import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

				from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
				from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=3       ,			__UpperCamelCase=32       ,			__UpperCamelCase=3       ,			__UpperCamelCase=10       ,			__UpperCamelCase=[10, 20, 30, 40]       ,			__UpperCamelCase=[1, 1, 2, 1]       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="relu"       ,			__UpperCamelCase=3       ,			__UpperCamelCase=None       ,			__UpperCamelCase=["stage2", "stage3", "stage4"]       ,			__UpperCamelCase=[2, 3, 4]       ,			)	->    Dict:



									'''simple docstring'''







									snake_case__   :      str   				= parent
									snake_case__   :      List[str]   				= batch_size
									snake_case__   :      List[str]   				= image_size
									snake_case__   :      str   				= num_channels
									snake_case__   :      int   				= embeddings_size
									snake_case__   :      Optional[int]   				= hidden_sizes
									snake_case__   :      int   				= depths
									snake_case__   :      Tuple   				= is_training
									snake_case__   :      List[Any]   				= use_labels
									snake_case__   :      Tuple   				= hidden_act
									snake_case__   :      Any   				= num_labels
									snake_case__   :      Optional[int]   				= scope
									snake_case__   :      Optional[int]   				= len(__UpperCamelCase						)
									snake_case__   :      List[str]   				= out_features
									snake_case__   :      Union[str, Any]   				= out_indices




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      str   				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									snake_case__   :      Optional[Any]   				= None
									if self.use_labels:
													snake_case__   :      Optional[Any]   				= ids_tensor([self.batch_size]       ,			self.num_labels						)

									snake_case__   :      Optional[Any]   				= self.get_config()

									return config, pixel_values, labels




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									return ResNetConfig(
									    num_channels=self.num_channels       ,			embeddings_size=self.embeddings_size       ,			hidden_sizes=self.hidden_sizes       ,			depths=self.depths       ,			hidden_act=self.hidden_act       ,			num_labels=self.num_labels       ,			out_features=self.out_features       ,			out_indices=self.out_indices       ,			)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Tuple   				= ResNetModel(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      Union[str, Any]   				= model(__UpperCamelCase						)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape       ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,			)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







									snake_case__   :      Any   				= self.num_labels
									snake_case__   :      Dict   				= ResNetForImageClassification(__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      Union[str, Any]   				= model(__UpperCamelCase       ,			labels=__UpperCamelCase						)
									self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Dict   				= ResNetBackbone(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      Any   				= model(__UpperCamelCase						)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps						)       ,			len(config.out_features						)						)
									self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,			[self.batch_size, self.hidden_sizes[1], 4, 4]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)       ,			len(config.out_features						)						)
									self.parent.assertListEqual(model.channels       ,			config.hidden_sizes[1:]						)

									# verify backbone works with out_features=None
									snake_case__   :      Union[str, Any]   				= None
									snake_case__   :      str   				= ResNetBackbone(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      int   				= model(__UpperCamelCase						)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps						)       ,			1						)
									self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,			[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)       ,			1						)
									self.parent.assertListEqual(model.channels       ,			[config.hidden_sizes[-1]]						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







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





@require_torch
class 					__snake_case    (     _lowerCamelCase       ,_lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  (
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCamelCase			   =  (
					    {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= ResNetModelTester(self						)
									snake_case__   :      Union[str, Any]   				= ConfigTester(self       ,			config_class=__UpperCamelCase       ,			has_text_modality=__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									return




					@unittest.skip(reason='ResNet does not use inputs_embeds'						)
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									pass




					@unittest.skip(reason='ResNet does not support input and output embeddings'						)
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									pass




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= self.model_tester.prepare_config_and_inputs_for_common()

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

													snake_case__   :      Optional[int]   				= ['''pixel_values''']
													self.assertListEqual(arg_names[:1]       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      str   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__UpperCamelCase						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													snake_case__   :      Tuple   				= model_class(config=__UpperCamelCase						)
													for name, module in model.named_modules():
																	if isinstance(__UpperCamelCase       ,			(nn.BatchNormad, nn.GroupNorm)						):
																					self.assertTrue(
																					    torch.all(module.weight == 1						)       ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,			)
																					self.assertTrue(
																					    torch.all(module.bias == 0						)       ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,			)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									def check_hidden_states_output(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						):
													snake_case__   :      Dict   				= model_class(__UpperCamelCase						)
													model.to(__UpperCamelCase						)
													model.eval()

													with torch.no_grad():
																	snake_case__   :      Tuple   				= model(**self._prepare_for_class(__UpperCamelCase       ,			__UpperCamelCase						)						)

													snake_case__   :      Any   				= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													snake_case__   :      Union[str, Any]   				= self.model_tester.num_stages
													self.assertEqual(len(__UpperCamelCase						)       ,			expected_num_stages + 1						)

													# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]						)       ,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,			)

									snake_case__   :      Tuple   				= self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__   :      Dict   				= ['''basic''', '''bottleneck''']
									for model_class in self.all_model_classes:
													for layer_type in layers_type:
																	snake_case__   :      Optional[Any]   				= layer_type
																	snake_case__   :      Optional[int]   				= True
																	check_hidden_states_output(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

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

																	check_hidden_states_output(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)




					@unittest.skip(reason='ResNet does not use feedforward chunking'						)
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									pass




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)




					@slow
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case__   :      List[str]   				= ResNetModel.from_pretrained(__UpperCamelCase						)
													self.assertIsNotNone(__UpperCamelCase						)




def 					UpperCamelCase__		(							)  ->   Optional[int]:
				snake_case__   :      Any   				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image





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




					@cached_property
					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									return (
									    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
									    if is_vision_available()
									    else None
									)




					@slow
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

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

									# forward pass
									with torch.no_grad():
													snake_case__   :      Any   				= model(**__UpperCamelCase						)

									# verify the logits
									snake_case__   :      str   				= torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape       ,			__UpperCamelCase						)

									snake_case__   :      List[Any]   				= torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]						).to(__UpperCamelCase						)

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





@require_torch
class 					__snake_case    (     _lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  (ResNetBackbone,) if is_torch_available() else ()
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  ResNetConfig




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      str   				= ResNetModelTester(self						)



'''simple docstring'''
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					int			)							->	list:
  seed(1			)
  return [gauss(__UpperCAmelCase ,   __UpperCAmelCase			) for _ in range(__UpperCAmelCase			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	list:
  return [k for k in range(__UpperCAmelCase			) for _ in range(instance_count[k]			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	float:
  return sum(__UpperCAmelCase			) / instance_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	float:
  return instance_count / total_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					int			)							->	float:
  lowercase				:							List[Any]									=   []  # An empty list to store all squared differences
  # iterate over number of elements in items
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of elements in inner layer of items
    for j in range(len(items[i]			)			):
      # appending squared differences to 'squared_diff' list
      squared_diff.append((items[i][j] - means[i]) ** 2			)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
  lowercase				:							List[str]									=   len(__UpperCAmelCase			)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					list			)							->	list:
  lowercase				:							Union[str, Any]									=   []
  # for loop iterates over number of elements in list
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of inner items of each element
    for j in range(len(x_items[i]			)			):
      lowercase				:							Any									=   []  # to store all discriminant values of each item as a list
      # for loop iterates over number of classes we have in our dataset
      for k in range(len(__UpperCAmelCase			)			):
        # appending values of discriminants for each class to 'temp' list
        temp.append(
            x_items[i][j] * (means[k] / variance)
            - (means[k] ** 2 / (2 * variance))
            + log(probabilities[k]			)			)
      # appending discriminant values of each item to 'results' list
      results.append(__UpperCAmelCase			)

  return [result.index(max(__UpperCAmelCase			)			) for result in results]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list			)							->	float:
  lowercase				:							List[str]									=   sum(1 for i, j in zip(__UpperCAmelCase ,   __UpperCAmelCase			) if i == j			)
  # percentage of accuracy equals to number of correct predictions divided by number
  # of all data and multiplied by 100
  return (correct / len(__UpperCAmelCase			)) * 100


_A		:      Dict						   =TypeVar('''num''')







def 				__UpperCamelCase (					__UpperCAmelCase  :					Callable[[object], num] ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Callable[[num], bool] = lambda __UpperCAmelCase			: True ,   __UpperCAmelCase  :					str | None = None ,   )							->	num:
  while True:
    try:
      lowercase				:							int									=   input_type(input(__UpperCAmelCase			).strip() or default			)
      if condition(__UpperCAmelCase			):
        return user_input
      else:
        print(f"""{user_input}: {err_msg}"""			)
        continue
    except ValueError:
      print(
          f"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""			)







def 				__UpperCamelCase (					)							->	Optional[int]:
  while True:
    print(''' Linear Discriminant Analysis '''.center(50 ,   '''*'''			)			)
    print('''*''' * 50 ,   '''\n'''			)
    print('''First of all we should specify the number of classes that'''			)
    print('''we want to generate as training dataset'''			)
    # Trying to get number of classes
    lowercase				:							List[str]									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg='''Enter the number of classes (Data Groupings): ''' ,   err_msg='''Number of classes should be positive!''' ,   )

    print('''-''' * 100			)

    # Trying to get the value of standard deviation
    lowercase				:							Dict									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x >= 0 ,   input_msg=(
            '''Enter the value of standard deviation'''
            '''(Default value is 1.0 for all classes): '''
        ) ,   err_msg='''Standard deviation should not be negative!''' ,   default='''1.0''' ,   )

    print('''-''' * 100			)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    lowercase				:							str									=   []  # An empty list to store instance counts of classes in dataset
    for i in range(__UpperCAmelCase			):
      lowercase				:							List[Any]									=   valid_input(
          input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg=(f"""Enter The number of instances for class_{i+1}: """) ,   err_msg='''Number of instances should be positive!''' ,   )
      counts.append(__UpperCAmelCase			)
    print('''-''' * 100			)

    # An empty list to store values of user-entered means of classes
    lowercase				:							int									=   []
    for a in range(__UpperCAmelCase			):
      lowercase				:							int									=   valid_input(
          input_type=__UpperCAmelCase ,   input_msg=(f"""Enter the value of mean for class_{a+1}: """) ,   err_msg='''This is an invalid value.''' ,   )
      user_means.append(__UpperCAmelCase			)
    print('''-''' * 100			)

    print('''Standard deviation: ''' ,   __UpperCAmelCase			)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Number of instances in class_{i} is: {count}"""			)
    print('''-''' * 100			)

    # print out mean values of classes separated line
    for i, user_mean in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Mean of class_{i} is: {user_mean}"""			)
    print('''-''' * 100			)

    # Generating training dataset drawn from gaussian distribution
    lowercase				:							Union[str, Any]									=   [
        gaussian_distribution(user_means[j] ,   __UpperCAmelCase ,   counts[j]			)
        for j in range(__UpperCAmelCase			)
    ]
    print('''Generated Normal Distribution: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Generating Ys to detecting corresponding classes
    lowercase				:							List[str]									=   y_generator(__UpperCAmelCase ,   __UpperCAmelCase			)
    print('''Generated Corresponding Ys: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Calculating the value of actual mean for each class
    lowercase				:							int									=   [calculate_mean(counts[k] ,   x[k]			) for k in range(__UpperCAmelCase			)]
    # for loop iterates over number of elements in 'actual_means' list and print
    # out them in separated line
    for i, actual_mean in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Actual(Real) mean of class_{i} is: {actual_mean}"""			)
    print('''-''' * 100			)

    # Calculating the value of probabilities for each class
    lowercase				:							Optional[int]									=   [
        calculate_probabilities(counts[i] ,   sum(__UpperCAmelCase			)			) for i in range(__UpperCAmelCase			)
    ]

    # for loop iterates over number of elements in 'probabilities' list and print
    # out them in separated line
    for i, probability in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Probability of class_{i} is: {probability}"""			)
    print('''-''' * 100			)

    # Calculating the values of variance for each class
    lowercase				:							Tuple									=   calculate_variance(__UpperCAmelCase ,   __UpperCAmelCase ,   sum(__UpperCAmelCase			)			)
    print('''Variance: ''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    lowercase				:							Any									=   predict_y_values(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Calculating Accuracy of the model
    print(f"""Accuracy: {accuracy(__UpperCAmelCase ,   __UpperCAmelCase			)}"""			)
    print('''-''' * 100			)
    print(''' DONE '''.center(100 ,   '''+'''			)			)

    if input('''Press any key to restart or \'q\' for quit: '''			).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(100 ,   '''-'''			) + '''\n'''			)
      break
    system('''cls''' if name == '''nt''' else '''clear'''			)  # noqa: S605


if __name__ == "__main__":
 main()