )

								__magic_name__		:str        =      {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
								self.assertEqual(zero_shot_classifier.entailment_id       ,					2     )

								__magic_name__		:Optional[Any]        =      original_labelaid
								self.assertEqual(lowerCAmelCase__       ,					zero_shot_classifier.entailment_id     )







				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      pipeline(
								    '''zero-shot-classification'''       ,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''       ,					framework='''pt'''       ,					)
								# There was a regression in 4.10 for this
								# Adding a test so we don't make the mistake again.
								# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
								zero_shot_classifier(
								    '''Who are you voting for in 2020?''' * 1_0_0       ,					candidate_labels=['''politics''', '''public health''', '''science''']     )







				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      pipeline(
								    '''zero-shot-classification'''       ,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''       ,					framework='''pt'''       ,					)
								__magic_name__		:Optional[int]        =      zero_shot_classifier(
								    '''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''', '''public health''', '''science''']     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''science''', '''public health''', '''politics'''],
								        '''scores''': [0.333, 0.333, 0.333],
								    }       ,					)







				@require_tf
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      pipeline(
								    '''zero-shot-classification'''       ,					model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''       ,					framework='''tf'''       ,					)
								__magic_name__		:List[Any]        =      zero_shot_classifier(
								    '''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''', '''public health''', '''science''']     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''science''', '''public health''', '''politics'''],
								        '''scores''': [0.333, 0.333, 0.333],
								    }       ,					)







				@slow
				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      pipeline('''zero-shot-classification'''       ,					model='''roberta-large-mnli'''       ,					framework='''pt'''     )
								__magic_name__		:Any        =      zero_shot_classifier(
								    '''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''', '''public health''', '''science''']     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''politics''', '''public health''', '''science'''],
								        '''scores''': [0.976, 0.015, 0.009],
								    }       ,					)
								__magic_name__		:Any        =      zero_shot_classifier(
								    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
								    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
								    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
								    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
								    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
								    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
								    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
								    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
								    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
								    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
								    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
								    ''' large and limited training data.'''       ,					candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']       ,					multi_label=lowerCAmelCase__       ,					)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': (
								            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
								            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
								            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
								            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
								            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
								            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
								            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
								            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
								            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
								            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
								            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
								            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
								            ''' limited training data.'''
								        ),
								        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
								        '''scores''': [0.817, 0.713, 0.018, 0.018],
								    }       ,					)







				@slow
				@require_tf
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      pipeline('''zero-shot-classification'''       ,					model='''roberta-large-mnli'''       ,					framework='''tf'''     )
								__magic_name__		:Union[str, Any]        =      zero_shot_classifier(
								    '''Who are you voting for in 2020?'''       ,					candidate_labels=['''politics''', '''public health''', '''science''']     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''politics''', '''public health''', '''science'''],
								        '''scores''': [0.976, 0.015, 0.009],
								    }       ,					)
								__magic_name__		:Optional[Any]        =      zero_shot_classifier(
								    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
								    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
								    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
								    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
								    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
								    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
								    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
								    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
								    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
								    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
								    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
								    ''' large and limited training data.'''       ,					candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']       ,					multi_label=lowerCAmelCase__       ,					)
								self.assertEqual(
								    nested_simplify(lowerCAmelCase__     )       ,					{
								        '''sequence''': (
								            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
								            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
								            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
								            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
								            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
								            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
								            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
								            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
								            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
								            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
								            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
								            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
								            ''' limited training data.'''
								        ),
								        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
								        '''scores''': [0.817, 0.713, 0.018, 0.018],
								    }       ,					)














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()
SCREAMING_SNAKE_CASE__      :			List[str]							=		logging.get_logger(__name__)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      DPTConfig()

				if "large" in checkpoint_url:
								__magic_name__		:str        =      1_0_2_4
								__magic_name__		:Tuple        =      4_0_9_6
								__magic_name__		:Union[str, Any]        =      2_4
								__magic_name__		:int        =      1_6
								__magic_name__		:Dict        =      [5, 1_1, 1_7, 2_3]
								__magic_name__		:Optional[int]        =      [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								__magic_name__		:Union[str, Any]        =      (1, 3_8_4, 3_8_4)

				if "ade" in checkpoint_url:
								__magic_name__		:Tuple        =      True

								__magic_name__		:int        =      1_5_0
								__magic_name__		:List[Any]        =      '''huggingface/label-files'''
								__magic_name__		:Dict        =      '''ade20k-id2label.json'''
								__magic_name__		:str        =      json.load(open(cached_download(hf_hub_url(snake_case,  snake_case,  repo_type='''dataset''' ) ),  '''r''' ) )
								__magic_name__		:Optional[int]        =      {int(snake_case ): v for k, v in idalabel.items()}
								__magic_name__		:Any        =      idalabel
								__magic_name__		:List[str]        =      {v: k for k, v in idalabel.items()}
								__magic_name__		:Optional[Any]        =      [1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case,  snake_case )

def      __lowercase					( snake_case ):







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

				return name

def      __lowercase					( snake_case,  snake_case ):







				"""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)
								__magic_name__		:int        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
								__magic_name__		:Union[str, Any]        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__		:Optional[Any]        =      in_proj_weight[: config.hidden_size, :]
								__magic_name__		:str        =      in_proj_bias[: config.hidden_size]
								__magic_name__		:List[str]        =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__		:Optional[Any]        =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__		:int        =      in_proj_weight[
								    -config.hidden_size :, :
								]
								__magic_name__		:Union[str, Any]        =      in_proj_bias[-config.hidden_size :]

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:Union[str, Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )
				return im

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Any        =      get_dpt_config(snake_case )
				# load original state_dict from URL
				__magic_name__		:List[str]        =      torch.hub.load_state_dict_from_url(snake_case,  map_location='''cpu''' )
				# remove certain keys
				remove_ignore_keys_(snake_case )
				# rename keys
				for key in state_dict.copy().keys():
								__magic_name__		:int        =      state_dict.pop(snake_case )
								__magic_name__		:Tuple        =      val
				# read in qkv matrices
				read_in_q_k_v(snake_case,  snake_case )

				# load HuggingFace model
				__magic_name__		:int        =      DPTForSemanticSegmentation(snake_case ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case )
				model.load_state_dict(snake_case )
				model.eval()

				# Check outputs on an image
				__magic_name__		:Union[str, Any]        =      4_8_0 if '''ade''' in checkpoint_url else 3_8_4
				__magic_name__		:int        =      DPTImageProcessor(size=snake_case )

				__magic_name__		:Dict        =      prepare_img()
				__magic_name__		:List[str]        =      image_processor(snake_case,  return_tensors='''pt''' )

				# forward pass
				__magic_name__		:Dict        =      model(**snake_case ).logits if '''ade''' in checkpoint_url else model(**snake_case ).predicted_depth

				# Assert logits
				__magic_name__		:int        =      torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
				if "ade" in checkpoint_url:
								__magic_name__		:Any        =      torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
				assert outputs.shape == torch.Size(snake_case )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3],  snake_case,  atol=1E-4 )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3],  snake_case )
				)

				Path(snake_case ).mkdir(exist_ok=snake_case )
				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(snake_case )
				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(snake_case )

				if push_to_hub:
								print('''Pushing model to hub...''' )
								model.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add model''',  use_temp_dir=snake_case,  )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add image processor''',  use_temp_dir=snake_case,  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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.""",
					)

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






