"""simple docstring"""






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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(UpperCAmelCase__   , "tf_padding"		)		)
									self.parent.assertTrue(hasattr(UpperCAmelCase__   , "depth_multiplier"		)		)



class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				int=13   , _snake_case			:				Union[str, Any]=3   , _snake_case			:				Optional[Any]=32   , _snake_case			:				Optional[int]=0.25   , _snake_case			:				Dict=8   , _snake_case			:				List[Any]=8   , _snake_case			:				Optional[int]=6   , _snake_case			:				List[str]=32   , _snake_case			:				Union[str, Any]=True   , _snake_case			:				Any=True   , _snake_case			:				Any=True   , _snake_case			:				Optional[int]="relu6"   , _snake_case			:				Dict=1280   , _snake_case			:				Any=0.1   , _snake_case			:				List[str]=0.02   , _snake_case			:				int=True   , _snake_case			:				Union[str, Any]=True   , _snake_case			:				Optional[int]=10   , _snake_case			:				Any=None   , )							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				image_size
									SCREAMING_SNAKE_CASE__		  =				depth_multiplier
									SCREAMING_SNAKE_CASE__		  =				depth_divisible_by
									SCREAMING_SNAKE_CASE__		  =				min_depth
									SCREAMING_SNAKE_CASE__		  =				expand_ratio
									SCREAMING_SNAKE_CASE__		  =				tf_padding
									SCREAMING_SNAKE_CASE__		  =				output_stride
									SCREAMING_SNAKE_CASE__		  =				first_layer_is_expansion
									SCREAMING_SNAKE_CASE__		  =				finegrained_output
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
									SCREAMING_SNAKE_CASE__		  =				classifier_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, pixel_values, labels, pixel_labels



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									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 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Tuple   , _snake_case			:				Any   , _snake_case			:				int		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaModel(config=UpperCAmelCase__		)
									model.to(UpperCAmelCase__		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(UpperCAmelCase__		)
									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 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Optional[int]   , _snake_case			:				List[str]   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[int]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaForImageClassification(UpperCAmelCase__		)
									model.to(UpperCAmelCase__		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(UpperCAmelCase__   , labels=UpperCAmelCase__		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Any		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaForSemanticSegmentation(UpperCAmelCase__		)
									model.to(UpperCAmelCase__		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(UpperCAmelCase__		)
									self.parent.assertEqual(
									    result.logits.shape   , (
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )   , )
									SCREAMING_SNAKE_CASE__		  =				model(UpperCAmelCase__   , labels=UpperCAmelCase__		)
									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 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{'''pixel_values''': pixel_values}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					a   	=		(
					    {
					        'feature-extraction': MobileNetVaModel,
					        'image-classification': MobileNetVaForImageClassification,
					        'image-segmentation': MobileNetVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a   	=		False
					a   	=		False
					a   	=		False
					a   	=		False



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaConfigTester(self   , config_class=UpperCAmelCase__   , has_text_modality=UpperCAmelCase__		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Tuple:
									self.config_tester.run_common_tests()



					@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"		)
					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									pass



					@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"		)
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									pass



					@unittest.skip(reason="MobileNetV2 does not output attentions"		)
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Tuple:
									pass



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE__		  =				['''pixel_values''']
													self.assertListEqual(arg_names[:1]   , UpperCAmelCase__		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase__		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Optional[Any]:
									def check_hidden_states_output(_snake_case			:				Tuple   , _snake_case			:				Dict   , _snake_case			:				Dict		):
													SCREAMING_SNAKE_CASE__		  =				model_class(UpperCAmelCase__		)
													model.to(UpperCAmelCase__		)
													model.eval()

													with torch.no_grad():
																	SCREAMING_SNAKE_CASE__		  =				model(**self._prepare_for_class(UpperCAmelCase__   , UpperCAmelCase__		)		)

													SCREAMING_SNAKE_CASE__		  =				outputs.hidden_states

													SCREAMING_SNAKE_CASE__		  =				16
													self.assertEqual(len(UpperCAmelCase__		)   , UpperCAmelCase__		)

									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				True
													check_hidden_states_output(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__		)

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

													check_hidden_states_output(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase__		)



					@slow
					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				MobileNetVaModel.from_pretrained(UpperCAmelCase__		)
													self.assertIsNotNone(UpperCAmelCase__		)




def 						SCREAMING_SNAKE_CASE					(  )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
				return image

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






					'''simple docstring'''


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



					@slow
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"		).to(UpperCAmelCase__		)

									SCREAMING_SNAKE_CASE__		  =				self.default_image_processor
									SCREAMING_SNAKE_CASE__		  =				prepare_img()
									SCREAMING_SNAKE_CASE__		  =				image_processor(images=UpperCAmelCase__   , return_tensors="pt"		).to(UpperCAmelCase__		)

									# forward pass
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(**UpperCAmelCase__		)

									# verify the logits
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 1001)		)
									self.assertEqual(outputs.logits.shape   , UpperCAmelCase__		)

									SCREAMING_SNAKE_CASE__		  =				torch.tensor([0.2445, -1.1993, 0.1905]		).to(UpperCAmelCase__		)

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)
									SCREAMING_SNAKE_CASE__		  =				model.to(UpperCAmelCase__		)

									SCREAMING_SNAKE_CASE__		  =				MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)

									SCREAMING_SNAKE_CASE__		  =				prepare_img()
									SCREAMING_SNAKE_CASE__		  =				image_processor(images=UpperCAmelCase__   , return_tensors="pt"		).to(UpperCAmelCase__		)

									# forward pass
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(**UpperCAmelCase__		)
									SCREAMING_SNAKE_CASE__		  =				outputs.logits

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

									SCREAMING_SNAKE_CASE__		  =				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=UpperCAmelCase__   , )

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





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




