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							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						list:
     seed(1							)
     return [gauss(lowerCAmelCase_    ,   lowerCAmelCase_							) for _ in range(lowerCAmelCase_							)]







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						list:
     return [k for k in range(lowerCAmelCase_							) for _ in range(instance_count[k]							)]







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						float:
     return sum(lowerCAmelCase_							) / instance_count







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						float:
     return instance_count / total_count







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						float:
     a	:     Optional[Any]						      =    []  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(lowerCAmelCase_							)							):
          # 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	:     str						      =    len(lowerCAmelCase_							)  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						list:
     a	:     Any						      =    []
     # for loop iterates over number of elements in list
     for i in range(len(lowerCAmelCase_							)							):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i]							)							):
               a	:     int						      =    []  # 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(lowerCAmelCase_							)							):
                    # 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(lowerCAmelCase_							)

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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						float:
     a	:     List[Any]						      =    sum(1 for i, j in zip(lowerCAmelCase_    ,   lowerCAmelCase_							) 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(lowerCAmelCase_							)) * 1_0_0


UpperCAmelCase				 =   TypeVar("""num""")







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = lambda lowerCAmelCase_							: True    ,   lowerCAmelCase_ = None    ,   )						->						num:
     while True:
          try:
               a	:     List[Any]						      =    input_type(input(lowerCAmelCase_							).strip() or default							)
               if condition(lowerCAmelCase_							):
                    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							(		)						->						str:
     while True:
          print(''' Linear Discriminant Analysis '''.center(5_0    ,   '''*'''							)							)
          print('''*''' * 5_0    ,   '''\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	:     Optional[int]						      =    valid_input(
              input_type=lowerCAmelCase_    ,   condition=lambda lowerCAmelCase_							: x > 0    ,   input_msg='''Enter the number of classes (Data Groupings): '''    ,   err_msg='''Number of classes should be positive!'''    ,   )

          print('''-''' * 1_0_0							)

          # Trying to get the value of standard deviation
          a	:     Dict						      =    valid_input(
              input_type=lowerCAmelCase_    ,   condition=lambda lowerCAmelCase_							: 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_0_0							)

          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          a	:     List[str]						      =    []  # An empty list to store instance counts of classes in dataset
          for i in range(lowerCAmelCase_							):
               a	:     str						      =    valid_input(
                   input_type=lowerCAmelCase_    ,   condition=lambda lowerCAmelCase_							: x > 0    ,   input_msg=(f"""Enter The number of instances for class_{i+1}: """)    ,   err_msg='''Number of instances should be positive!'''    ,   )
               counts.append(lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          # An empty list to store values of user-entered means of classes
          a	:     Optional[Any]						      =    []
          for a in range(lowerCAmelCase_							):
               a	:     int						      =    valid_input(
                   input_type=lowerCAmelCase_    ,   input_msg=(f"""Enter the value of mean for class_{a+1}: """)    ,   err_msg='''This is an invalid value.'''    ,   )
               user_means.append(lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          print('''Standard deviation: '''    ,   lowerCAmelCase_							)
          # print out the number of instances in classes in separated line
          for i, count in enumerate(lowerCAmelCase_    ,   1							):
               print(f"""Number of instances in class_{i} is: {count}"""							)
          print('''-''' * 1_0_0							)

          # print out mean values of classes separated line
          for i, user_mean in enumerate(lowerCAmelCase_    ,   1							):
               print(f"""Mean of class_{i} is: {user_mean}"""							)
          print('''-''' * 1_0_0							)

          # Generating training dataset drawn from gaussian distribution
          a	:     Any						      =    [
              gaussian_distribution(user_means[j]    ,   lowerCAmelCase_    ,   counts[j]							)
              for j in range(lowerCAmelCase_							)
          ]
          print('''Generated Normal Distribution: \n'''    ,   lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          # Generating Ys to detecting corresponding classes
          a	:     str						      =    y_generator(lowerCAmelCase_    ,   lowerCAmelCase_							)
          print('''Generated Corresponding Ys: \n'''    ,   lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          # Calculating the value of actual mean for each class
          a	:     Optional[int]						      =    [calculate_mean(counts[k]    ,   x[k]							) for k in range(lowerCAmelCase_							)]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(lowerCAmelCase_    ,   1							):
               print(f"""Actual(Real) mean of class_{i} is: {actual_mean}"""							)
          print('''-''' * 1_0_0							)

          # Calculating the value of probabilities for each class
          a	:     List[str]						      =    [
              calculate_probabilities(counts[i]    ,   sum(lowerCAmelCase_							)							) for i in range(lowerCAmelCase_							)
          ]

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(lowerCAmelCase_    ,   1							):
               print(f"""Probability of class_{i} is: {probability}"""							)
          print('''-''' * 1_0_0							)

          # Calculating the values of variance for each class
          a	:     Any						      =    calculate_variance(lowerCAmelCase_    ,   lowerCAmelCase_    ,   sum(lowerCAmelCase_							)							)
          print('''Variance: '''    ,   lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          a	:     Tuple						      =    predict_y_values(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)
          print('''-''' * 1_0_0							)

          # Calculating Accuracy of the model
          print(f"""Accuracy: {accuracy(lowerCAmelCase_    ,   lowerCAmelCase_							)}"""							)
          print('''-''' * 1_0_0							)
          print(''' DONE '''.center(1_0_0    ,   '''+'''							)							)

          if input('''Press any key to restart or \'q\' for quit: '''							).strip().lower() == "q":
               print('''\n''' + '''GoodBye!'''.center(1_0_0    ,   '''-'''							) + '''\n'''							)
               break
          system('''cls''' if name == '''nt''' else '''clear'''							)  # noqa: S605


if __name__ == "__main__":
  main()
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm