code: 
  
  
  
  
  
  
  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_:	float     ,			A_:	float     ,			A_:	int		)  ->							list:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         seed(1		)
         return [gauss(A_     ,			A_		) for _ in range(A_		)]
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	list		)  ->							list:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return [k for k in range(A_		) for _ in range(instance_count[k]		)]
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	list		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return sum(A_		) / instance_count
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	int		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return instance_count / total_count
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	list     ,			A_:	list     ,			A_:	int		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=[]  # An empty list to store all squared differences
         # iterate over number of elements in items
         for i in range(len(A_		)		):
                # 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
         __UpperCAmelCase    							=len(A_		)  # Number of classes in dataset
         return 1 / (total_count - n_classes) * sum(A_		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	list     ,			A_:	list     ,			A_:	float     ,			A_:	list		)  ->							list:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=[]
         # for loop iterates over number of elements in list
         for i in range(len(A_		)		):
                # for loop iterates over number of inner items of each element
                for j in range(len(x_items[i]		)		):
                       __UpperCAmelCase    							=[]  # 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(A_		)		):
                              # 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(A_		)
  
         return [result.index(max(A_		)		) for result in results]
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	list     ,			A_:	list		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=sum(1 for i, j in zip(A_     ,			A_		) 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(A_		)) * 100
  
  
  __A  							=  TypeVar("num")
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Callable[[object], num]     ,			A_:	str     ,			A_:	str     ,			A_:	Callable[[num], bool] = lambda A_		: True     ,			A_:	str | None = None     ,			)  ->							num:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         while True:
                try:
                       __UpperCAmelCase    							=input_type(input(A_		).strip() or default		)
                       if condition(A_		):
                              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__  (				)  ->							List[Any]:
  
  
  
  
  
  
  
         """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
                __UpperCAmelCase    							=valid_input(
                    input_type=A_     ,			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
                __UpperCAmelCase    							=valid_input(
                    input_type=A_     ,			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
                __UpperCAmelCase    							=[]  # An empty list to store instance counts of classes in dataset
                for i in range(A_		):
                       __UpperCAmelCase    							=valid_input(
                           input_type=A_     ,			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(A_		)
                print("""-""" * 100		)
  
                # An empty list to store values of user-entered means of classes
                __UpperCAmelCase    							=[]
                for a in range(A_		):
                       __UpperCAmelCase    							=valid_input(
                           input_type=A_     ,			input_msg=(F'''Enter the value of mean for class_{a+1}: ''')     ,			err_msg="""This is an invalid value."""     ,			)
                       user_means.append(A_		)
                print("""-""" * 100		)
  
                print("""Standard deviation: """     ,			A_		)
                # print out the number of instances in classes in separated line
                for i, count in enumerate(A_     ,			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(A_     ,			1		):
                       print(F'''Mean of class_{i} is: {user_mean}'''		)
                print("""-""" * 100		)
  
                # Generating training dataset drawn from gaussian distribution
                __UpperCAmelCase    							=[
                    gaussian_distribution(user_means[j]     ,			A_     ,			counts[j]		)
                    for j in range(A_		)
                ]
                print("""Generated Normal Distribution: \n"""     ,			A_		)
                print("""-""" * 100		)
  
                # Generating Ys to detecting corresponding classes
                __UpperCAmelCase    							=y_generator(A_     ,			A_		)
                print("""Generated Corresponding Ys: \n"""     ,			A_		)
                print("""-""" * 100		)
  
                # Calculating the value of actual mean for each class
                __UpperCAmelCase    							=[calculate_mean(counts[k]     ,			x[k]		) for k in range(A_		)]
                # for loop iterates over number of elements in 'actual_means' list and print
                # out them in separated line
                for i, actual_mean in enumerate(A_     ,			1		):
                       print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''		)
                print("""-""" * 100		)
  
                # Calculating the value of probabilities for each class
                __UpperCAmelCase    							=[
                    calculate_probabilities(counts[i]     ,			sum(A_		)		) for i in range(A_		)
                ]
  
                # for loop iterates over number of elements in 'probabilities' list and print
                # out them in separated line
                for i, probability in enumerate(A_     ,			1		):
                       print(F'''Probability of class_{i} is: {probability}'''		)
                print("""-""" * 100		)
  
                # Calculating the values of variance for each class
                __UpperCAmelCase    							=calculate_variance(A_     ,			A_     ,			sum(A_		)		)
                print("""Variance: """     ,			A_		)
                print("""-""" * 100		)
  
                # Predicting Y values
                # storing predicted Y values in 'pre_indexes' variable
                __UpperCAmelCase    							=predict_y_values(A_     ,			A_     ,			A_     ,			A_		)
                print("""-""" * 100		)
  
                # Calculating Accuracy of the model
                print(F'''Accuracy: {accuracy(A_     ,			A_		)}'''		)
                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()
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import argparse
  import os
  import pathlib
  
  import fairseq
  import torch
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import (
      DataaVecTextConfig,
      DataaVecTextForMaskedLM,
      DataaVecTextForSequenceClassification,
      DataaVecTextModel,
  )
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  
  # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
  # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")
  
  
  logging.set_verbosity_info()
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  "Hello world! cécé herlolip"
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         dataavec.eval()  # disable dropout
         __UpperCAmelCase    							=dataavec.models[0]
         __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
         __UpperCAmelCase    							=DataaVecTextConfig(
             vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=dataavec_model.args.encoder_embed_dim     ,			num_hidden_layers=dataavec_model.args.encoder_layers     ,			num_attention_heads=dataavec_model.args.encoder_attention_heads     ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1e-5     ,			)
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
         print("""Our BERT config:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
         __UpperCAmelCase    							=torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
                __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]
  
                # self attention
                __UpperCAmelCase    							=layer.attention.self
                assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
  
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                __UpperCAmelCase    							=layer.attention.output
                assert (
                    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
                ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                __UpperCAmelCase    							=layer.intermediate
                assert (
                    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
  
                # output
                __UpperCAmelCase    							=layer.output
                assert (
                    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.bias
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.bias
         else:
                # LM Head
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[0]
         print(our_output.shape     ,			their_output.shape		)
         __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
         print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
         __UpperCAmelCase    							=torch.allclose(A_     ,			A_     ,			atol=1e-3		)
         print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
         if not success:
                raise Exception("""Something went wRoNg"""		)
  
         pathlib.Path(A_		).mkdir(parents=A_     ,			exist_ok=A_		)
         print(F'''Saving model to {pytorch_dump_folder_path}'''		)
         model.save_pretrained(A_		)
  
  
  if __name__ == "__main__":
     __A  							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--classification_head", action="store_true", help="Whether to convert a final classification head."
     )
     __A  							=  parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  