




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    A						(__A   :    Optional[Any]=None   ,				__A   :    List[Any]=None       )   ->      Dict:







     """simple docstring"""


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







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







def    A						(__A   :    List[Any]       )   ->      Dict:







     """simple docstring"""


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


def    A						(__A   :    Dict       )   ->      int:







     """simple docstring"""


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







class __snake_case			:







   def __init__(      self		:						Optional[int]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							args
        UpperCAmelCase_          =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

        with open(self.args.csv_file					,						newline='''''') as csv_file:
             UpperCAmelCase_          =							csv.DictReader(_snake_case)
             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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        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=_snake_case					,						)
                  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(_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."""

        UpperCAmelCase_          =							title_str[:-4]
        UpperCAmelCase_          =							'''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    A						()   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							HfArgumentParser(__A       )
     UpperCAmelCase_          =							parser.parse_args_into_dataclasses()[0]
     UpperCAmelCase_          =							Plot(args=__A       )
     plot.plot()


if __name__ == "__main__":
    main()












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


snake_case_ : Any    		=       logging.get_logger(__name__)

snake_case_ : Optional[Any]    		=       {
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Optional[Any]          =  '''falcon'''
   UpperCAmelCase__  :    List[Any]          =  ['''past_key_values''']







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=65024					,						_snake_case		:						int=4544					,						_snake_case		:						int=32					,						_snake_case		:						Any=71					,						_snake_case		:						int=1e-5					,						_snake_case		:						Dict=0.0_2					,						_snake_case		:						int=True					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						Tuple=0.0					,						_snake_case		:						int=None					,						_snake_case		:						Tuple=False					,						_snake_case		:						Any=False					,						_snake_case		:						str=True					,						_snake_case		:						Any=True					,						_snake_case		:						List[str]=False					,						_snake_case		:						Tuple=11					,						_snake_case		:						Dict=11					,						**_snake_case		:						Optional[int]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        # Backward compatibility with n_embed kwarg
        UpperCAmelCase_          =							kwargs.pop('''n_embed'''					,						_snake_case)
        UpperCAmelCase_          =							hidden_size if n_embed is None else n_embed
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache
        UpperCAmelCase_          =							hidden_dropout
        UpperCAmelCase_          =							attention_dropout

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id
        UpperCAmelCase_          =							num_attention_heads if num_kv_heads is None else num_kv_heads
        UpperCAmelCase_          =							alibi
        UpperCAmelCase_          =							new_decoder_architecture
        UpperCAmelCase_          =							multi_query  # Ignored when new_decoder_architecture is True
        UpperCAmelCase_          =							parallel_attn
        UpperCAmelCase_          =							bias

        super().__init__(bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        return self.hidden_size // self.num_attention_heads







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return not self.alibi






