'''simple docstring'''

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

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

lowerCAmelCase_			:			Dict    	=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

lowerCAmelCase_			:			Dict    	=   {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCAmelCase_			:			Union[str, Any]    	=   {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowerCAmelCase_			:			Any    	=   {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


def __a			(						)						->				Union[str, Any]:
      '''simple docstring'''


      lowercase_     			=   (
          list(range(ord("!"					)			,		ord("~"					) + 1					)					) + list(range(ord("¡"					)			,		ord("¬"					) + 1					)					) + list(range(ord("®"					)			,		ord("ÿ"					) + 1					)					)
      )
      lowercase_     			=   bs[:]
      lowercase_     			=   0
      for b in range(2**8					):
            if b not in bs:
                  bs.append(__lowerCamelCase					)
                  cs.append(2**8 + n					)
                  n += 1
      lowercase_     			=   [chr(__lowerCamelCase					) for n in cs]
      return dict(zip(__lowerCamelCase			,		__lowerCamelCase					)					)


def __a			(						__lowerCamelCase      :    Optional[Any]					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   set()
      lowercase_     			=   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)					)
            lowercase_     			=   char
      return pairs






class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =VOCAB_FILES_NAMES
   lowerCamelCase_            =PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_            =['input_ids', 'attention_mask', 'token_type_ids']


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						int="replace" ,		__lowerCAmelCase					:						str="[CLS]" ,		__lowerCAmelCase					:						List[str]="[SEP]" ,		__lowerCAmelCase					:						List[Any]="[SEP]" ,		__lowerCAmelCase					:						Any="[CLS]" ,		__lowerCAmelCase					:						Tuple="[UNK]" ,		__lowerCAmelCase					:						Tuple="[PAD]" ,		__lowerCAmelCase					:						Any="[MASK]" ,		__lowerCAmelCase					:						Optional[int]=False ,		__lowerCAmelCase					:						int=False ,		**__lowerCAmelCase					:						List[str] ,		)   ->       List[Any]:
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else bos_token
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else eos_token
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else sep_token
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else cls_token
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else unk_token
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowercase_     			=   AddedToken(__lowerCAmelCase ,		lstrip=__lowerCAmelCase ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase ,		__lowerCAmelCase) else mask_token

         super().__init__(
             errors=__lowerCAmelCase ,		bos_token=__lowerCAmelCase ,		eos_token=__lowerCAmelCase ,		unk_token=__lowerCAmelCase ,		sep_token=__lowerCAmelCase ,		cls_token=__lowerCAmelCase ,		pad_token=__lowerCAmelCase ,		mask_token=__lowerCAmelCase ,		add_prefix_space=__lowerCAmelCase ,		add_bos_token=__lowerCAmelCase ,		**__lowerCAmelCase ,		)
         lowercase_     			=   add_bos_token

         with open(__lowerCAmelCase ,		encoding="utf-8") as vocab_handle:
               lowercase_     			=   json.load(__lowerCAmelCase)
         lowercase_     			=   {v: k for k, v in self.encoder.items()}
         lowercase_     			=   errors  # how to handle errors in decoding
         lowercase_     			=   bytes_to_unicode()
         lowercase_     			=   {v: k for k, v in self.byte_encoder.items()}
         with open(__lowerCAmelCase ,		encoding="utf-8") as merges_handle:
               lowercase_     			=   merges_handle.read().split("\n")[1:-1]
         lowercase_     			=   [tuple(merge.split()) for merge in bpe_merges]
         lowercase_     			=   dict(zip(__lowerCAmelCase ,		range(len(__lowerCAmelCase))))
         lowercase_     			=   {}
         lowercase_     			=   add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowercase_     			=   re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")


   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         return len(self.encoder)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       str:
         return dict(self.encoder ,		**self.added_tokens_encoder)


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						List[Any])   ->       Tuple:
         if token in self.cache:
               return self.cache[token]
         lowercase_     			=   tuple(__lowerCAmelCase)
         lowercase_     			=   get_pairs(__lowerCAmelCase)

         if not pairs:
               return token

         while True:
               lowercase_     			=   min(__lowerCAmelCase ,		key=lambda __lowerCAmelCase: self.bpe_ranks.get(__lowerCAmelCase ,		float("inf")))
               if bigram not in self.bpe_ranks:
                     break
               lowercase_			,				lowercase_     			=   bigram
               lowercase_     			=   []
               lowercase_     			=   0
               while i < len(__lowerCAmelCase):
                     try:
                           lowercase_     			=   word.index(__lowerCAmelCase ,		__lowerCAmelCase)
                     except ValueError:
                           new_word.extend(word[i:])
                           break
                     else:
                           new_word.extend(word[i:j])
                           lowercase_     			=   j

                     if word[i] == first and i < len(__lowerCAmelCase) - 1 and word[i + 1] == second:
                           new_word.append(first + second)
                           i += 2
                     else:
                           new_word.append(word[i])
                           i += 1
               lowercase_     			=   tuple(__lowerCAmelCase)
               lowercase_     			=   new_word
               if len(__lowerCAmelCase) == 1:
                     break
               else:
                     lowercase_     			=   get_pairs(__lowerCAmelCase)
         lowercase_     			=   " ".join(__lowerCAmelCase)
         lowercase_     			=   word
         return word


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None)   ->       List[int]:
         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase_     			=   [self.cls_token_id]
         lowercase_     			=   [self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None ,		__lowerCAmelCase					:						bool = False)   ->       List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCAmelCase ,		token_ids_a=__lowerCAmelCase ,		already_has_special_tokens=__lowerCAmelCase)

         if token_ids_a is None:
               return [1] + ([0] * len(__lowerCAmelCase)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase)) + [1] + ([0] * len(__lowerCAmelCase)) + [1]


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						List[int] ,		__lowerCAmelCase					:						Optional[List[int]] = None)   ->       List[int]:
         lowercase_     			=   [self.sep_token_id]
         lowercase_     			=   [self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						str)   ->       Optional[int]:
         lowercase_     			=   []
         for token in re.findall(self.pat ,		__lowerCAmelCase):
               lowercase_     			=   "".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCAmelCase).split(" "))
         return bpe_tokens


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						Any)   ->       int:
         return self.encoder.get(__lowerCAmelCase ,		self.encoder.get(self.unk_token))


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						Union[str, Any])   ->       Tuple:
         return self.decoder.get(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						int)   ->       Optional[Any]:
         lowercase_     			=   "".join(__lowerCAmelCase)
         lowercase_     			=   bytearray([self.byte_decoder[c] for c in text]).decode("utf-8" ,		errors=self.errors)
         return text


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Optional[str] = None)   ->       Tuple[str]:
         if not os.path.isdir(__lowerCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     			=   os.path.join(
             __lowerCAmelCase ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
         lowercase_     			=   os.path.join(
             __lowerCAmelCase ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

         with open(__lowerCAmelCase ,		"w" ,		encoding="utf-8") as f:
               f.write(json.dumps(self.encoder ,		indent=2 ,		sort_keys=__lowerCAmelCase ,		ensure_ascii=__lowerCAmelCase) + "\n")

         lowercase_     			=   0
         with open(__lowerCAmelCase ,		"w" ,		encoding="utf-8") as writer:
               writer.write("#version: 0.2\n")
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,		key=lambda __lowerCAmelCase: kv[1]):
                     if index != token_index:
                           logger.warning(
                               F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                               " Please check that the tokenizer is not corrupted!")
                           lowercase_     			=   token_index
                     writer.write(" ".join(__lowerCAmelCase) + "\n")
                     index += 1

         return vocab_file, merge_file


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[int]=False ,		**__lowerCAmelCase					:						Any)   ->       List[Any]:
         lowercase_     			=   kwargs.pop("add_prefix_space" ,		self.add_prefix_space)
         if (is_split_into_words or add_prefix_space) and (len(__lowerCAmelCase) > 0 and not text[0].isspace()):
               lowercase_     			=   " " + text
         return (text, kwargs)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						"Conversation")   ->       List[int]:
         lowercase_     			=   []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])
         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     			=   input_ids[-self.model_max_length :]
         return input_ids







'''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 __a			(						__lowerCamelCase      :    Optional[Any]=None			,		__lowerCamelCase      :    List[str]=None					)						->				Any:
      '''simple docstring'''


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





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




def __a			(						__lowerCamelCase      :    int					)						->				Tuple:
      '''simple docstring'''


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


def __a			(						__lowerCamelCase      :    Union[str, Any]					)						->				str:
      '''simple docstring'''


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





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Dict:
         lowercase_     			=   args
         lowercase_     			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

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


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         lowercase_			,				lowercase_     			=   plt.subplots()
         lowercase_     			=   "Time usage" if self.args.is_time else "Memory usage"
         lowercase_     			=   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()):
               lowercase_     			=   sorted(set(self.result_dict[model_name]["bsz"]))
               lowercase_     			=   sorted(set(self.result_dict[model_name]["seq_len"]))
               lowercase_     			=   self.result_dict[model_name]["result"]

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

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

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

                     lowercase_     			=   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.'

         lowercase_     			=   title_str[:-4]
         lowercase_     			=   "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 __a			(						)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   HfArgumentParser(__lowerCamelCase					)
      lowercase_     			=   parser.parse_args_into_dataclasses()[0]
      lowercase_     			=   Plot(args=__lowerCamelCase					)
      plot.plot()


if __name__ == "__main__":
       main()






