





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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		float     ,							__lowercase:		float     ,							__lowercase:		int):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		list):



  '''simple docstring'''






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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		list):



  '''simple docstring'''






  return sum(__lowercase) / instance_count


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		int):



  '''simple docstring'''






  return instance_count / total_count


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list     ,							__lowercase:		list     ,							__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase			=				[]  # An empty list to store all squared differences
  # iterate over number of elements in items
  for i in range(len(__lowercase)):
    # 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
  __lowerCAmelCase			=				len(__lowercase)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(__lowercase)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list     ,							__lowercase:		list     ,							__lowercase:		float     ,							__lowercase:		list):



  '''simple docstring'''






  __lowerCAmelCase			=				[]
  # for loop iterates over number of elements in list
  for i in range(len(__lowercase)):
    # for loop iterates over number of inner items of each element
    for j in range(len(x_items[i])):
      __lowerCAmelCase			=				[]  # 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(__lowercase)):
        # 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(__lowercase)

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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list     ,							__lowercase:		list):



  '''simple docstring'''






  __lowerCAmelCase			=				sum(1 for i, j in zip(__lowercase     ,							__lowercase) 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(__lowercase)) * 1_00


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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Callable[[object], num]     ,							__lowercase:		str     ,							__lowercase:		str     ,							__lowercase:		Callable[[num], bool] = lambda __lowercase: True     ,							__lowercase:		str | None = None     ,							):



  '''simple docstring'''






  while True:
    try:
      __lowerCAmelCase			=				input_type(input(__lowercase).strip() or default)
      if condition(__lowercase):
        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 					__SCREAMING_SNAKE_CASE  (							):



  '''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
    __lowerCAmelCase			=				valid_input(
        input_type=__lowercase     ,							condition=lambda __lowercase: x > 0     ,							input_msg='''Enter the number of classes (Data Groupings): '''     ,							err_msg='''Number of classes should be positive!'''     ,							)

    print('''-''' * 1_00)

    # Trying to get the value of standard deviation
    __lowerCAmelCase			=				valid_input(
        input_type=__lowercase     ,							condition=lambda __lowercase: 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('''-''' * 1_00)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    __lowerCAmelCase			=				[]  # An empty list to store instance counts of classes in dataset
    for i in range(__lowercase):
      __lowerCAmelCase			=				valid_input(
          input_type=__lowercase     ,							condition=lambda __lowercase: x > 0     ,							input_msg=(F'''Enter The number of instances for class_{i+1}: ''')     ,							err_msg='''Number of instances should be positive!'''     ,							)
      counts.append(__lowercase)
    print('''-''' * 1_00)

    # An empty list to store values of user-entered means of classes
    __lowerCAmelCase			=				[]
    for a in range(__lowercase):
      __lowerCAmelCase			=				valid_input(
          input_type=__lowercase     ,							input_msg=(F'''Enter the value of mean for class_{a+1}: ''')     ,							err_msg='''This is an invalid value.'''     ,							)
      user_means.append(__lowercase)
    print('''-''' * 1_00)

    print('''Standard deviation: '''     ,							__lowercase)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__lowercase     ,							1):
      print(F'''Number of instances in class_{i} is: {count}''')
    print('''-''' * 1_00)

    # print out mean values of classes separated line
    for i, user_mean in enumerate(__lowercase     ,							1):
      print(F'''Mean of class_{i} is: {user_mean}''')
    print('''-''' * 1_00)

    # Generating training dataset drawn from gaussian distribution
    __lowerCAmelCase			=				[
        gaussian_distribution(user_means[j]     ,							__lowercase     ,							counts[j])
        for j in range(__lowercase)
    ]
    print('''Generated Normal Distribution: \n'''     ,							__lowercase)
    print('''-''' * 1_00)

    # Generating Ys to detecting corresponding classes
    __lowerCAmelCase			=				y_generator(__lowercase     ,							__lowercase)
    print('''Generated Corresponding Ys: \n'''     ,							__lowercase)
    print('''-''' * 1_00)

    # Calculating the value of actual mean for each class
    __lowerCAmelCase			=				[calculate_mean(counts[k]     ,							x[k]) for k in range(__lowercase)]
    # for loop iterates over number of elements in 'actual_means' list and print
    # out them in separated line
    for i, actual_mean in enumerate(__lowercase     ,							1):
      print(F'''Actual(Real) mean of class_{i} is: {actual_mean}''')
    print('''-''' * 1_00)

    # Calculating the value of probabilities for each class
    __lowerCAmelCase			=				[
        calculate_probabilities(counts[i]     ,							sum(__lowercase)) for i in range(__lowercase)
    ]

    # for loop iterates over number of elements in 'probabilities' list and print
    # out them in separated line
    for i, probability in enumerate(__lowercase     ,							1):
      print(F'''Probability of class_{i} is: {probability}''')
    print('''-''' * 1_00)

    # Calculating the values of variance for each class
    __lowerCAmelCase			=				calculate_variance(__lowercase     ,							__lowercase     ,							sum(__lowercase))
    print('''Variance: '''     ,							__lowercase)
    print('''-''' * 1_00)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    __lowerCAmelCase			=				predict_y_values(__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase)
    print('''-''' * 1_00)

    # Calculating Accuracy of the model
    print(F'''Accuracy: {accuracy(__lowercase     ,							__lowercase)}''')
    print('''-''' * 1_00)
    print(''' DONE '''.center(1_00     ,							'''+'''))

    if input('''Press any key to restart or \'q\' for quit: ''').strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(1_00     ,							'''-''') + '''\n''')
      break
    system('''cls''' if name == '''nt''' else '''clear''')  # noqa: S605


if __name__ == "__main__":
      main()







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""blenderbot"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(   self						:		Any			,						snake_case						:		Union[str, Any]=8_008			,						snake_case						:		List[str]=128			,						snake_case						:		Tuple=2			,						snake_case						:		int=10_240			,						snake_case						:		Optional[int]=32			,						snake_case						:		Dict=24			,						snake_case						:		List[Any]=10_240			,						snake_case						:		Optional[int]=32			,						snake_case						:		List[str]=0.0			,						snake_case						:		Optional[int]=0.0			,						snake_case						:		Optional[Any]=True			,						snake_case						:		int=True			,						snake_case						:		List[str]="gelu"			,						snake_case						:		Any=2_560			,						snake_case						:		Optional[Any]=0.1			,						snake_case						:		Union[str, Any]=0.0			,						snake_case						:		List[Any]=0.0			,						snake_case						:		Dict=0.02			,						snake_case						:		int=1			,						snake_case						:		Dict=False			,						snake_case						:		List[str]=0			,						snake_case						:		Optional[Any]=1			,						snake_case						:		List[Any]=2			,						snake_case						:		List[str]=3			,						snake_case						:		List[str]=2			,						**snake_case						:		int			,						):

      """simple docstring"""





      __lowerCAmelCase			=				vocab_size
      __lowerCAmelCase			=				max_position_embeddings
      __lowerCAmelCase			=				d_model
      __lowerCAmelCase			=				encoder_ffn_dim
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				encoder_attention_heads
      __lowerCAmelCase			=				decoder_ffn_dim
      __lowerCAmelCase			=				decoder_layers
      __lowerCAmelCase			=				decoder_attention_heads
      __lowerCAmelCase			=				dropout
      __lowerCAmelCase			=				attention_dropout
      __lowerCAmelCase			=				activation_dropout
      __lowerCAmelCase			=				activation_function
      __lowerCAmelCase			=				init_std
      __lowerCAmelCase			=				encoder_layerdrop
      __lowerCAmelCase			=				decoder_layerdrop
      __lowerCAmelCase			=				use_cache
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=snake_case			,						bos_token_id=snake_case			,						eos_token_id=snake_case			,						is_encoder_decoder=snake_case			,						decoder_start_token_id=snake_case			,						encoder_no_repeat_ngram_size=snake_case			,						forced_eos_token_id=snake_case			,						**snake_case			,						)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		int							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase			=				{0: '''batch'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
        if self.use_past:
          self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
      elif self.task == "causal-lm":
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
            ]							)

      return common_inputs





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		int			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				decoder_seq_length
        __lowerCAmelCase			=				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers

        for _ in range(snake_case							):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
              )							)
      return common_inputs





    def 	A		(   self						:		str			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				seqlen
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[int]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0							)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
      __lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
      return common_inputs





    def 	A		(   self						:		str			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      elif self.task == "causal-lm":
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
      else:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		int			,						snake_case						:		str			,						snake_case						:		str			,						snake_case						:		List[str]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
            snake_case			,						snake_case			,						snake_case			,						snake_case							)





    def 	A		(   self						:		List[Any]			,						snake_case						:		Mapping[str, Mapping[int, str]]			,						snake_case						:		str							):

      """simple docstring"""





      if direction not in ["inputs", "outputs"]:
        raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''							)

      __lowerCAmelCase			=				'''past_key_values''' if direction == '''inputs''' else '''present'''
      __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers

      __lowerCAmelCase			=				'''past_encoder_sequence'''
      __lowerCAmelCase			=				'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(snake_case							):
        __lowerCAmelCase			=				{0: '''batch''', 2: decoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: decoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: encoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: encoder_sequence}
