



'''simple docstring'''






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		List[Any]     	=					self.config_class(**self.inputs_dict				)
        self.parent.assertTrue(hasattr(a_    ,"""hidden_sizes"""				)				)
        self.parent.assertTrue(hasattr(a_    ,"""num_encoder_blocks"""				)				)







class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=13    ,a_=64    ,a_=3    ,a_=4    ,a_=[2, 2, 2, 2]    ,a_=[8, 4, 2, 1]    ,a_=[16, 32, 64, 128]    ,a_=[1, 4, 8, 16]    ,a_=False    ,a_=True    ,a_="gelu"    ,a_=0.1    ,a_=0.02    ,a_=3    ,a_=None    ,)						->  List[str]:
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Dict     	=					batch_size
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[str]     	=					num_channels
        _UpperCAmelCase    :		Optional[Any]     	=					num_encoder_blocks
        _UpperCAmelCase    :		Union[str, Any]     	=					sr_ratios
        _UpperCAmelCase    :		Tuple     	=					depths
        _UpperCAmelCase    :		Tuple     	=					hidden_sizes
        _UpperCAmelCase    :		Any     	=					downsampling_rates
        _UpperCAmelCase    :		List[str]     	=					is_training
        _UpperCAmelCase    :		Any     	=					use_labels
        _UpperCAmelCase    :		str     	=					hidden_act
        _UpperCAmelCase    :		List[Any]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		Tuple     	=					initializer_range
        _UpperCAmelCase    :		List[Any]     	=					num_labels
        _UpperCAmelCase    :		Optional[Any]     	=					scope







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[str]     	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        _UpperCAmelCase    :		Optional[int]     	=					None
        if self.use_labels:
               _UpperCAmelCase    :		Dict     	=					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,self.num_labels				)

        _UpperCAmelCase    :		Union[str, Any]     	=					PoolFormerConfig(
            image_size=self.image_size    ,num_channels=self.num_channels    ,num_encoder_blocks=self.num_encoder_blocks    ,depths=self.depths    ,hidden_sizes=self.hidden_sizes    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,initializer_range=self.initializer_range    ,)

        return config, pixel_values, labels







 def _snake_case					(				self    ,a_    ,a_    ,a_				)						->  Tuple:
        _UpperCAmelCase    :		int     	=					PoolFormerModel(config=a_				)
        model.to(a_				)
        model.eval()
        _UpperCAmelCase    :		str     	=					model(a_				)
        _UpperCAmelCase    :		Optional[int]     	=					self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.prepare_config_and_inputs()
        _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					config_and_inputs
        _UpperCAmelCase    :		List[str]     	=					{"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 UpperCAmelCase		      =    (
     {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False
 UpperCAmelCase		      =    False







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		Dict     	=					PoolFormerModelTester(self				)
        _UpperCAmelCase    :		int     	=					PoolFormerConfigTester(self    ,config_class=a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        self.config_tester.run_common_tests()







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_				)







 @unittest.skip("""PoolFormer does not use inputs_embeds"""				)
 def _snake_case					(				self				)						->  Optional[int]:
        pass







 @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""				)
 def _snake_case					(				self				)						->  int:
        pass







 def _snake_case					(				self				)						->  Any:
        def check_hidden_states_output(a_    ,a_    ,a_				):
               _UpperCAmelCase    :		Optional[int]     	=					model_class(a_				)
               model.to(a_				)
               model.eval()

               with torch.no_grad():
                      _UpperCAmelCase    :		List[Any]     	=					model(**self._prepare_for_class(a_    ,a_				)				)

               _UpperCAmelCase    :		Union[str, Any]     	=					outputs.hidden_states

               _UpperCAmelCase    :		List[Any]     	=					self.model_tester.num_encoder_blocks
               self.assertEqual(len(a_				)    ,a_				)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]				)    ,[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]    ,)

        _UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

               check_hidden_states_output(a_    ,a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[int]:
        if not self.model_tester.is_training:
               return

        _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase    :		Dict     	=					True

        for model_class in self.all_model_classes:
               if model_class in get_values(a_				):
                      continue
               _UpperCAmelCase    :		Tuple     	=					model_class(a_				)
               model.to(a_				)
               model.train()
               _UpperCAmelCase    :		List[str]     	=					self._prepare_for_class(a_    ,a_    ,return_labels=a_				)
               _UpperCAmelCase    :		Optional[int]     	=					model(**a_				).loss
               loss.backward()







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					self.model_tester.prepare_config_and_inputs_for_common()

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

               _UpperCAmelCase    :		Any     	=					["""pixel_values"""]
               self.assertListEqual(arg_names[:1]    ,a_				)







 @slow
 def _snake_case					(				self				)						->  Union[str, Any]:
        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase    :		int     	=					PoolFormerModel.from_pretrained(a_				)
               self.assertIsNotNone(a_				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       return image



@require_torch
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @slow
 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		int     	=					PoolFormerImageProcessor()
        _UpperCAmelCase    :		int     	=					PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""				).to(a_				)

        _UpperCAmelCase    :		Dict     	=					image_processor(images=prepare_img()    ,return_tensors="""pt"""				).to(a_				)

        # forward pass
        with torch.no_grad():
               _UpperCAmelCase    :		int     	=					model(**a_				)

        # verify the logits
        _UpperCAmelCase    :		List[str]     	=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape    ,a_				)

        _UpperCAmelCase    :		List[str]     	=					torch.tensor([-0.6113, 0.1685, -0.0492]				).to(a_				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,a_    ,atol=1E-4				)				)




'''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()