



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  __snake_case      (   tf.keras.layers.Layer):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_ = None  ,  a_ = None		):
											super().__init__()
											a__				      =		pad_token_id
											a__				      =		max_length
											a__				      =		vocab
											a__				      =		merges
											a__				      =		BytePairTokenizer(_lowercase  ,  _lowercase  ,  sequence_length=_lowercase		)


							@classmethod
							def _a      (							cls  ,  a_  ,  *a_  ,  **a_		):
											a__				      =		[""" """.join(_lowercase		) for m in tokenizer.bpe_ranks.keys()]
											a__				      =		tokenizer.get_vocab()
											return cls(_lowercase  ,  _lowercase  ,  *_lowercase  ,  **_lowercase		)


							@classmethod
							def _a      (							cls  ,  a_  ,  *a_  ,  **a_		):
											a__				      =		GPTaTokenizer.from_pretrained(_lowercase  ,  *_lowercase  ,  **_lowercase		)
											return cls.from_tokenizer(_lowercase  ,  *_lowercase  ,  **_lowercase		)


							@classmethod
							def _a      (							cls  ,  a_		):
											return cls(**_lowercase		)


							def _a      (							self		):
											return {
											    "vocab": self.vocab,
											    "merges": self.merges,
											    "max_length": self.max_length,
											    "pad_token_id": self.pad_token_id,
											}







							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		self.tf_tokenizer(_lowercase		)
											a__				      =		tf.ones_like(_lowercase		)

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

															if max_length is not None:
																			a__				      =		pad_model_inputs(
																			    _lowercase  ,  max_seq_length=_lowercase  ,  pad_value=self.pad_token_id		)

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






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

