






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 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.config_class(**self.inputs_dict     )
								self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''tf_padding'''     )     )
								self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''depth_multiplier'''     )     )







class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=0.25       ,					__lowerCAmelCase=8       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase="relu6"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      parent
								__magic_name__		:Any        =      batch_size
								__magic_name__		:List[Any]        =      num_channels
								__magic_name__		:List[Any]        =      image_size
								__magic_name__		:Any        =      depth_multiplier
								__magic_name__		:List[Any]        =      min_depth
								__magic_name__		:List[str]        =      tf_padding
								__magic_name__		:List[str]        =      int(last_hidden_size * depth_multiplier     )
								__magic_name__		:Optional[int]        =      output_stride
								__magic_name__		:Dict        =      hidden_act
								__magic_name__		:List[Any]        =      classifier_dropout_prob
								__magic_name__		:int        =      use_labels
								__magic_name__		:Optional[int]        =      is_training
								__magic_name__		:str        =      num_labels
								__magic_name__		:Optional[Any]        =      initializer_range
								__magic_name__		:List[Any]        =      scope







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

								__magic_name__		:Dict        =      None
								__magic_name__		:Dict        =      None
								if self.use_labels:
												__magic_name__		:List[str]        =      ids_tensor([self.batch_size]       ,					self.num_labels     )
												__magic_name__		:str        =      ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels     )

								__magic_name__		:str        =      self.get_config()

								return config, pixel_values, labels, pixel_labels







				def 			A	(						self     ):
								"""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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      MobileNetVaModel(config=__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:str        =      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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.num_labels
								__magic_name__		:Union[str, Any]        =      MobileNetVaForImageClassification(__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:int        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Union[str, Any]        =      config_and_inputs
								__magic_name__		:Optional[Any]        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
				a__					       =  (
				    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      MobileNetVaModelTester(self     )
								__magic_name__		:Tuple        =      MobileNetVaConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







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







				@unittest.skip(reason='''MobileNetV1 does not output attentions'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:int        =      self.model_tester.prepare_config_and_inputs_for_common()

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

												__magic_name__		:List[str]        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Optional[int]        =      model_class(__lowerCAmelCase     )
												model.to(__lowerCAmelCase     )
												model.eval()

												with torch.no_grad():
																__magic_name__		:List[str]        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

												__magic_name__		:List[str]        =      outputs.hidden_states

												__magic_name__		:Optional[int]        =      2_6
												self.assertEqual(len(__lowerCAmelCase     )       ,					__lowerCAmelCase     )

								__magic_name__     ,				__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Optional[int]        =      True
												check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

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

												check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:Optional[int]        =      MobileNetVaModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image

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







				@cached_property
				def 			A	(						self     ):
								"""simple docstring"""
								return (
								    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''     ) if is_vision_available() else None
								)







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''     ).to(__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      self.default_image_processor
								__magic_name__		:str        =      prepare_img()
								__magic_name__		:Optional[int]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''pt'''     ).to(__lowerCAmelCase     )

								# forward pass
								with torch.no_grad():
												__magic_name__		:int        =      model(**__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:str        =      torch.Size((1, 1_0_0_1)     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__lowerCAmelCase     )

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














from __future__ import annotations

from typing import Any
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 0     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      row, column
								__magic_name__		:Union[str, Any]        =      [[default_value for c in range(__lowerCAmelCase     )] for r in range(__lowerCAmelCase     )]







				def __str__(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__magic_name__		:Dict        =      0
								for row_vector in self.array:
												for obj in row_vector:
																__magic_name__		:Dict        =      max(__lowerCAmelCase       ,					len(str(__lowerCAmelCase     )     )     )
								__magic_name__		:List[str]        =      F'''%{max_element_length}s'''

								# Make string and return
								def single_line(__lowerCAmelCase     ) -> str:
												nonlocal string_format_identifier
												__magic_name__		:int        =      '''['''
												line += ", ".join(string_format_identifier % (obj,) for obj in row_vector     )
												line += "]"
												return line

								s += "\n".join(single_line(__lowerCAmelCase     ) for row_vector in self.array     )
								return s







				def __repr__(						self     ):
								"""simple docstring"""
								return str(self     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if not (isinstance(__lowerCAmelCase       ,					(list, tuple)     ) and len(__lowerCAmelCase     ) == 2):
												return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
												return False
								else:
												return True







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								return self.array[loc[0]][loc[1]]







				def __setitem__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      value







				def __add__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == another.row and self.column == another.column

								# Add
								__magic_name__		:List[Any]        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      self[r, c] + another[r, c]
								return result







				def __neg__(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      -self[r, c]
								return result







				def __sub__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self + (-another)







				def __mul__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if isinstance(__lowerCAmelCase       ,					(int, float)     ):  # Scalar multiplication
												__magic_name__		:List[str]        =      Matrix(self.row       ,					self.column     )
												for r in range(self.row     ):
																for c in range(self.column     ):
																				__magic_name__		:Tuple        =      self[r, c] * another
												return result
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):  # Matrix multiplication
												assert self.column == another.row
												__magic_name__		:List[Any]        =      Matrix(self.row       ,					another.column     )
												for r in range(self.row     ):
																for c in range(another.column     ):
																				for i in range(self.column     ):
																								result[r, c] += self[r, i] * another[i, c]
												return result
								else:
												__magic_name__		:List[str]        =      F'''Unsupported type given for another ({type(__lowerCAmelCase     )})'''
												raise TypeError(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix(self.column       ,					self.row     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:List[str]        =      self[r, c]
								return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								__magic_name__		:int        =      v.transpose()
								__magic_name__		:str        =      (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
												return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

					def      __lowercase					( ):







									"""simple docstring"""






									__magic_name__		:Optional[int]        =      Matrix(3,  3,  0 )
									for i in range(3 ):
													__magic_name__		:Tuple        =      1
									print(f'''a^(-1) is {ainv}''' )
									# u, v
									__magic_name__		:Optional[int]        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      1, 2, -3
									__magic_name__		:int        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      4, -2, 5
									print(f'''u is {u}''' )
									print(f'''v is {v}''' )
									print(f'''uv^T is {u * v.transpose()}''' )
									# Sherman Morrison
									print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case,  snake_case )}''' )

					def      __lowercase					( ):







									"""simple docstring"""






									import doctest

									doctest.testmod()

					testa()






