code: 
  
  
  
  
  
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  __snake_case							      =						{
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  __snake_case							      =						[
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  def 						_A     (       ):
      UpperCamelCase :Optional[int]										=     argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.''' )
      parser.add_argument('''model_name'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''The name of the model to try.''' )
      parser.add_argument(
          '''--tokenizer_name'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''The name of the tokenizer (if different from the model.''' )
      parser.add_argument('''--is_causal'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''Whether or not the model is causal.''' )
      parser.add_argument(
          '''--model_revision'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''The revision to use for the model checkpoint.''' )
      parser.add_argument('''--torch_dtype'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''The dtype for the model.''' )
      parser.add_argument('''--disk_offload'''  ,						action='''store_true''' )
  
      UpperCamelCase :int										=     parser.parse_args()
  
      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
          UpperCamelCase :Optional[int]										=     DEFAULT_MODELS[args.model_name]
          UpperCamelCase :List[str]										=     defaults['''model''']
          if args.tokenizer_name is None:
              UpperCamelCase :Dict										=     defaults.get('''tokenizer'''  ,						args.model_name )
          if args.is_causal is None:
              UpperCamelCase :Tuple										=     defaults['''is_causal''']
          if args.model_revision is None:
              UpperCamelCase :List[str]										=     defaults.get('''model_revision'''  ,						'''main''' )
  
      if args.is_causal is None:
          raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.''' )
      if args.tokenizer_name is None:
          UpperCamelCase :str										=     args.model_name
      if args.model_revision is None:
          UpperCamelCase :Tuple										=     '''main'''
  
      return args
  
  
  def 						_A     (       ):
      transformers.utils.logging.set_verbosity_error()
      UpperCamelCase :int										=     parse_args()
  
      if args.torch_dtype is None:
          UpperCamelCase :Tuple										=     AutoConfig.from_pretrained(args.model_name )
          UpperCamelCase :Union[str, Any]										=     getattr(SCREAMING_SNAKE_CASE__  ,						'''torch_dtype'''  ,						torch.floataa )
      else:
          UpperCamelCase :Tuple										=     getattr(SCREAMING_SNAKE_CASE__  ,						args.torch_dtype )
      UpperCamelCase :Optional[Any]										=     AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      UpperCamelCase :str										=     {
          '''torch_dtype''': torch_dtype,
          '''revision''': args.model_revision,
      }
      if args.disk_offload:
          UpperCamelCase :Tuple										=     '''tmp_offload'''
          UpperCamelCase :Dict										=     True
  
      UpperCamelCase :Dict										=     start_measure()
      UpperCamelCase :List[str]										=     model_cls.from_pretrained(args.model_name  ,						device_map='''auto'''  ,						**SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :List[Any]										=     end_measure(SCREAMING_SNAKE_CASE__ )
      log_measures(SCREAMING_SNAKE_CASE__  ,						'''Model loading''' )
  
      UpperCamelCase :int										=     compute_module_sizes(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :List[str]										=     {v: 0 for v in model.hf_device_map.values()}
      for module, device in model.hf_device_map.items():
          device_size[device] += module_sizes[module]
      UpperCamelCase :List[Any]										=     '''\n'''.join([F'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()] )
      print(F'''\nTheoretical use:\n{message}''' )
  
      UpperCamelCase :Any										=     AutoTokenizer.from_pretrained(args.tokenizer_name )
  
      UpperCamelCase :Optional[Any]										=     start_measure()
      UpperCamelCase :Tuple										=     []
      UpperCamelCase :Union[str, Any]										=     []
      UpperCamelCase :Optional[Any]										=     []
      for prompt in PROMPTS:
          UpperCamelCase :List[str]										=     tokenizer(SCREAMING_SNAKE_CASE__  ,						return_tensors='''pt''' ).to(0 )
          UpperCamelCase :Tuple										=     inputs['''input_ids'''][0].tolist()
          UpperCamelCase :Any										=     time.time()
          UpperCamelCase :Optional[Any]										=     model.generate(inputs['''input_ids'''] )
          UpperCamelCase :Any										=     time.time()
          UpperCamelCase :Dict										=     outputs[0].tolist()
          UpperCamelCase :Dict										=     len(SCREAMING_SNAKE_CASE__ ) if outputs[: len(SCREAMING_SNAKE_CASE__ )] != tokens else len(SCREAMING_SNAKE_CASE__ ) - len(SCREAMING_SNAKE_CASE__ )
          UpperCamelCase :str										=     after_generate - before_generate
  
          UpperCamelCase :Optional[int]										=     tokenizer.decode(SCREAMING_SNAKE_CASE__  ,						skip_special_tokens=SCREAMING_SNAKE_CASE__ )
          texts_outs.append(SCREAMING_SNAKE_CASE__ )
          generation_times.append(SCREAMING_SNAKE_CASE__ )
          gen_tokens.append(SCREAMING_SNAKE_CASE__ )
          print(F'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n''' )
  
      UpperCamelCase :int										=     end_measure(SCREAMING_SNAKE_CASE__ )
      log_measures(SCREAMING_SNAKE_CASE__  ,						'''Model generation''' )
  
      UpperCamelCase :Optional[Any]										=     [gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )]
      UpperCamelCase :Any										=     sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
      print(F'''Average time of generation per token: {avg_gen:.2f}s''' )
      print(F'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s''' )
      UpperCamelCase :Tuple										=     sum(generation_times_per_token[1:] ) / (len(SCREAMING_SNAKE_CASE__ ) - 1)
      print(F'''Average time of generation per token (excluding the first): {avg_gen:.2f}s''' )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections.abc import Callable
  from math import log
  from os import name, system
  from random import gauss, seed
  from typing import TypeVar
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			int ):
      seed(1 )
      return [gauss(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return sum(SCREAMING_SNAKE_CASE__ ) / instance_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int ):
      return instance_count / total_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Optional[Any]										=     []  # An empty list to store all squared differences
      # iterate over number of elements in items
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          # 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 :Tuple										=     len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
      return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :Dict										=     []
      # for loop iterates over number of elements in list
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i] ) ):
              UpperCamelCase :List[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(SCREAMING_SNAKE_CASE__ ) ):
                  # 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(SCREAMING_SNAKE_CASE__ )
  
      return [result.index(max(SCREAMING_SNAKE_CASE__ ) ) for result in results]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :List[Any]										=     sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) 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(SCREAMING_SNAKE_CASE__ )) * 100
  
  
  __snake_case							      =						TypeVar("""num""")
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Callable[[object], num]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE__ : True  ,						SCREAMING_SNAKE_CASE__						:			str | None = None  ,						):
      while True:
          try:
              UpperCamelCase :List[Any]										=     input_type(input(SCREAMING_SNAKE_CASE__ ).strip() or default )
              if condition(SCREAMING_SNAKE_CASE__ ):
                  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 						_A     (       ):
      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 :List[Any]										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : 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 :Any										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : 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 :Tuple										=     []  # An empty list to store instance counts of classes in dataset
          for i in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :List[Any]										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : x > 0  ,						input_msg=(F'''Enter The number of instances for class_{i+1}: ''')  ,						err_msg='''Number of instances should be positive!'''  ,						)
              counts.append(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # An empty list to store values of user-entered means of classes
          UpperCamelCase :Union[str, Any]										=     []
          for a in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :str										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						input_msg=(F'''Enter the value of mean for class_{a+1}: ''')  ,						err_msg='''This is an invalid value.'''  ,						)
              user_means.append(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          print('''Standard deviation: '''  ,						SCREAMING_SNAKE_CASE__ )
          # print out the number of instances in classes in separated line
          for i, count in enumerate(SCREAMING_SNAKE_CASE__  ,						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(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Mean of class_{i} is: {user_mean}''' )
          print('''-''' * 100 )
  
          # Generating training dataset drawn from gaussian distribution
          UpperCamelCase :Tuple										=     [
              gaussian_distribution(user_means[j]  ,						SCREAMING_SNAKE_CASE__  ,						counts[j] )
              for j in range(SCREAMING_SNAKE_CASE__ )
          ]
          print('''Generated Normal Distribution: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Generating Ys to detecting corresponding classes
          UpperCamelCase :int										=     y_generator(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''Generated Corresponding Ys: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating the value of actual mean for each class
          UpperCamelCase :Tuple										=     [calculate_mean(counts[k]  ,						x[k] ) for k in range(SCREAMING_SNAKE_CASE__ )]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Actual(Real) mean of class_{i} is: {actual_mean}''' )
          print('''-''' * 100 )
  
          # Calculating the value of probabilities for each class
          UpperCamelCase :Dict										=     [
              calculate_probabilities(counts[i]  ,						sum(SCREAMING_SNAKE_CASE__ ) ) for i in range(SCREAMING_SNAKE_CASE__ )
          ]
  
          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Probability of class_{i} is: {probability}''' )
          print('''-''' * 100 )
  
          # Calculating the values of variance for each class
          UpperCamelCase :int										=     calculate_variance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						sum(SCREAMING_SNAKE_CASE__ ) )
          print('''Variance: '''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          UpperCamelCase :Any										=     predict_y_values(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating Accuracy of the model
          print(F'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )}''' )
          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()
  
  
  
  
  
  