





import argparse
import os
import re


lowerCAmelCase_        =							'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_        =							re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
lowerCAmelCase_        =							re.compile(r'''\s*\(\s*"(\S[^"]+)"''')






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase = False					)						->		Tuple:






				"""simple docstring"""


				with open(_UpperCamelCase					,							'''r'''					,							encoding='''utf-8'''					) as f:
								snake_case_		:       Optional[Any]           =    f.read()

				snake_case_		:       Optional[Any]           =    content.split('''\n'''					)
				snake_case_		:       Optional[int]           =    []
				snake_case_		:       List[Any]           =    0
				while line_idx < len(_UpperCamelCase					):
								if _re_intro_mapping.search(lines[line_idx]					) is not None:
												snake_case_		:       Any           =    len(re.search(R'''^(\s*)\S'''					,							lines[line_idx]					).groups()[0]					) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(''' ''' * indent + '''('''					):
																new_lines.append(lines[line_idx]					)
																line_idx += 1

												snake_case_		:       List[Any]           =    []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				snake_case_		:       int           =    line_idx
																				while not lines[line_idx].startswith(''' ''' * indent + ''')'''					):
																								line_idx += 1
																				blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]					)					)
																else:
																				blocks.append(lines[line_idx]					)
																line_idx += 1

												# Sort blocks by their identifiers
												snake_case_		:       int           =    sorted(_UpperCamelCase					,							key=lambda _UpperCamelCase					: _re_identifier.search(_UpperCamelCase					).groups()[0]					)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]					)
												line_idx += 1

				if overwrite:
								with open(_UpperCamelCase					,							'''w'''					,							encoding='''utf-8'''					) as f:
												f.write('''\n'''.join(_UpperCamelCase					)					)
				elif "\n".join(_UpperCamelCase					) != content:
								return True






def 	lowerCamelCase_		(							_UpperCamelCase = False					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       Union[str, Any]           =    [os.path.join(_UpperCamelCase					,							_UpperCamelCase					) for f in os.listdir(_UpperCamelCase					) if f.endswith('''.py'''					)]
				snake_case_		:       Any           =    [sort_auto_mapping(_UpperCamelCase					,							overwrite=_UpperCamelCase					) for fname in fnames]

				if not overwrite and any(_UpperCamelCase					):
								snake_case_		:       List[str]           =    [f for f, d in zip(_UpperCamelCase					,							_UpperCamelCase					) if d]
								raise ValueError(
								    f'''The following files have auto mappings that need sorting: {", ".join(_UpperCamelCase					)}. Run `make style` to fix'''
								    ''' this.'''					)


if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
	lowerCAmelCase_        =							parser.parse_args()

	sort_all_auto_mappings(not args.check_only)





"""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 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(A				,   'tf_padding'							)							)
						self.parent.assertTrue(hasattr(A				,   'depth_multiplier'							)							)







class 					A_:







			"""simple docstring"""



			def __init__(	self				,   A				,   A=13				,   A=3				,   A=32				,   A=0.2_5				,   A=8				,   A=8				,   A=6				,   A=32				,   A=True				,   A=True				,   A=True				,   A="relu6"				,   A=1280				,   A=0.1				,   A=0.0_2				,   A=True				,   A=True				,   A=10				,   A=None				,   ):
						_lowerCamelCase					:					str	       =    parent
						_lowerCamelCase					:					Union[str, Any]	       =    batch_size
						_lowerCamelCase					:					List[str]	       =    num_channels
						_lowerCamelCase					:					int	       =    image_size
						_lowerCamelCase					:					str	       =    depth_multiplier
						_lowerCamelCase					:					Optional[int]	       =    depth_divisible_by
						_lowerCamelCase					:					Any	       =    min_depth
						_lowerCamelCase					:					Optional[Any]	       =    expand_ratio
						_lowerCamelCase					:					Tuple	       =    tf_padding
						_lowerCamelCase					:					Optional[Any]	       =    output_stride
						_lowerCamelCase					:					Optional[int]	       =    first_layer_is_expansion
						_lowerCamelCase					:					List[str]	       =    finegrained_output
						_lowerCamelCase					:					Any	       =    hidden_act
						_lowerCamelCase					:					Dict	       =    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier							)
						_lowerCamelCase					:					Any	       =    classifier_dropout_prob
						_lowerCamelCase					:					Optional[Any]	       =    use_labels
						_lowerCamelCase					:					Any	       =    is_training
						_lowerCamelCase					:					List[str]	       =    num_labels
						_lowerCamelCase					:					int	       =    initializer_range
						_lowerCamelCase					:					Tuple	       =    scope
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						_lowerCamelCase					:					Union[str, Any]	       =    None
						_lowerCamelCase					:					str	       =    None
						if self.use_labels:
									_lowerCamelCase					:					Dict	       =    ids_tensor([self.batch_size]				,   self.num_labels							)
									_lowerCamelCase					:					Dict	       =    ids_tensor([self.batch_size, self.image_size, self.image_size]				,   self.num_labels							)

						_lowerCamelCase					:					List[Any]	       =    self.get_config()

						return config, pixel_values, labels, pixel_labels
			def    _lowerCAmelCase		(	self							):
						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				,   A				,   A				,   A				,   A							):
						_lowerCamelCase					:					int	       =    MobileNetVaModel(config=A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					str	       =    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    _lowerCAmelCase		(	self				,   A				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[Any]	       =    self.num_labels
						_lowerCamelCase					:					Tuple	       =    MobileNetVaForImageClassification(A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					List[str]	       =    model(A				,   labels=A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A				,   A							):
						_lowerCamelCase					:					int	       =    self.num_labels
						_lowerCamelCase					:					List[str]	       =    MobileNetVaForSemanticSegmentation(A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					Dict	       =    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,
						    )				,   )
						_lowerCamelCase					:					str	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    self.prepare_config_and_inputs()
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					List[Any]	       =    config_and_inputs
						_lowerCamelCase					:					Any	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class 					A_(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			List[Any]												=			(
			    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			a_       :			Optional[int]												=			(
			    {
			        """feature-extraction""": MobileNetVaModel,
			        """image-classification""": MobileNetVaForImageClassification,
			        """image-segmentation""": MobileNetVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			a_       :			List[Any]												=			False
			a_       :			Union[str, Any]												=			False
			a_       :			Union[str, Any]												=			False
			a_       :			str												=			False
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    MobileNetVaModelTester(self							)
						_lowerCamelCase					:					int	       =    MobileNetVaConfigTester(self				,   config_class=A				,   has_text_modality=A							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'							)
			def    _lowerCAmelCase		(	self							):
						pass
			@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'							)
			def    _lowerCAmelCase		(	self							):
						pass
			@unittest.skip(reason='MobileNetV2 does not output attentions'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

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

									_lowerCamelCase					:					Tuple	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						def check_hidden_states_output(A				,   A				,   A							):
									_lowerCamelCase					:					Any	       =    model_class(A							)
									model.to(A							)
									model.eval()

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

									_lowerCamelCase					:					int	       =    outputs.hidden_states

									_lowerCamelCase					:					Dict	       =    16
									self.assertEqual(len(A							)				,   A							)

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

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					Optional[Any]	       =    True
									check_hidden_states_output(A				,   A				,   A							)

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

									check_hidden_states_output(A				,   A				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCamelCase					:					Any	       =    MobileNetVaModel.from_pretrained(A							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					List[Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return (
						    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'							) if is_vision_available() else None
						)
			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'							).to(A							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					Tuple	       =    prepare_img()
						_lowerCamelCase					:					int	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

						# forward pass
						with torch.no_grad():
									_lowerCamelCase					:					int	       =    model(**A							)

						# verify the logits
						_lowerCamelCase					:					Tuple	       =    torch.Size((1, 1001)							)
						self.assertEqual(outputs.logits.shape				,   A							)

						_lowerCamelCase					:					Union[str, Any]	       =    torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]							).to(A							)

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

			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					str	       =    MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'							)
						_lowerCamelCase					:					Tuple	       =    model.to(A							)

						_lowerCamelCase					:					Tuple	       =    MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'							)

						_lowerCamelCase					:					Any	       =    prepare_img()
						_lowerCamelCase					:					Any	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

						# forward pass
						with torch.no_grad():
									_lowerCamelCase					:					Optional[Any]	       =    model(**A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					Tuple	       =    torch.Size((1, 21, 65, 65)							)
						self.assertEqual(logits.shape				,   A							)

						_lowerCamelCase					:					Optional[int]	       =    torch.tensor(
						    [
						        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
						        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
						        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
						    ]				,   device=A				,   )

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





