




import json
import sys




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with open(lowerCamelCase						,  encoding="""utf-8""" ) as f:
								__magic_name__						:						Tuple										=json.load(lowerCamelCase )

				__magic_name__						:						Optional[Any]										=["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

				for benchmark_name in sorted(lowerCamelCase ):
								__magic_name__						:						Union[str, Any]										=results[benchmark_name]

								__magic_name__						:						Union[str, Any]										=benchmark_name.split("""/""" )[-1]
								output_md.append(F"### Benchmark: {benchmark_file_name}" )

								__magic_name__						:						Tuple										="""| metric |"""
								__magic_name__						:						Optional[Any]										="""|--------|"""
								__magic_name__						:						List[Any]										="""| new / old (diff) |"""
								for metric_name in sorted(lowerCamelCase ):
												__magic_name__						:						Optional[int]										=benchmark_res[metric_name]
												__magic_name__						:						str										=metric_vals["""new"""]
												__magic_name__						:						Tuple										=metric_vals.get("""old"""						,  lowerCamelCase )
												__magic_name__						:						Tuple										=metric_vals.get("""diff"""						,  lowerCamelCase )

												__magic_name__						:						List[Any]										=F" {new_val:f}" if isinstance(lowerCamelCase						,  (int, float) ) else """None"""

												if old_val is not None:
																val_str += F" / {old_val:f}" if isinstance(lowerCamelCase						,  (int, float) ) else "None"
												if dif_val is not None:
																val_str += F" ({dif_val:f})" if isinstance(lowerCamelCase						,  (int, float) ) else "None"

												title += " " + metric_name + " |"
												lines += "---|"
												value += val_str + " |"

								output_md += [title, lines, value, " "]

				output_md.append("""</details>""" )

				with open(lowerCamelCase						,  """w"""						,  encoding="""utf-8""" ) as f:
								f.writelines("""\n""".join(lowerCamelCase ) )


if __name__ == "__main__":
							UpperCAmelCase_		:					Tuple          =  sys.argv[1]
							UpperCAmelCase_		:					List[Any]          =  sys.argv[2]

							format_json_to_md(input_json_file, output_md_file)










import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

														if max_length is not None:
																		__magic_name__					,			__magic_name__						:						Tuple										=pad_model_inputs(
																		    __snake_case     ,			max_seq_length=__snake_case     ,			pad_value=self.pad_token_id			)

										return {"attention_mask": attention_mask, "input_ids": input_ids}




