



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, MobileNetVaModel
				from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.config_class(**self.inputs_dict )
					self.parent.assertTrue(hasattr(a_       ,       '''tf_padding''' ) )
					self.parent.assertTrue(hasattr(a_       ,       '''depth_multiplier''' ) )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[str]       ,       a_							: int       ,       a_							: List[str]=13       ,       a_							: str=3       ,       a_							: Any=32       ,       a_							: Any=0.2_5       ,       a_							: Optional[int]=8       ,       a_							: List[str]=True       ,       a_							: Tuple=10_24       ,       a_							: Union[str, Any]=32       ,       a_							: Dict="relu6"       ,       a_							: List[str]=0.1       ,       a_							: str=0.0_2       ,       a_							: Optional[int]=True       ,       a_							: Dict=True       ,       a_							: Union[str, Any]=10       ,       a_							: str=None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	parent
					__UpperCAmelCase    :     List[str]													=	batch_size
					__UpperCAmelCase    :     Any													=	num_channels
					__UpperCAmelCase    :     Optional[Any]													=	image_size
					__UpperCAmelCase    :     List[str]													=	depth_multiplier
					__UpperCAmelCase    :     Tuple													=	min_depth
					__UpperCAmelCase    :     List[Any]													=	tf_padding
					__UpperCAmelCase    :     Any													=	int(last_hidden_size * depth_multiplier )
					__UpperCAmelCase    :     Optional[Any]													=	output_stride
					__UpperCAmelCase    :     List[Any]													=	hidden_act
					__UpperCAmelCase    :     List[str]													=	classifier_dropout_prob
					__UpperCAmelCase    :     List[Any]													=	use_labels
					__UpperCAmelCase    :     Dict													=	is_training
					__UpperCAmelCase    :     Optional[int]													=	num_labels
					__UpperCAmelCase    :     Any													=	initializer_range
					__UpperCAmelCase    :     Optional[Any]													=	scope




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     Union[str, Any]													=	None
					if self.use_labels:
							__UpperCAmelCase    :     Union[str, Any]													=	ids_tensor([self.batch_size]       ,       self.num_labels )
							__UpperCAmelCase    :     Optional[Any]													=	ids_tensor([self.batch_size, self.image_size, self.image_size]       ,       self.num_labels )

					__UpperCAmelCase    :     Optional[int]													=	self.get_config()

					return config, pixel_values, labels, pixel_labels




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return MobileNetVaConfig(
					    num_channels=self.num_channels       ,       image_size=self.image_size       ,       depth_multiplier=self.depth_multiplier       ,       min_depth=self.min_depth       ,       tf_padding=self.tf_padding       ,       hidden_act=self.hidden_act       ,       classifier_dropout_prob=self.classifier_dropout_prob       ,       initializer_range=self.initializer_range       ,       )




			def  snake_case__     (   self							: int       ,       a_							: Optional[int]       ,       a_							: Union[str, Any]       ,       a_							: str       ,       a_							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	MobileNetVaModel(config=a_ )
					model.to(a_ )
					model.eval()
					__UpperCAmelCase    :     Any													=	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,
					    )       ,       )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[str]       ,       a_							: List[str]       ,       a_							: str       ,       a_							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.num_labels
					__UpperCAmelCase    :     int													=	MobileNetVaForImageClassification(a_ )
					model.to(a_ )
					model.eval()
					__UpperCAmelCase    :     Any													=	model(a_       ,       labels=a_ )
					self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels) )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





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

@require_torch
class 		UpperCAmelCase__			(			__UpperCamelCase						,__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
			UpperCamelCase									=       (
			    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase									=       False
			UpperCamelCase									=       False
			UpperCamelCase									=       False
			UpperCamelCase									=       False




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	MobileNetVaModelTester(self )
					__UpperCAmelCase    :     Tuple													=	MobileNetVaConfigTester(self       ,       config_class=a_       ,       has_text_modality=a_ )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					self.config_tester.run_common_tests()




			@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds''' )
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					pass




			@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings''' )
			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					pass




			@unittest.skip(reason='''MobileNetV1 does not output attentions''' )
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					pass




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





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

					for model_class in self.all_model_classes:
							__UpperCAmelCase    :     Union[str, Any]													=	model_class(a_ )
							__UpperCAmelCase    :     str													=	inspect.signature(model.forward )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__UpperCAmelCase    :     Optional[Any]													=	[*signature.parameters.keys()]

							__UpperCAmelCase    :     Union[str, Any]													=	['''pixel_values''']
							self.assertListEqual(arg_names[:1]       ,       a_ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_ )




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					def check_hidden_states_output(a_							: Optional[Any]       ,       a_							: Dict       ,       a_							: Optional[int] ):
							__UpperCAmelCase    :     Any													=	model_class(a_ )
							model.to(a_ )
							model.eval()

							with torch.no_grad():
									__UpperCAmelCase    :     Dict													=	model(**self._prepare_for_class(a_       ,       a_ ) )

							__UpperCAmelCase    :     Optional[Any]													=	outputs.hidden_states

							__UpperCAmelCase    :     str													=	26
							self.assertEqual(len(a_ )       ,       a_ )

					__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__UpperCAmelCase    :     List[Any]													=	True
							check_hidden_states_output(a_       ,       a_       ,       a_ )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__UpperCAmelCase    :     Dict													=	True

							check_hidden_states_output(a_       ,       a_       ,       a_ )




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_ )




			@slow
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__UpperCAmelCase    :     Union[str, Any]													=	MobileNetVaModel.from_pretrained(a_ )
							self.assertIsNotNone(a_ )

def        a						(      ):







		'''simple docstring'''





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

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




			'''simple docstring'''






			@cached_property
			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					return (
					    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224''' ) if is_vision_available() else None
					)




			@slow
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224''' ).to(a_ )

					__UpperCAmelCase    :     List[Any]													=	self.default_image_processor
					__UpperCAmelCase    :     List[Any]													=	prepare_img()
					__UpperCAmelCase    :     Optional[Any]													=	image_processor(images=a_       ,       return_tensors='''pt''' ).to(a_ )

					# forward pass
					with torch.no_grad():
							__UpperCAmelCase    :     Dict													=	model(**a_ )

					# verify the logits
					__UpperCAmelCase    :     int													=	torch.Size((1, 10_01) )
					self.assertEqual(outputs.logits.shape       ,       a_ )

					__UpperCAmelCase    :     str													=	torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(a_ )

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










import glob
import os
import random
from string import ascii_lowercase, digits

import cva
__A   			=""
__A   			=""
__A   			=""
__A   			=1  # (0 is vertical, 1 is horizontal)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataset(_UpperCAmelCase ,   _UpperCAmelCase						)
		print('''Processing...'''						)
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	update_image_and_anno(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		for index, image in enumerate(_UpperCAmelCase						):
				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				__UpperCAmelCase    :     Any													=	random_chars(32						)
				__UpperCAmelCase    :     List[str]													=	paths[index].split(os.sep						)[-1].rsplit('''.''' ,   1						)[0]
				__UpperCAmelCase    :     Optional[Any]													=	f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
				cva.imwrite(f'/{file_root}.jpg' ,   _UpperCAmelCase ,   [cva.IMWRITE_JPEG_QUALITY, 85]						)
				print(f'Success {index+1}/{len(_UpperCAmelCase						)} with {file_name}'						)
				__UpperCAmelCase    :     Optional[Any]													=	[]
				for anno in new_annos[index]:
						__UpperCAmelCase    :     Union[str, Any]													=	f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
						annos_list.append(_UpperCAmelCase						)
				with open(f'/{file_root}.txt' ,   '''w'''						) as outfile:
						outfile.write('''\n'''.join(line for line in annos_list						)						)

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for label_file in glob.glob(os.path.join(_UpperCAmelCase ,   '''*.txt'''						)						):
				__UpperCAmelCase    :     Optional[Any]													=	label_file.split(os.sep						)[-1].rsplit('''.''' ,   1						)[0]
				with open(_UpperCAmelCase						) as in_file:
						__UpperCAmelCase    :     List[str]													=	in_file.readlines()
				__UpperCAmelCase    :     Optional[Any]													=	os.path.join(_UpperCAmelCase ,   f'{label_name}.jpg'						)

				__UpperCAmelCase    :     str													=	[]
				for obj_list in obj_lists:
						__UpperCAmelCase    :     str													=	obj_list.rstrip('''\n'''						).split(''' '''						)
						boxes.append(
						    [
						        int(obj[0]						),
						        float(obj[1]						),
						        float(obj[2]						),
						        float(obj[3]						),
						        float(obj[4]						),
						    ]						)
				if not boxes:
						continue
				img_paths.append(_UpperCAmelCase						)
				labels.append(_UpperCAmelCase						)
		return img_paths, labels

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int = 1						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for idx in range(len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Tuple													=	[]
				__UpperCAmelCase    :     List[Any]													=	img_list[idx]
				path_list.append(_UpperCAmelCase						)
				__UpperCAmelCase    :     str													=	anno_list[idx]
				__UpperCAmelCase    :     str													=	cva.imread(_UpperCAmelCase						)
				if flip_type == 1:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     List[str]													=	1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]						)
				elif flip_type == 0:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     Union[str, Any]													=	1 - bbox[2]
								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]						)
				new_annos_lists.append(_UpperCAmelCase						)
				new_imgs_list.append(_UpperCAmelCase						)
		return new_imgs_list, new_annos_lists, path_list

def        a						(      _UpperCAmelCase							:					int = 32						):







		'''simple docstring'''





		assert number_char > 1, "The number of character should greater than 1"
		__UpperCAmelCase    :     Union[str, Any]													=	ascii_lowercase + digits
		return "".join(random.choice(_UpperCAmelCase						) for _ in range(_UpperCAmelCase						)						)


if __name__ == "__main__":
				main()
				print("DONE ✅")





