


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :Tuple   			=							logging.get_logger(__name__)

def        lowerCAmelCase__ (			a__:		Optional[Any]       )  ->		int:


							'''simple docstring'''





							if "resnet-50" in model_name:
														_UpperCAmelCase 		=			ResNetConfig.from_pretrained('microsoft/resnet-50'       )
							elif "resnet-101" in model_name:
														_UpperCAmelCase 		=			ResNetConfig.from_pretrained('microsoft/resnet-101'       )
							else:
														raise ValueError('Model name should include either resnet50 or resnet101'       )

							_UpperCAmelCase 		=			DetrConfig(use_timm_backbone=lowerCamelCase_			,  backbone_config=lowerCamelCase_       )

							# set label attributes
							_UpperCAmelCase 		=			'panoptic' in model_name
							if is_panoptic:
														_UpperCAmelCase 		=			2_5_0
							else:
														_UpperCAmelCase 		=			9_1
														_UpperCAmelCase 		=			'huggingface/label-files'
														_UpperCAmelCase 		=			'coco-detection-id2label.json'
														_UpperCAmelCase 		=			json.load(open(hf_hub_download(lowerCamelCase_			,  lowerCamelCase_			,  repo_type='dataset'       )			,  'r'       )       )
														_UpperCAmelCase 		=			{int(lowerCamelCase_       ): v for k, v in idalabel.items()}
														_UpperCAmelCase 		=			idalabel
														_UpperCAmelCase 		=			{v: k for k, v in idalabel.items()}

							return config, is_panoptic

def        lowerCAmelCase__ (			a__:		Optional[Any]       )  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase 		=			[]

							# stem
							# fmt: off
							rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')       )
							rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')       )
							rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')       )
							rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')       )
							rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')       )
							# stages
							for stage_idx in range(len(config.backbone_config.depths       )       ):
														for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																												    )       )
																					# 3 convs
																					for i in range(3       ):
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																												    )       )
																												rename_keys.append(
																												    (
																												        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																												        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																												    )       )
    # fmt: on

							for i in range(config.encoder_layers       ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
														        F'''encoder.layers.{i}.self_attn.out_proj.weight''',
														    )       )
														rename_keys.append(
														    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')       )
														rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')       )
														rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')       )
														rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')       )
														rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')       )
														rename_keys.append(
														    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')       )
														rename_keys.append(
														    (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')       )
														rename_keys.append(
														    (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')       )
														rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')       )
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
														        F'''decoder.layers.{i}.self_attn.out_proj.weight''',
														    )       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')       )
														rename_keys.append(
														    (
														        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
														        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
														    )       )
														rename_keys.append(
														    (
														        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
														        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
														    )       )
														rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')       )
														rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')       )
														rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')       )
														rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')       )
														rename_keys.append(
														    (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')       )
														rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')       )

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('input_proj.weight', 'input_projection.weight'),
							        ('input_proj.bias', 'input_projection.bias'),
							        ('query_embed.weight', 'query_position_embeddings.weight'),
							        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
							        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
							        ('class_embed.weight', 'class_labels_classifier.weight'),
							        ('class_embed.bias', 'class_labels_classifier.bias'),
							        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
							        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
							        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
							        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
							        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
							        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
							    ]       )

							return rename_keys

def        lowerCAmelCase__ (			a__:		Any			,  a__:		Optional[int]			,  a__:		Optional[Any]       )  ->		Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase 		=			state_dict.pop(lowerCamelCase_       )
							_UpperCAmelCase 		=			val

def        lowerCAmelCase__ (			a__:		str			,  a__:		Union[str, Any]=False       )  ->		int:


							'''simple docstring'''





							_UpperCAmelCase 		=			''
							if is_panoptic:
														_UpperCAmelCase 		=			'detr.'

							# first: transformer encoder
							for i in range(6       ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														_UpperCAmelCase 		=			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
														_UpperCAmelCase 		=			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''       )
														# next, add query, keys and values (in that order) to the state dict
														_UpperCAmelCase 		=			in_proj_weight[:2_5_6, :]
														_UpperCAmelCase 		=			in_proj_bias[:2_5_6]
														_UpperCAmelCase 		=			in_proj_weight[2_5_6:5_1_2, :]
														_UpperCAmelCase 		=			in_proj_bias[2_5_6:5_1_2]
														_UpperCAmelCase 		=			in_proj_weight[-2_5_6:, :]
														_UpperCAmelCase 		=			in_proj_bias[-2_5_6:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6       ):
														# read in weights + bias of input projection layer of self-attention
														_UpperCAmelCase 		=			state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''       )
														_UpperCAmelCase 		=			state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''       )
														# next, add query, keys and values (in that order) to the state dict
														_UpperCAmelCase 		=			in_proj_weight[:2_5_6, :]
														_UpperCAmelCase 		=			in_proj_bias[:2_5_6]
														_UpperCAmelCase 		=			in_proj_weight[2_5_6:5_1_2, :]
														_UpperCAmelCase 		=			in_proj_bias[2_5_6:5_1_2]
														_UpperCAmelCase 		=			in_proj_weight[-2_5_6:, :]
														_UpperCAmelCase 		=			in_proj_bias[-2_5_6:]
														# read in weights + bias of input projection layer of cross-attention
														_UpperCAmelCase 		=			state_dict.pop(
														    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''       )
														_UpperCAmelCase 		=			state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''       )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														_UpperCAmelCase 		=			in_proj_weight_cross_attn[:2_5_6, :]
														_UpperCAmelCase 		=			in_proj_bias_cross_attn[:2_5_6]
														_UpperCAmelCase 		=			in_proj_weight_cross_attn[2_5_6:5_1_2, :]
														_UpperCAmelCase 		=			in_proj_bias_cross_attn[2_5_6:5_1_2]
														_UpperCAmelCase 		=			in_proj_weight_cross_attn[-2_5_6:, :]
														_UpperCAmelCase 		=			in_proj_bias_cross_attn[-2_5_6:]

def        lowerCAmelCase__ (			)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase 		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
							_UpperCAmelCase 		=			Image.open(requests.get(lowerCamelCase_			,  stream=lowerCamelCase_       ).raw       )

							return im

@torch.no_grad()
def        lowerCAmelCase__ (			a__:		Optional[Any]			,  a__:		Any=None			,  a__:		Dict=False       )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase 		=			get_detr_config(lowerCamelCase_       )

							# load original model from torch hub
							_UpperCAmelCase 		=			{
							    'detr-resnet-50': 'detr_resnet50',
							    'detr-resnet-101': 'detr_resnet101',
							}
							logger.info(F'''Converting model {model_name}...'''       )
							_UpperCAmelCase 		=			torch.hub.load('facebookresearch/detr'			,  model_name_to_original_name[model_name]			,  pretrained=lowerCamelCase_       ).eval()
							_UpperCAmelCase 		=			detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(lowerCamelCase_       ):
														if is_panoptic:
																					_UpperCAmelCase 		=			'detr.' + src
														rename_key(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_       )
							# query, key and value matrices need special treatment
							read_in_q_k_v(lowerCamelCase_			,  is_panoptic=lowerCamelCase_       )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							_UpperCAmelCase 		=			'detr.model.' if is_panoptic else 'model.'
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('detr'       )
																					    and not key.startswith('class_labels_classifier'       )
																					    and not key.startswith('bbox_predictor'       )
																					):
																												_UpperCAmelCase 		=			state_dict.pop(lowerCamelCase_       )
																												_UpperCAmelCase 		=			val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												_UpperCAmelCase 		=			state_dict.pop(lowerCamelCase_       )
																												_UpperCAmelCase 		=			val
																					elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
																												continue
																					else:
																												_UpperCAmelCase 		=			state_dict.pop(lowerCamelCase_       )
																												_UpperCAmelCase 		=			val
														else:
																					if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
																												_UpperCAmelCase 		=			state_dict.pop(lowerCamelCase_       )
																												_UpperCAmelCase 		=			val

    # finally, create HuggingFace model and load state dict
							_UpperCAmelCase 		=			DetrForSegmentation(lowerCamelCase_       ) if is_panoptic else DetrForObjectDetection(lowerCamelCase_       )
							model.load_state_dict(lowerCamelCase_       )
							model.eval()

							# verify our conversion on an image
							_UpperCAmelCase 		=			'coco_panoptic' if is_panoptic else 'coco_detection'
							_UpperCAmelCase 		=			DetrImageProcessor(format=lowerCamelCase_       )

							_UpperCAmelCase 		=			processor(images=prepare_img()			,  return_tensors='pt'       )
							_UpperCAmelCase 		=			encoding['pixel_values']

							_UpperCAmelCase 		=			detr(lowerCamelCase_       )
							_UpperCAmelCase 		=			model(lowerCamelCase_       )

							assert torch.allclose(outputs.logits			,  original_outputs['pred_logits']			,  atol=1e-3       )
							assert torch.allclose(outputs.pred_boxes			,  original_outputs['pred_boxes']			,  atol=1e-3       )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks			,  original_outputs['pred_masks']			,  atol=1e-4       )
							print('Looks ok!'       )

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
														Path(lowerCamelCase_       ).mkdir(exist_ok=lowerCamelCase_       )
														model.save_pretrained(lowerCamelCase_       )
														processor.save_pretrained(lowerCamelCase_       )

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('Uploading PyTorch model and image processor to the hub...'       )
														model.push_to_hub(F'''nielsr/{model_name}'''       )
														processor.push_to_hub(F'''nielsr/{model_name}'''       )


if __name__ == "__main__":
		lowerCAmelCase__  :Dict   			=							argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''detr-resnet-50''',
		    type=str,
		    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
		    help='''Name of the DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
		lowerCAmelCase__  :List[str]   			=							parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch

 from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor






class _lowerCamelCase(						_a   ):
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.config_class(**self.inputs_dict)
           self.parent.assertTrue(hasattr(lowerCamelCase,      'width_multiplier'))






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=64,      lowerCamelCase=2,      lowerCamelCase=3,      lowerCamelCase="swish",      lowerCamelCase=3,      lowerCamelCase=32,      lowerCamelCase=0.1,      lowerCamelCase=0.0_2,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=10,      lowerCamelCase=None,      lowerCamelCase=0.2_5,      lowerCamelCase=0.0,      lowerCamelCase=0.0,      )       ->   Any:




           """simple docstring"""

           _lowercase       :							Any      		=		parent
           _lowercase       :							Optional[int]      		=		batch_size
           _lowercase       :							Dict      		=		image_size
           _lowercase       :							str      		=		patch_size
           _lowercase       :							Optional[int]      		=		num_channels
           _lowercase       :							Optional[Any]      		=		make_divisible(5_12 * width_multiplier,      divisor=8)
           _lowercase       :							str      		=		hidden_act
           _lowercase       :							Dict      		=		conv_kernel_size
           _lowercase       :							int      		=		output_stride
           _lowercase       :							Optional[Any]      		=		classifier_dropout_prob
           _lowercase       :							Tuple      		=		use_labels
           _lowercase       :							int      		=		is_training
           _lowercase       :							Optional[Any]      		=		num_labels
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							List[str]      		=		scope
           _lowercase       :							Tuple      		=		width_multiplier
           _lowercase       :							List[str]      		=		ffn_dropout
           _lowercase       :							Dict      		=		attn_dropout
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase       :							Dict      		=		None
           _lowercase       :							Optional[int]      		=		None
           if self.use_labels:
               _lowercase       :							Optional[Any]      		=		ids_tensor([self.batch_size],      self.num_labels)
               _lowercase       :							str      		=		ids_tensor([self.batch_size, self.image_size, self.image_size],      self.num_labels)

           _lowercase       :							Union[str, Any]      		=		self.get_config()

           return config, pixel_values, labels, pixel_labels
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return MobileViTVaConfig(
               image_size=self.image_size,      patch_size=self.patch_size,      num_channels=self.num_channels,      hidden_act=self.hidden_act,      conv_kernel_size=self.conv_kernel_size,      output_stride=self.output_stride,      classifier_dropout_prob=self.classifier_dropout_prob,      initializer_range=self.initializer_range,      width_multiplier=self.width_multiplier,      ffn_dropout=self.ffn_dropout_prob,      attn_dropout=self.attn_dropout_prob,      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		MobileViTVaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[int]      		=		model(lowerCamelCase)
           self.parent.assertEqual(
               result.last_hidden_state.shape,      (
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               ),      )
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		self.num_labels
           _lowercase       :							Optional[int]      		=		MobileViTVaForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		model(lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   int:




           """simple docstring"""

           _lowercase       :							Any      		=		self.num_labels
           _lowercase       :							Union[str, Any]      		=		MobileViTVaForSemanticSegmentation(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[int]      		=		model(lowerCamelCase)
           self.parent.assertEqual(
               result.logits.shape,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               ),      )
           _lowercase       :							List[Any]      		=		model(lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertEqual(
               result.logits.shape,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               ),      )




       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							str      		=		self.prepare_config_and_inputs()
           _lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							int      		=		config_and_inputs
           _lowercase       :							List[str]      		=		{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	List[Any]     =			(
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       lowercase_ :	Dict     =			(
           {
               """feature-extraction""": MobileViTVaModel,
               """image-classification""": MobileViTVaForImageClassification,
               """image-segmentation""": MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowercase_ :	List[Any]     =			False
       lowercase_ :	Optional[int]     =			False
       lowercase_ :	List[Any]     =			False
       lowercase_ :	Tuple     =			False
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		MobileViTVaModelTester(self)
           _lowercase       :							Tuple      		=		MobileViTVaConfigTester(self,      config_class=lowerCamelCase,      has_text_modality=lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='MobileViTV2 does not use inputs_embeds')
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip(reason='MobileViTV2 does not support input and output embeddings')
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip(reason='MobileViTV2 does not output attentions')
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           pass
       @require_torch_multi_gpu
       @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.')
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Dict      		=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase       :							List[Any]      		=		model_class(lowerCamelCase)
               _lowercase       :							Tuple      		=		inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase       :							Any      		=		[*signature.parameters.keys()]

               _lowercase       :							Union[str, Any]      		=		['pixel_values']
               self.assertListEqual(arg_names[:1],      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           def check_hidden_states_output(lowerCamelCase,      lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Optional[Any]      		=		model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()

               with torch.no_grad():
                   _lowercase       :							Optional[int]      		=		model(**self._prepare_for_class(lowerCamelCase,      lowerCamelCase))

               _lowercase       :							List[Any]      		=		outputs.hidden_states

               _lowercase       :							Tuple      		=		5
               self.assertEqual(len(lowerCamelCase),      lowerCamelCase)

               # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
               # with the width and height being successively divided by 2.
               _lowercase       :							Optional[int]      		=		2
               for i in range(len(lowerCamelCase)):
                   self.assertListEqual(
                       list(hidden_states[i].shape[-2:]),      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],      )
                   divisor *= 2

               self.assertEqual(self.model_tester.output_stride,      divisor // 2)

           _lowercase		,				_lowercase       :							Dict      		=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase       :							Tuple      		=		True
               check_hidden_states_output(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _lowercase       :							Optional[Any]      		=		True

               check_hidden_states_output(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							str      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							str      		=		MobileViTVaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def 				UpperCamelCase_(      )    ->						Dict:
    _lowercase       :							Tuple      		=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image






@require_torch
@require_vision
class _lowerCamelCase(						unittest.TestCase   ):
       @cached_property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return (
               MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256')
               if is_vision_available()
               else None
           )
       @slow
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[str]      		=		MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256').to(
               lowerCamelCase)

           _lowercase       :							Dict      		=		self.default_image_processor
           _lowercase       :							Union[str, Any]      		=		prepare_img()
           _lowercase       :							Dict      		=		image_processor(images=lowerCamelCase,      return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase       :							Tuple      		=		model(**lowerCamelCase)

           # verify the logits
           _lowercase       :							Optional[int]      		=		torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape,      lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]).to(lowerCamelCase)

           self.assertTrue(torch.allclose(outputs.logits[0, :3],      lowerCamelCase,      atol=1E-4))
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
           _lowercase       :							Optional[int]      		=		model.to(lowerCamelCase)

           _lowercase       :							Optional[int]      		=		MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

           _lowercase       :							Union[str, Any]      		=		prepare_img()
           _lowercase       :							Tuple      		=		image_processor(images=lowerCamelCase,      return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase       :							List[Any]      		=		model(**lowerCamelCase)
           _lowercase       :							str      		=		outputs.logits

           # verify the logits
           _lowercase       :							Tuple      		=		torch.Size((1, 21, 32, 32))
           self.assertEqual(logits.shape,      lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		torch.tensor(
               [
                   [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                   [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                   [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
               ],      device=lowerCamelCase,      )

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3],      lowerCamelCase,      atol=1E-4))




       @slow
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[str]      		=		MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
           _lowercase       :							Tuple      		=		model.to(lowerCamelCase)

           _lowercase       :							str      		=		MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

           _lowercase       :							int      		=		prepare_img()
           _lowercase       :							Dict      		=		image_processor(images=lowerCamelCase,      return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase       :							Union[str, Any]      		=		model(**lowerCamelCase)

           _lowercase       :							Any      		=		outputs.logits.detach().cpu()

           _lowercase       :							Optional[int]      		=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase,      target_sizes=[(50, 60)])
           _lowercase       :							Any      		=		torch.Size((50, 60))
           self.assertEqual(segmentation[0].shape,      lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		torch.Size((32, 32))
           self.assertEqual(segmentation[0].shape,      lowerCamelCase)


