code: 
  '''simple docstring'''
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  
  import numpy as np
  import regex as re
  
  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 ...tokenization_utils import AddedToken, PreTrainedTokenizer
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "vocab_file": "vocab.json",
      "merges_file": "merges.txt",
  }
  
  __A          =    {
      "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",
      },
  }
  
  __A          =    {
      "Salesforce/codegen-350M-mono": 2_048,
  }
  
  
  
  
  
  @lru_cache()
  def  _A		(							):
  
  						'''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(lowercase__	)
  																		cs.append(2**8 + n	)
  																		n += 1
  						lowercase__							 =       [chr(lowercase__	) for n in cs]
  						return dict(zip(lowercase__		,		lowercase__	)	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  
  						'''simple docstring'''
  
  
  
  
  
  						lowercase__							 =       set()
  						lowercase__							 =       word[0]
  						for char in word[1:]:
  												pairs.add((prev_char, char)	)
  												lowercase__							 =       char
  						return pairs
  
  
  
  
  
  
  
  class 			A							(       _SCREAMING_SNAKE_CASE				):
  			lowerCamelCase							:							str       		=  VOCAB_FILES_NAMES
  			lowerCamelCase							:							Union[str, Any]       		=  PRETRAINED_VOCAB_FILES_MAP
  			lowerCamelCase							:							List[Any]       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			lowerCamelCase							:							Tuple       		=  ['input_ids', 'attention_mask']
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__="replace"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__=None       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       ,     )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       AddedToken(UpperCamelCase__       ,     lstrip=UpperCamelCase__       ,     rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__       ,     UpperCamelCase__       ) else bos_token
  									lowercase__							 =       AddedToken(UpperCamelCase__       ,     lstrip=UpperCamelCase__       ,     rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__       ,     UpperCamelCase__       ) else eos_token
  									lowercase__							 =       AddedToken(UpperCamelCase__       ,     lstrip=UpperCamelCase__       ,     rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__       ,     UpperCamelCase__       ) else unk_token
  									lowercase__							 =       AddedToken(UpperCamelCase__       ,     lstrip=UpperCamelCase__       ,     rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__       ,     UpperCamelCase__       ) else pad_token
  									super().__init__(
  									    errors=UpperCamelCase__       ,     unk_token=UpperCamelCase__       ,     bos_token=UpperCamelCase__       ,     eos_token=UpperCamelCase__       ,     pad_token=UpperCamelCase__       ,     add_prefix_space=UpperCamelCase__       ,     add_bos_token=UpperCamelCase__       ,     **UpperCamelCase__       ,     )
  									lowercase__							 =       add_bos_token
  
  									with open(UpperCamelCase__       ,     encoding="""utf-8"""       ) as vocab_handle:
  															lowercase__							 =       json.load(UpperCamelCase__       )
  									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(UpperCamelCase__       ,     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(UpperCamelCase__       ,     range(len(UpperCamelCase__       )       )       )       )
  									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
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return len(self.encoder       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return dict(self.encoder       ,     **self.added_tokens_encoder       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if token in self.cache:
  															return self.cache[token]
  									lowercase__							 =       tuple(UpperCamelCase__       )
  									lowercase__							 =       get_pairs(UpperCamelCase__       )
  
  									if not pairs:
  															return token
  
  									while True:
  															lowercase__							 =       min(UpperCamelCase__       ,     key=lambda lowerCamelCase__       : self.bpe_ranks.get(UpperCamelCase__       ,     float("""inf"""       )       )       )
  															if bigram not in self.bpe_ranks:
  																					break
  															lowercase__ ,						lowercase__							 =       bigram
  															lowercase__							 =       []
  															lowercase__							 =       0
  															while i < len(UpperCamelCase__       ):
  																					try:
  																											lowercase__							 =       word.index(UpperCamelCase__       ,     UpperCamelCase__       )
  																					except ValueError:
  																											new_word.extend(word[i:]       )
  																											break
  																					else:
  																											new_word.extend(word[i:j]       )
  																											lowercase__							 =       j
  
  																					if word[i] == first and i < len(UpperCamelCase__       ) - 1 and word[i + 1] == second:
  																											new_word.append(first + second       )
  																											i += 2
  																					else:
  																											new_word.append(word[i]       )
  																											i += 1
  															lowercase__							 =       tuple(UpperCamelCase__       )
  															lowercase__							 =       new_word
  															if len(UpperCamelCase__       ) == 1:
  																					break
  															else:
  																					lowercase__							 =       get_pairs(UpperCamelCase__       )
  									lowercase__							 =       """ """.join(UpperCamelCase__       )
  									lowercase__							 =       word
  									return word
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.add_bos_token:
  															lowercase__							 =       [self.bos_token_id]
  									else:
  															lowercase__							 =       []
  
  									lowercase__							 =       bos_token_ids + token_ids_a
  
  									if token_ids_a is None:
  															return output
  
  									return output + bos_token_ids + token_ids_a
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       []
  									for token in re.findall(self.pat       ,     UpperCamelCase__       ):
  															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(UpperCamelCase__       ).split(""" """       )       )
  									return bpe_tokens
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.encoder.get(UpperCamelCase__       ,     self.encoder.get(self.unk_token       )       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.decoder.get(UpperCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       """""".join(UpperCamelCase__       )
  									lowercase__							 =       bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""       ,     errors=self.errors       )
  									return text
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not os.path.isdir(UpperCamelCase__       ):
  															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
  															return
  									lowercase__							 =       os.path.join(
  									    UpperCamelCase__       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
  									lowercase__							 =       os.path.join(
  									    UpperCamelCase__       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )
  
  									with open(UpperCamelCase__       ,     """w"""       ,     encoding="""utf-8"""       ) as f:
  															f.write(json.dumps(self.encoder       ,     indent=2       ,     sort_keys=UpperCamelCase__       ,     ensure_ascii=UpperCamelCase__       ) + """\n"""       )
  
  									lowercase__							 =       0
  									with open(UpperCamelCase__       ,     """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(UpperCamelCase__       ) + """\n"""       )
  																					index += 1
  
  									return vocab_file, merge_file
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       kwargs.pop("""add_prefix_space"""       ,     self.add_prefix_space       )
  									if is_split_into_words or add_prefix_space:
  															lowercase__							 =       """ """ + text
  									return (text, kwargs)
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     **lowerCamelCase__       ,     )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super()._decode(
  									    token_ids=UpperCamelCase__       ,     skip_special_tokens=UpperCamelCase__       ,     clean_up_tokenization_spaces=UpperCamelCase__       ,     **UpperCamelCase__       ,     )
  
  									if truncate_before_pattern is not None and len(UpperCamelCase__       ) > 0:
  															lowercase__							 =       self.truncate(UpperCamelCase__       ,     UpperCamelCase__       )
  
  									return decoded_text
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									def find_re(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       pattern.search(UpperCamelCase__       ,     UpperCamelCase__       )
  															return m.start() if m else -1
  
  									lowercase__							 =       [re.compile(UpperCamelCase__       ,     re.MULTILINE       ) for pattern in truncate_before_pattern]
  
  									lowercase__							 =       list(re.finditer("""^print"""       ,     UpperCamelCase__       ,     re.MULTILINE       )       )
  
  									if len(UpperCamelCase__       ) > 1:
  															lowercase__							 =       completion[: prints[1].start()]
  
  									lowercase__							 =       list(re.finditer("""^def"""       ,     UpperCamelCase__       ,     re.MULTILINE       )       )
  
  									if len(UpperCamelCase__       ) > 1:
  															lowercase__							 =       completion[: defs[1].start()]
  
  									lowercase__							 =       0
  
  									lowercase__							 =       [
  									    pos for pos in [find_re(UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ) for terminal in terminals] if pos != -1
  									]
  
  									if len(UpperCamelCase__       ) > 0:
  															return completion[: min(UpperCamelCase__       )]
  									else:
  															return completion
  
  
  
  
  
  
  
style_context: 
  '''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		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							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  _A		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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 A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  