






"""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       snake_case__		(   _SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None			)    ->Optional[int]:
 return field(default_factory=lambda: default       ,	metadata=_SCREAMING_SNAKE_CASE			)


@dataclass
class 	_UpperCamelCase :



    '''simple docstring'''


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




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Union[str, Any]:
 try:
  int(_SCREAMING_SNAKE_CASE			)
  return True
 except ValueError:
  return False




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Any]:
 try:
  float(_SCREAMING_SNAKE_CASE			)
  return True
 except ValueError:
  return False


class 	_UpperCamelCase :



    '''simple docstring'''


    def __init__(      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				args
     UpperCAmelCase__ 						=				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

     with open(self.args.csv_file   ,			newline=""""""			) as csv_file:
      UpperCAmelCase__ 						=				csv.DictReader(__lowercase			)
      for row in reader:
       UpperCAmelCase__ 						=				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
        UpperCAmelCase__ 						=				int(row["""result"""]			)
       elif can_convert_to_float(row["""result"""]			):
        # value is not None
        UpperCAmelCase__ 						=				float(row["""result"""]			)






    def 		A__      (      self			):
     UpperCAmelCase__						,	UpperCAmelCase__ 						=				plt.subplots()
     UpperCAmelCase__ 						=				"""Time usage""" if self.args.is_time else """Memory usage"""
     UpperCAmelCase__ 						=				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()			):
      UpperCAmelCase__ 						=				sorted(set(self.result_dict[model_name]["""bsz"""]			)			)
      UpperCAmelCase__ 						=				sorted(set(self.result_dict[model_name]["""seq_len"""]			)			)
      UpperCAmelCase__ 						=				self.result_dict[model_name]["""result"""]

      ((UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				(
          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
      )

      UpperCAmelCase__ 						=				(
          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:
        UpperCAmelCase__ 						=				np.asarray(
            [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,			dtype=__lowercase   ,			)
       else:
        UpperCAmelCase__ 						=				np.asarray(
            [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,			dtype=np.floataa   ,			)

       ((UpperCAmelCase__)						,	(UpperCAmelCase__)) 						=				(
           ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
       )

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

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

     UpperCAmelCase__ 						=				title_str[:-4]
     UpperCAmelCase__ 						=				"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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




def       snake_case__		(   )    ->Optional[Any]:
 UpperCAmelCase__ 						=				HfArgumentParser(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				parser.parse_args_into_dataclasses()[0]
 UpperCAmelCase__ 						=				Plot(args=_SCREAMING_SNAKE_CASE			)
 plot.plot()


if __name__ == "__main__":
 main()











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :						Optional[Any]					       =							logging.get_logger(__name__)

a  :						int					       =							{
    '''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 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						'opt'
    __lowercase    :   str											=						['past_key_values']


    def __init__(      self   ,			__lowercase=50272   ,			__lowercase=768   ,			__lowercase=12   ,			__lowercase=3072   ,			__lowercase=2048   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=None   ,			__lowercase=0.1   ,			__lowercase=0.0   ,			__lowercase=12   ,			__lowercase="relu"   ,			__lowercase=0.0   ,			__lowercase=0.02   ,			__lowercase=True   ,			__lowercase=1   ,			__lowercase=2   ,			__lowercase=2   ,			__lowercase=True   ,			__lowercase=True   ,			**__lowercase   ,			):
     super().__init__(
         pad_token_id=__lowercase   ,			bos_token_id=__lowercase   ,			eos_token_id=__lowercase   ,			**__lowercase   ,			)
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     UpperCAmelCase__ 						=				ffn_dim
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				dropout
     UpperCAmelCase__ 						=				attention_dropout
     UpperCAmelCase__ 						=				activation_function
     UpperCAmelCase__ 						=				init_std
     UpperCAmelCase__ 						=				layerdrop
     UpperCAmelCase__ 						=				use_cache
     UpperCAmelCase__ 						=				do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     UpperCAmelCase__ 						=				enable_bias
     UpperCAmelCase__ 						=				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
     UpperCAmelCase__ 						=				_remove_final_layer_norm



