





"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						UpperCamelCase_	 =None

UpperCamelCase_	 =logging.get_logger(__name__)
UpperCamelCase_	 ={"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_ ,      UpperCamelCase_	 ="""[INST]""", """[/INST]"""
UpperCamelCase_ ,      UpperCamelCase_	 ="""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCamelCase_	 ="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       LlamaTokenizer
				UpperCamelCase								=       '''left'''
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']






				def __init__(					self						:       List[str],					lowerCAmelCase__						:       Tuple=None,					lowerCAmelCase__						:       int=None,					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       int="<unk>",					lowerCAmelCase__						:       Union[str, Any]="<s>",					lowerCAmelCase__						:       str="</s>",					lowerCAmelCase__						:       List[Any]=True,					lowerCAmelCase__						:       Any=False,					**lowerCAmelCase__						:       Union[str, Any],					)      -> int:




										'''simple docstring'''



										super().__init__(
										    vocab_file=lowerCAmelCase__,					tokenizer_file=lowerCAmelCase__,					clean_up_tokenization_spaces=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					bos_token=lowerCAmelCase__,					eos_token=lowerCAmelCase__,					**lowerCAmelCase__,					)
										_UpperCamelCase     :   Dict      =       add_bos_token
										_UpperCamelCase     :   Any      =       add_eos_token
										self.update_post_processor()

										_UpperCamelCase     :   List[Any]      =       vocab_file
										_UpperCamelCase     :   List[Any]      =       False if not self.vocab_file else True






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.bos_token
										_UpperCamelCase     :   List[Any]      =       self.bos_token_id

										_UpperCamelCase     :   str      =       self.eos_token
										_UpperCamelCase     :   Tuple      =       self.eos_token_id

										_UpperCamelCase     :   Optional[Any]      =       f"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
										_UpperCamelCase     :   int      =       f"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

										_UpperCamelCase     :   Union[str, Any]      =       []
										if self.add_bos_token:
																special_tokens.append((bos, bos_token_id)				)
										if self.add_eos_token:
																special_tokens.append((eos, eos_token_id)				)
										_UpperCamelCase     :   Any      =       processors.TemplateProcessing(
										    single=lowerCAmelCase__,					pair=lowerCAmelCase__,					special_tokens=lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										return self._add_eos_token






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''simple docstring'''



										return self._add_bos_token






				@add_eos_token.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       value
										self.update_post_processor()






				@add_bos_token.setter
				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[str]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       value
										self.update_post_processor()






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[str] = None				)      -> Tuple[str]:




										'''simple docstring'''



										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																    '''tokenizer.'''				)

										if not os.path.isdir(lowerCAmelCase__				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																return
										_UpperCamelCase     :   Optional[int]      =       os.path.join(
										    lowerCAmelCase__,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				):
																copyfile(self.vocab_file,					lowerCAmelCase__				)

										return (out_vocab_file,)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       "Conversation"				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       list(conversation.iter_texts()				)
										if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
										    [not is_user for is_user, msg in dialogue[1::2]]				):
																raise ValueError(
																    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

										_UpperCamelCase     :   Optional[Any]      =       []
										if len(conversation.past_user_inputs				) > 0:
																if not conversation.past_user_inputs[0].startswith(lowerCAmelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																						_UpperCamelCase     :   Any      =       (
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
										elif not dialogue[0][1].startswith(lowerCAmelCase__				) or E_SYS not in dialogue[0][1]:
																_UpperCamelCase     :   Tuple      =       (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

										dialog_tokens += sum(
										    [
										        [self.bos_token_id]
										        + self.encode(
										            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """,					add_special_tokens=lowerCAmelCase__				)
										        + [self.eos_token_id]
										        for prompt, answer in zip(dialogue[::2],					dialogue[1::2]				)
										    ],					[],					)
										if not (dialogue[-1][0]):
																raise ValueError(f"""Last message must be from user, got {dialogue[-1]['role']}"""				)
										dialog_tokens += [self.bos_token_id] + self.encode(
										    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}""",					add_special_tokens=lowerCAmelCase__				)
										return dialog_tokens









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    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




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


@dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					str     					=    field(
         metadata={"help": "The csv file to plot."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Disable logarithmic scale when plotting"}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }      ,		)
     _SCREAMING_SNAKE_CASE      :					Optional[str]     					=    field(
         default=UpperCAmelCase__      ,		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=UpperCAmelCase__      ,		metadata={"help": "List of model names that are used instead of the ones in the csv file."}				)





def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




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


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






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

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

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

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

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

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

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

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

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





def 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




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


if __name__ == "__main__":
    main()
