





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


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

_lowerCAmelCase				:   Dict						       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				:   str						       =						{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCAmelCase				:   Optional[Any]						       =						{
    '''Salesforce/codegen-350M-mono''': 2_048,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']
     __UpperCamelCase            =  CodeGenTokenizer



     def __init__(				self							:str      ,   snake_case							:Tuple=None      ,   snake_case							:Any=None      ,   snake_case							:Optional[Any]=None      ,   snake_case							:Optional[int]="<|endoftext|>"      ,   snake_case							:List[Any]="<|endoftext|>"      ,   snake_case							:List[Any]="<|endoftext|>"      ,   snake_case							:int=False      ,   **snake_case							:List[Any]      ,   ):





        '''simple docstring'''




        super().__init__(
            snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   unk_token=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   add_prefix_space=snake_case      ,   **snake_case      ,   )

        if kwargs.pop("add_bos_token"      ,   snake_case	):
           A_       :					int        =      kwargs.pop("name_or_path"      ,   ""	)
           raise ValueError(
               "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
               "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
               f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
               f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
               "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
               " so that the fast tokenizer works correctly."	)

        A_       :					List[str]        =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
           A_       :					Any        =      getattr(snake_case      ,   pre_tok_state.pop("type"	)	)
           A_       :					Tuple        =      add_prefix_space
           A_       :					Tuple        =      pre_tok_class(**snake_case	)

        A_       :					List[Any]        =      add_prefix_space



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   *snake_case							:Any      ,   **snake_case							:int	):





        '''simple docstring'''




        A_       :					int        =      kwargs.get("is_split_into_words"      ,   snake_case	)
        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   *snake_case							:int      ,   **snake_case							:Any	):





        '''simple docstring'''




        A_       :					Any        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        A_       :					List[Any]        =      self._tokenizer.model.save(snake_case      ,   name=snake_case	)
        return tuple(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]      ,   snake_case							:bool = False      ,   snake_case							:bool = None      ,   snake_case							:Optional[List[str]] = None      ,   **snake_case							:int      ,   ):





        '''simple docstring'''




        A_       :					List[Any]        =      super().decode(
            token_ids=snake_case      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   **snake_case      ,   )

        if truncate_before_pattern is not None and len(snake_case	) > 0:
           A_       :					List[Any]        =      self.truncate(snake_case      ,   snake_case	)

        return decoded_text





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





        '''simple docstring'''




        def find_re(snake_case							:List[str]      ,   snake_case							:Optional[Any]      ,   snake_case							:List[str]	):
           A_       :					int        =      pattern.search(snake_case      ,   snake_case	)
           return m.start() if m else -1

        A_       :					List[str]        =      [re.compile(snake_case      ,   re.MULTILINE	) for pattern in truncate_before_pattern]

        A_       :					Dict        =      list(re.finditer("^print"      ,   snake_case      ,   re.MULTILINE	)	)

        if len(snake_case	) > 1:
           A_       :					List[Any]        =      completion[: prints[1].start()]

        A_       :					Any        =      list(re.finditer("^def"      ,   snake_case      ,   re.MULTILINE	)	)

        if len(snake_case	) > 1:
           A_       :					Tuple        =      completion[: defs[1].start()]

        A_       :					int        =      0

        A_       :					List[str]        =      [
            pos for pos in [find_re(snake_case      ,   snake_case      ,   snake_case	) for terminal in terminals] if pos != -1
        ]

        if len(snake_case	) > 0:
           return completion[: min(snake_case	)]
        else:
           return completion







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    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



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




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



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





        '''simple docstring'''




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

        with open(self.args.csv_file      ,   newline=""	) as csv_file:
           A_       :					Union[str, Any]        =      csv.DictReader(snake_case	)
           for row in reader:
              A_       :					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
                 A_       :					List[Any]        =      int(row["result"]	)
              elif can_convert_to_float(row["result"]	):
                 # value is not None
                 A_       :					Optional[Any]        =      float(row["result"]	)





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_   ,    A_       :					List[str]        =      plt.subplots()
        A_       :					List[str]        =      "Time usage" if self.args.is_time else "Memory usage"
        A_       :					Optional[Any]        =      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_       :					int        =      sorted(set(self.result_dict[model_name]["bsz"]	)	)
           A_       :					List[str]        =      sorted(set(self.result_dict[model_name]["seq_len"]	)	)
           A_       :					Dict        =      self.result_dict[model_name]["result"]

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					List[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:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					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      ,   )

              ((A_)   ,    (A_))       :					Optional[Any]        =      (
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )

              A_       :					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."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "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   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
