


"""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 	lowercase     (							A_    ,  A_    ,  A_			)-> list:
  '''simple docstring'''







  seed(1			)
  return [gauss(__UpperCamelCase    ,  __UpperCamelCase			) for _ in range(__UpperCamelCase			)]


def 	lowercase     (							A_    ,  A_			)-> list:
  '''simple docstring'''







  return [k for k in range(__UpperCamelCase			) for _ in range(instance_count[k]			)]


def 	lowercase     (							A_    ,  A_			)-> float:
  '''simple docstring'''







  return sum(__UpperCamelCase			) / instance_count


def 	lowercase     (							A_    ,  A_			)-> float:
  '''simple docstring'''







  return instance_count / total_count


def 	lowercase     (							A_    ,  A_    ,  A_			)-> float:
  '''simple docstring'''







  a		:						int 				=     []  # 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
  a		:						Any 				=     len(__UpperCamelCase			)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(__UpperCamelCase			)


def 	lowercase     (							A_    ,  A_    ,  A_    ,  A_			)-> list:
  '''simple docstring'''







  a		:						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]			)			):
      a		:						List[str] 				=     []  # 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 	lowercase     (							A_    ,  A_			)-> float:
  '''simple docstring'''







  a		:						Tuple 				=     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


__lowercase          = TypeVar("""num""")


def 	lowercase     (							A_    ,  A_    ,  A_    ,  A_ = lambda A_			: True    ,  A_ = None    ,  )-> num:
  '''simple docstring'''







  while True:
    try:
      a		:						Optional[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 	lowercase     (							)-> str:
  '''simple docstring'''







  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
    a		:						Union[str, Any] 				=     valid_input(
        input_type=__UpperCamelCase    ,  condition=lambda A_			: 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
    a		:						Optional[Any] 				=     valid_input(
        input_type=__UpperCamelCase    ,  condition=lambda A_			: 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
    a		:						Dict 				=     []  # An empty list to store instance counts of classes in dataset
    for i in range(__UpperCamelCase			):
      a		:						List[Any] 				=     valid_input(
          input_type=__UpperCamelCase    ,  condition=lambda A_			: 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
    a		:						int 				=     []
    for a in range(__UpperCamelCase			):
      a		:						Union[str, Any] 				=     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
    a		:						Tuple 				=     [
        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
    a		:						int 				=     y_generator(__UpperCamelCase    ,  __UpperCamelCase			)
    print("Generated Corresponding Ys: \n"    ,  __UpperCamelCase			)
    print("-" * 100			)

    # Calculating the value of actual mean for each class
    a		:						Dict 				=     [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
    a		:						Dict 				=     [
        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
    a		:						Dict 				=     calculate_variance(__UpperCamelCase    ,  __UpperCamelCase    ,  sum(__UpperCamelCase			)			)
    print("Variance: "    ,  __UpperCamelCase			)
    print("-" * 100			)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    a		:						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()








import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    CpmAntConfig,
					    CpmAntForCausalLM,
					    CpmAntModel,
					    CpmAntTokenizer,
					)







@require_torch
class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=2					,							UpperCamelCase__=8					,							UpperCamelCase__=True					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=False					,							UpperCamelCase__=99					,							UpperCamelCase__=32					,							UpperCamelCase__=3					,							UpperCamelCase__=4					,							UpperCamelCase__=37					,							UpperCamelCase__=32					,							UpperCamelCase__=128					,							UpperCamelCase__=8					,							UpperCamelCase__=8					,							UpperCamelCase__=8					,							UpperCamelCase__=1.0					,							UpperCamelCase__=True					,							):
					A__	:						Dict								=				parent
					A__	:						Optional[int]								=				batch_size
					A__	:						Any								=				seq_length
					A__	:						Tuple								=				is_training
					A__	:						List[str]								=				use_token_type_ids
					A__	:						List[Any]								=				use_input_mask
					A__	:						Any								=				use_labels
					A__	:						Dict								=				use_mc_token_ids
					A__	:						Tuple								=				vocab_size
					A__	:						Any								=				hidden_size
					A__	:						List[Any]								=				num_hidden_layers
					A__	:						Union[str, Any]								=				num_attention_heads
					A__	:						Tuple								=				intermediate_size
					A__	:						Union[str, Any]								=				num_buckets
					A__	:						Dict								=				max_distance
					A__	:						str								=				prompt_length
					A__	:						int								=				prompt_types
					A__	:						int								=				segment_types
					A__	:						Union[str, Any]								=				init_std
					A__	:						Dict								=				return_dict





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				{}
					A__	:						List[Any]								=				ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size		).type(torch.intaa		)
					A__	:						Dict								=				False

					A__	:						Union[str, Any]								=				self.get_config()

					return (config, input_ids)





	def 	__snake_case					(      self		):
					return CpmAntConfig(
					    vocab_size=self.vocab_size					,							hidden_size=self.hidden_size					,							num_hidden_layers=self.num_hidden_layers					,							num_attention_heads=self.num_attention_heads					,							dim_ff=self.intermediate_size					,							position_bias_num_buckets=self.num_buckets					,							position_bias_max_distance=self.max_distance					,							prompt_types=self.prompt_types					,							prompt_length=self.prompt_length					,							segment_types=self.segment_types					,							use_cache=UpperCamelCase__					,							init_std=self.init_std					,							return_dict=self.return_dict					,							)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							*UpperCamelCase__		):
					A__	:						Tuple								=				CpmAntModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__	:						Union[str, Any]								=				model(**UpperCamelCase__		).last_hidden_state

					self.parent.assertEqual(hidden_states.shape					,							(self.batch_size, self.seq_length, config.hidden_size)		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							*UpperCamelCase__		):
					A__	:						int								=				CpmAntForCausalLM(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					A__	:						Any								=				input_ids['''input_ids'''].to(UpperCamelCase__		)
					model.eval()

					A__	:						Dict								=				model(**UpperCamelCase__		)
					self.parent.assertEqual(
					    model_output.logits.shape					,							(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)					,							)





	def 	__snake_case					(      self		):
					A__							,				A__	:						List[str]								=				self.prepare_config_and_inputs()
					return config, inputs_dict







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







	'''simple docstring'''
	_lowerCAmelCase											=					(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
	_lowerCAmelCase											=					(
	    {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
	)

	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				CpmAntModelTester(self		)
					A__	:						List[str]								=				ConfigTester(self					,							config_class=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					self.config_tester.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.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()





	def 	__snake_case					(      self		):
					unittest.skip('''CPMAnt doesn\'t support input_embeds.'''		)(self.test_inputs_embeds		)





	def 	__snake_case					(      self		):
					unittest.skip(
					    '''CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'''		)(self.test_retain_grad_hidden_states_attentions		)





	def 	__snake_case					(      self		):
					A__							,				A__	:						Tuple								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_cpmant_model(UpperCamelCase__					,							UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__							,				A__	:						List[str]								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head_model(UpperCamelCase__					,							UpperCamelCase__		)







@require_torch
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				'''今天天气真好！'''
					A__	:						Any								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntModel.from_pretrained(UpperCamelCase__		)
					A__	:						str								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Union[str, Any]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Tuple								=				model(**UpperCamelCase__		).last_hidden_state

					A__	:						str								=				torch.tensor(
					    [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]					,							)
					self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,							UpperCamelCase__					,							atol=1e-2		)		)







@require_torch
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Union[str, Any]								=				'''今天天气真好！'''
					A__	:						List[str]								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						Tuple								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Union[str, Any]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Optional[Any]								=				model(**UpperCamelCase__		).logits

					A__	:						List[str]								=				torch.tensor(
					    [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]					,							)
					self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,							UpperCamelCase__					,							atol=1e-2		)		)





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Tuple								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						Optional[Any]								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						int								=				'''今天天气不错，'''
					A__	:						Dict								=				'''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'''
					A__	:						Dict								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''		)
					A__	:						Any								=				model.generate(**UpperCamelCase__		)
					A__	:						List[str]								=				tokenizer.batch_decode(UpperCamelCase__		)
					self.assertEqual(UpperCamelCase__					,							UpperCamelCase__		)





	@tooslow
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				'''openbmb/cpm-ant-10b'''
					A__	:						Tuple								=				CpmAntForCausalLM.from_pretrained(UpperCamelCase__		)
					A__	:						List[Any]								=				CpmAntTokenizer.from_pretrained(UpperCamelCase__		)
					A__	:						Optional[Any]								=				['''今天天气不错，''', '''新年快乐，万事如意！''']
					A__	:						Any								=				[
					    '''今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的''',
					    '''新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的''',
					]
					A__	:						Optional[int]								=				tokenizer(UpperCamelCase__					,							return_tensors='''pt'''					,							padding=UpperCamelCase__		)
					A__	:						Any								=				model.generate(**UpperCamelCase__		)
					A__	:						Optional[Any]								=				tokenizer.batch_decode(UpperCamelCase__		)
					self.assertEqual(UpperCamelCase__					,							UpperCamelCase__		)