
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			).convert('RGB'			)

		return image


def 				A	( lowercase			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# fmt: off

		# vision encoder
		rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')			)
		rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')			)
		rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')			)
		rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')			)
		rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')			)
		rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')			)

		for i in range(config.vision_config.num_hidden_layers			):
				rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')			)
				rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')			)

		# QFormer
		rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')			)
		rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')			)

		# fmt: on
		return rename_keys


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Tuple:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase			)       ->  Tuple:
		'''simple docstring'''

		for i in range(config.vision_config.num_hidden_layers			):
				# read in original q and v biases
				UpperCamelCase          =					state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''			)
				UpperCamelCase          =					state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''			)

				# next, set bias in the state dict
				UpperCamelCase          =					torch.cat((q_bias, torch.zeros_like(lowercase   ,	requires_grad=lowercase			), v_bias)			)
				UpperCamelCase          =					qkv_bias


def 				A	( lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					364 if 'coco' in model_name else 224
		UpperCamelCase          =					InstructBlipVisionConfig(image_size=lowercase			).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "t5-xl" in model_name:
				UpperCamelCase          =					TaConfig.from_pretrained('google/flan-t5-xl'   ,	dense_act_fn='gelu'   ,	bos_token_id=1			).to_dict()
		elif "t5-xxl" in model_name:
				UpperCamelCase          =					TaConfig.from_pretrained('google/flan-t5-xxl'   ,	dense_act_fn='gelu'   ,	bos_token_id=1			).to_dict()
		elif "vicuna-7b" in model_name:
				UpperCamelCase          =					LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'   ,	vocab_size=32_001			).to_dict()
		elif "vicuna-13b" in model_name:
				UpperCamelCase          =					LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'   ,	vocab_size=32_001			).to_dict()
		else:
				raise ValueError('Model name not supported'			)

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		UpperCamelCase          =					InstructBlipQFormerConfig(vocab_size=30_523			).to_dict()
		UpperCamelCase          =					InstructBlipConfig(vision_config=lowercase   ,	text_config=lowercase   ,	qformer_config=lowercase			)

		return config, image_size


@torch.no_grad()
def 				A	( lowercase   ,	lowercase=None   ,	lowercase=False			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-uncased'   ,	truncation_side='left'			)
		qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}			)

		if "t5" in model_name:
				UpperCamelCase          =					TaTokenizerFast.from_pretrained('google/flan-t5-xl'   ,	truncation_side='left'			)
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				UpperCamelCase          =					LlamaTokenizerFast.from_pretrained(
				    'huggyllama/llama-7b'   ,	truncation_side='left'   ,	bos_token='</s>'   ,	unk_token='</s>'			)
				tokenizer.add_special_tokens({'pad_token': '[PAD]'}			)

		UpperCamelCase     ,  UpperCamelCase          =					get_blipa_config(lowercase			)
		UpperCamelCase          =					InstructBlipForConditionalGeneration(lowercase			).eval()

		UpperCamelCase          =					{
		    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
		    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
		    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
		    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
		}

		UpperCamelCase     ,  UpperCamelCase          =					model_name_to_original[model_name]

		# load original model
		print('Loading original model...'			)
		UpperCamelCase          =					'cuda:1' if torch.cuda.is_available() else 'cpu'
		UpperCamelCase          =					'cuda:2' if torch.cuda.is_available() else 'cpu'
		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					load_model_and_preprocess(
		    name=lowercase   ,	model_type=lowercase   ,	is_eval=lowercase   ,	device=lowercase			)
		original_model.eval()
		print('Done!'			)

		# update state dict keys
		UpperCamelCase          =					original_model.state_dict()
		UpperCamelCase          =					create_rename_keys(lowercase			)
		for src, dest in rename_keys:
				rename_key(lowercase   ,	lowercase   ,	lowercase			)

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				UpperCamelCase          =					state_dict.pop(lowercase			)
				if key.startswith('Qformer.bert'			):
						UpperCamelCase          =					key.replace('Qformer.bert'   ,	'qformer'			)
				if "attention.self" in key:
						UpperCamelCase          =					key.replace('self'   ,	'attention'			)
				if "llm_proj" in key:
						UpperCamelCase          =					key.replace('llm_proj'   ,	'language_projection'			)
				if "t5_proj" in key:
						UpperCamelCase          =					key.replace('t5_proj'   ,	'language_projection'			)
				if key.startswith('llm_model'			):
						UpperCamelCase          =					key.replace('llm_model'   ,	'language_model'			)
				if key.startswith('t5'			):
						UpperCamelCase          =					key.replace('t5'   ,	'language'			)
				UpperCamelCase          =					val

		# read in qv biases
		read_in_q_v_bias(lowercase   ,	lowercase			)

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(lowercase   ,	strict=lowercase			)

		UpperCamelCase          =					load_demo_image()
		UpperCamelCase          =					'What is unusual about this image?'

		# create processor
		UpperCamelCase          =					BlipImageProcessor(
		    size={'height': image_size, 'width': image_size}   ,	image_mean=lowercase   ,	image_std=lowercase			)
		UpperCamelCase          =					InstructBlipProcessor(
		    image_processor=lowercase   ,	tokenizer=lowercase   ,	qformer_tokenizer=lowercase   ,	)
		UpperCamelCase          =					processor(images=lowercase   ,	text=lowercase   ,	return_tensors='pt'			).to(lowercase			)

		# make sure processor creates exact same pixel values
		UpperCamelCase          =					vis_processors['eval'](lowercase			).unsqueeze(0			).to(lowercase			)
		UpperCamelCase          =					inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device			)   ,	lowercase			)

		original_model.to(lowercase			)
		hf_model.to(lowercase			)
		with torch.no_grad():
				if "vicuna" in model_name:
						UpperCamelCase          =					original_model({'image': original_pixel_values, 'text_input': [prompt]}			).logits
						UpperCamelCase          =					hf_model(**lowercase			).logits
				else:
						UpperCamelCase          =					original_model(
						    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}			).logits
						UpperCamelCase          =					tokenizer('\n'   ,	return_tensors='pt'			).input_ids.to(lowercase			)
						UpperCamelCase          =					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id   ,	-100			)
						UpperCamelCase          =					hf_model(**lowercase   ,	labels=lowercase			).logits

		print('First values of original logits:'   ,	original_logits[0, :3, :3]			)
		print('First values of HF logits:'   ,	logits[0, :3, :3]			)

		# assert values
		assert original_logits.shape == logits.shape
		UpperCamelCase          =					1e-4 if 'vicuna' in model_name else 1e-5
		assert torch.allclose(original_logits.to(logits.device			)   ,	lowercase   ,	atol=lowercase			)
		print('Looks ok!'			)

		print('Generating with original model...'			)
		UpperCamelCase          =					original_model.generate({'image': original_pixel_values, 'prompt': prompt}   ,	num_beams=5			)

		# important: we need to cast the weights of the HF model to the appropriate type
		print('Generating with HF model...'			)
		UpperCamelCase          =					hf_model.generate(
		    **lowercase   ,	do_sample=lowercase   ,	num_beams=5   ,	max_length=256   ,	min_length=1   ,	top_p=0.9   ,	repetition_penalty=1.5   ,	length_penalty=1.0   ,	temperature=1   ,	)
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				UpperCamelCase          =					2
		print('Original generation:'   ,	lowercase			)
		UpperCamelCase          =					processor.batch_decode(lowercase   ,	skip_special_tokens=lowercase			)
		UpperCamelCase          =					[text.strip() for text in output_text]
		print('HF generation:'   ,	lowercase			)

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(lowercase			)
				hf_model.save_pretrained(lowercase			)

		if push_to_hub:
				processor.push_to_hub(f'''Salesforce/{model_name}'''			)
				hf_model.push_to_hub(f'''Salesforce/{model_name}'''			)


if __name__ == "__main__":
					_UpperCAmelCase :			List[Any]              =	argparse.ArgumentParser()
					_UpperCAmelCase :			Optional[int]              =	[
					    "instructblip-vicuna-7b",
					    "instructblip-vicuna-13b",
					    "instructblip-flan-t5-xl",
					    "instructblip-flan-t5-xxl",
					]
					parser.add_argument(
					    "--model_name",
					    default="instructblip-flan-t5-xl",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether to push the model and processor to the hub after converting",
					)

					_UpperCAmelCase :			List[Any]              =	parser.parse_args()

					convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(A_    ,				'tf_padding'	)	)
				self.parent.assertTrue(hasattr(A_    ,				'depth_multiplier'	)	)






class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=3    ,				A_=32    ,				A_=0.25    ,				A_=8    ,				A_=8    ,				A_=6    ,				A_=32    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_="relu6"    ,				A_=1_280    ,				A_=0.1    ,				A_=0.02    ,				A_=True    ,				A_=True    ,				A_=10    ,				A_=None    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					image_size
				UpperCamelCase          =					depth_multiplier
				UpperCamelCase          =					depth_divisible_by
				UpperCamelCase          =					min_depth
				UpperCamelCase          =					expand_ratio
				UpperCamelCase          =					tf_padding
				UpperCamelCase          =					output_stride
				UpperCamelCase          =					first_layer_is_expansion
				UpperCamelCase          =					finegrained_output
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier	)
				UpperCamelCase          =					classifier_dropout_prob
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					is_training
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				UpperCamelCase          =					None
				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.num_labels	)
						UpperCamelCase          =					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,				self.num_labels	)

				UpperCamelCase          =					self.get_config()

				return config, pixel_values, labels, pixel_labels



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return MobileNetVaConfig(
				    num_channels=self.num_channels    ,				image_size=self.image_size    ,				depth_multiplier=self.depth_multiplier    ,				depth_divisible_by=self.depth_divisible_by    ,				min_depth=self.min_depth    ,				expand_ratio=self.expand_ratio    ,				output_stride=self.output_stride    ,				first_layer_is_expansion=self.first_layer_is_expansion    ,				finegrained_output=self.finegrained_output    ,				hidden_act=self.hidden_act    ,				tf_padding=self.tf_padding    ,				classifier_dropout_prob=self.classifier_dropout_prob    ,				initializer_range=self.initializer_range    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				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,
				    )    ,				)
				self.parent.assertEqual(
				    result.pooler_output.shape    ,				(self.batch_size, self.last_hidden_size)    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileNetVaForImageClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileNetVaForSemanticSegmentation(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(
				    result.logits.shape    ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )    ,				)
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				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	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    (
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		__lowercase	:	Optional[int]     					=    (
		    {
		        "feature-extraction": MobileNetVaModel,
		        "image-classification": MobileNetVaForImageClassification,
		        "image-segmentation": MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase	:	Optional[int]     					=    False
		__lowercase	:	List[str]     					=    False
		__lowercase	:	List[str]     					=    False
		__lowercase	:	Dict     					=    False



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaModelTester(self	)
				UpperCamelCase          =					MobileNetVaConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'	)
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileNetV2 does not output attentions'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase          =					['pixel_values']
						self.assertListEqual(arg_names[:1]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				def check_hidden_states_output(A_    ,				A_    ,				A_	):
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()

						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)

						UpperCamelCase          =					outputs.hidden_states

						UpperCamelCase          =					16
						self.assertEqual(len(A_	)    ,				A_	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					True
						check_hidden_states_output(A_    ,				A_    ,				A_	)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase          =					True

						check_hidden_states_output(A_    ,				A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					MobileNetVaModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


@require_torch
@require_vision
class     lowercase (		unittest.TestCase       ):



		@cached_property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return (
				    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'	) if is_vision_available() else None
				)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'	).to(A_	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	).to(A_	)

				# forward pass
				with torch.no_grad():
						UpperCamelCase          =					model(**A_	)

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 1_001)	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor([0.2445, -1.1993, 0.1905]	).to(A_	)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				A_    ,				atol=1e-4	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)
				UpperCamelCase          =					model.to(A_	)

				UpperCamelCase          =					MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)

				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	).to(A_	)

				# forward pass
				with torch.no_grad():
						UpperCamelCase          =					model(**A_	)
				UpperCamelCase          =					outputs.logits

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 21, 65, 65)	)
				self.assertEqual(logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor(
				    [
				        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
				        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
				        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
				    ]    ,				device=A_    ,				)

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