

'''simple docstring'''



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def   lowerCamelCase__       (      a__=None			,      a__=None)		->    Optional[Any]:



    """simple docstring"""







    return field(default_factory=lambda: default			,      metadata=a__)

@dataclass
class 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				str    =				field(
          metadata={'''help''': '''The csv file to plot.'''}      ,)
      SCREAMING_SNAKE_CASE__ :				bool    =				field(
          default=lowercase_      ,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}      ,)
      SCREAMING_SNAKE_CASE__ :				bool    =				field(
          default=lowercase_      ,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}      ,)
      SCREAMING_SNAKE_CASE__ :				bool    =				field(
          default=lowercase_      ,metadata={'''help''': '''Disable logarithmic scale when plotting'''}      ,)
      SCREAMING_SNAKE_CASE__ :				bool    =				field(
          default=lowercase_      ,metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }      ,)
      SCREAMING_SNAKE_CASE__ :				Optional[str]    =				field(
          default=lowercase_      ,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}      ,)
      SCREAMING_SNAKE_CASE__ :				Optional[List[str]]    =				list_field(
          default=lowercase_      ,metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}					)



def   lowerCamelCase__       (      a__)		->    List[Any]:



    """simple docstring"""







    try:
        int(a__)
        return True
    except ValueError:
        return False

def   lowerCamelCase__       (      a__)		->    List[Any]:



    """simple docstring"""







    try:
        float(a__)
        return True
    except ValueError:
        return False

class 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      def __init__(					self :		Union[str, Any]     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : Tuple												=		args
          _snake_case : Union[str, Any]												=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

          with open(self.args.csv_file     ,					newline=''   ) as csv_file:
              _snake_case : Any												=		csv.DictReader(snake_case   )
              for row in reader:
                  _snake_case : List[Any]												=		row['model']
                  self.result_dict[model_name]["bsz"].append(int(row['batch_size']   )   )
                  self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']   )   )
                  if can_convert_to_int(row['result']   ):
                      # value is not None
                      _snake_case : Optional[int]												=		int(row['result']   )
                  elif can_convert_to_float(row['result']   ):
                      # value is not None
                      _snake_case : Tuple												=		float(row['result']   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          _snake_case					,							_snake_case : str												=		plt.subplots()
          _snake_case : int												=		'Time usage' if self.args.is_time else 'Memory usage'
          _snake_case : Dict												=		title_str + ' for training' if self.args.is_train else title_str + ' for inference'

          if not self.args.no_log_scale:
              # set logarithm scales
              ax.set_xscale('log'   )
              ax.set_yscale('log'   )

          for axis in [ax.xaxis, ax.yaxis]:
              axis.set_major_formatter(ScalarFormatter()   )

          for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
              _snake_case : Tuple												=		sorted(set(self.result_dict[model_name]['bsz']   )   )
              _snake_case : Optional[int]												=		sorted(set(self.result_dict[model_name]['seq_len']   )   )
              _snake_case : Optional[int]												=		self.result_dict[model_name]['result']

              ((_snake_case)					,							(_snake_case)) : Dict												=		(
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              _snake_case : Any												=		(
                  model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
              )

              for inner_loop_value in inner_loop_array:
                  if self.args.plot_along_batch:
                      _snake_case : Union[str, Any]												=		np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,					dtype=snake_case     ,					)
                  else:
                      _snake_case : Union[str, Any]												=		np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,					dtype=np.floataa     ,					)

                  ((_snake_case)					,							(_snake_case)) : Any												=		(
                      ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                  )

                  _snake_case : List[Any]												=		np.asarray(snake_case     ,					snake_case   )[: len(snake_case   )]
                  plt.scatter(
                      snake_case     ,					snake_case     ,					label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""   )
                  plt.plot(snake_case     ,					snake_case     ,					'--'   )

              title_str += F""" {label_model_name} vs."""

          _snake_case : int												=		title_str[:-4]
          _snake_case : Union[str, Any]												=		'Time in s' if self.args.is_time else 'Memory in MB'

          # plot
          plt.title(snake_case   )
          plt.xlabel(snake_case   )
          plt.ylabel(snake_case   )
          plt.legend()

          if self.args.figure_png_file is not None:
              plt.savefig(self.args.figure_png_file   )
          else:
              plt.show()



def   lowerCamelCase__       (      )		->    Any:



    """simple docstring"""







    _snake_case : Optional[Any]												=		HfArgumentParser(a__)
    _snake_case : int												=		parser.parse_args_into_dataclasses()[0]
    _snake_case : Union[str, Any]												=		Plot(args=a__)
    plot.plot()


if __name__ == "__main__":
   main()






'''simple docstring'''



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE_    =   logging.getLogger(__name__)

def   lowerCamelCase__       (      a__)		->    List[Any]:



    """simple docstring"""







    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

def   lowerCamelCase__       (      )		->    Tuple:



    """simple docstring"""







    _snake_case : Optional[int]												=		argparse.ArgumentParser()
    parser.add_argument(
        '--model_name_or_path'			,      default=a__			,      type=a__			,      required=a__			,      )
    parser.add_argument('--prompt'			,      type=a__			,      default='')
    parser.add_argument('--length'			,      type=a__			,      default=2_0)
    parser.add_argument('--stop_token'			,      type=a__			,      default=a__			,      help='Token at which text generation is stopped')
    parser.add_argument(
        '--temperature'			,      type=a__			,      default=1.0			,      help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			,      )
    parser.add_argument(
        '--repetition_penalty'			,      type=a__			,      default=1.0			,      help='primarily useful for CTRL model; in that case, use 1.2')
    parser.add_argument('--k'			,      type=a__			,      default=0)
    parser.add_argument('--penalty_alpha'			,      type=a__			,      default=0.0)
    parser.add_argument('--p'			,      type=a__			,      default=0.9)

    parser.add_argument('--prefix'			,      type=a__			,      default=''			,      help='Text added prior to input.')
    parser.add_argument('--padding_text'			,      type=a__			,      default=''			,      help='Deprecated, the use of `--prefix` is preferred.')
    parser.add_argument('--xlm_language'			,      type=a__			,      default=''			,      help='Optional language when used with the XLM model.')

    parser.add_argument('--seed'			,      type=a__			,      default=4_2			,      help='random seed for initialization')
    parser.add_argument('--no_cuda'			,      action='store_true'			,      help='Avoid using CUDA when available')
    parser.add_argument(
        '--fp16'			,      action='store_true'			,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			,      )
    _snake_case : Optional[int]												=		parser.parse_args()

    _snake_case : Optional[int]												=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
    _snake_case : int												=		0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}""")

    set_seed(a__)

    # Initialize the model and tokenizer
    _snake_case : Dict												=		AutoTokenizer.from_pretrained(args.model_name_or_path)
    _snake_case : List[Any]												=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path)

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device)

    if args.fpaa:
        model.half()

    logger.info(a__)
    _snake_case : Any												=		args.prompt if args.prompt else input('Model prompt >>> ')

    _snake_case : List[str]												=		tokenizer(a__			,      return_tensors='pt'			,      add_special_tokens=a__)
    _snake_case : List[str]												=		{key: value.to(args.device) for key, value in inputs.items()}

    _snake_case : Optional[int]												=		model.generate(
        **a__			,      max_length=args.length + len(inputs['input_ids'][0])			,      penalty_alpha=args.penalty_alpha			,      top_k=args.k			,      )

    _snake_case : int												=		[]
    for generated_sequence_idx, generated_sequence in enumerate(a__):
        print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===""")
        _snake_case : Any												=		generated_sequence.tolist()

        # Decode text
        _snake_case : str												=		tokenizer.decode(a__			,      clean_up_tokenization_spaces=a__			,      add_special_tokens=a__)

        # Remove all text after the stop token
        _snake_case : str												=		text[: text.find(args.stop_token) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        _snake_case : Tuple												=		(
            prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,      clean_up_tokenization_spaces=a__)) :]
        )

        generated_sequences.append(a__)
        print(a__)

    return generated_sequences


if __name__ == "__main__":
   main()



