



'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_keras_nlp_available():
							from transformers.models.gpta import TFGPTaTokenizer


__lowerCAmelCase						      =       ['''gpt2''']
__lowerCAmelCase						      =       '''gpt2'''

if is_tf_available():

							class      __magic_name__						(   tf.Module       ):







														def __init__(							self :	Tuple     ,_UpperCAmelCase :	int		):
																super().__init__()
																_a					:				Optional[Any]				=							tokenizer
																_a					:				List[Any]				=							AutoConfig.from_pretrained(_UpperCAmelCase		)
																_a					:				List[str]				=							TFGPTaLMHeadModel.from_config(_UpperCAmelCase		)







														@tf.function(input_signature=(tf.TensorSpec((None,)     ,tf.string     ,name='text'		),)		)
														def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	List[str]		):
																_a					:				Optional[int]				=							self.tokenizer(_UpperCAmelCase		)
																_a					:				List[Any]				=							tokenized['input_ids'].to_tensor()

																_a					:				List[Any]				=							tf.cast(input_ids_dense > 0     ,tf.intaa		)
																# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																_a					:				Optional[int]				=							self.model(input_ids=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)['logits']

																return outputs

@require_tf
@require_keras_nlp
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	str		):
									super().setUp()

									_a					:				str				=							[GPTaTokenizer.from_pretrained(_UpperCAmelCase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
									_a					:				int				=							[TFGPTaTokenizer.from_pretrained(_UpperCAmelCase		) for checkpoint in TOKENIZER_CHECKPOINTS]
									assert len(self.tokenizers		) == len(self.tf_tokenizers		)

									_a					:				str				=							[
									    'This is a straightforward English test sentence.',
									    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
									    'Now we\'re going to add some Chinese: 一 二 三 一二三',
									    'And some much more rare Chinese: 齉 堃 齉堃',
									    'Je vais aussi écrire en français pour tester les accents',
									    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
									]
									_a					:				Tuple				=							list(zip(self.test_sentences     ,self.test_sentences[::-1]		)		)







							def 							__lowercase      (							self :	List[str]		):
									for tokenizer, tf_tokenizer in zip(self.tokenizers     ,self.tf_tokenizers		):
											for test_inputs in self.test_sentences:
													_a					:				Union[str, Any]				=							tokenizer([test_inputs]     ,return_tensors='tf'		)
													_a					:				Any				=							tf_tokenizer([test_inputs]		)

													for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															_a					:				List[str]				=							python_outputs[key].numpy()
															_a					:				Optional[int]				=							tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
															self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase     ,tf.intaa		) == tf_outputs_values		)		)







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									for tf_tokenizer in self.tf_tokenizers:
											_a					:				str				=							tf.function(_UpperCAmelCase		)
											for test_inputs in self.test_sentences:
													_a					:				List[str]				=							tf.constant(_UpperCAmelCase		)
													_a					:				Optional[int]				=							compiled_tokenizer(_UpperCAmelCase		)
													_a					:				Tuple				=							tf_tokenizer(_UpperCAmelCase		)

													for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







							@slow
							def 							__lowercase      (							self :	Dict		):
									for tf_tokenizer in self.tf_tokenizers:
											_a					:				str				=							ModelToSave(tokenizer=_UpperCAmelCase		)
											_a					:				List[Any]				=							tf.convert_to_tensor([self.test_sentences[0]]		)
											_a					:				List[Any]				=							model.serving(_UpperCAmelCase		)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
													_a					:				Tuple				=							Path(_UpperCAmelCase		) / 'saved.model'
													tf.saved_model.save(_UpperCAmelCase     ,_UpperCAmelCase     ,signatures={'serving_default': model.serving}		)
													_a					:				List[str]				=							tf.saved_model.load(_UpperCAmelCase		)
											_a					:				List[str]				=							loaded_model.signatures['serving_default'](_UpperCAmelCase		)['output_0']
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertTrue(tf.reduce_all(out == loaded_output		)		)







							@slow
							def 							__lowercase      (							self :	Dict		):
									for tf_tokenizer in self.tf_tokenizers:
											_a					:				str				=							tf.convert_to_tensor([self.test_sentences[0]]		)
											_a					:				Optional[Any]				=							tf_tokenizer(_UpperCAmelCase		)  # Build model with some sample inputs

											_a					:				Tuple				=							tf_tokenizer.get_config()
											_a					:				List[str]				=							TFGPTaTokenizer.from_config(_UpperCAmelCase		)
											_a					:				int				=							model_from_config(_UpperCAmelCase		)

											for key in from_config_output.keys():
													self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)







							@slow
							def 							__lowercase      (							self :	str		):
									for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											_a					:				List[str]				=							123123

											for max_length in [3, 5, 1024]:
													_a					:				List[Any]				=							tf.convert_to_tensor([self.test_sentences[0]]		)
													_a					:				List[Any]				=							tf_tokenizer(_UpperCAmelCase     ,max_length=_UpperCAmelCase		)

													_a					:				Tuple				=							out['input_ids'].numpy().shape[1]

													assert out_length == max_length









'''simple docstring'''

__lowerCAmelCase						      =       range(2, 20 + 1)
__lowerCAmelCase						      =       [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase						      =       {}



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[Any]:
		_a					:				Optional[int]				=							sum(a_i[j] for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  )
		_a					:				List[str]				=							sum(a_i[j] * base[j] for j in range(min(len(lowerCAmelCase_  )						,      lowerCAmelCase_  )  )  )

		_a	, _a					:				Any				=							0, 0
		_a					:				Any				=							n - i

		_a					:				List[Any]				=							memo.get(lowerCAmelCase_  )

		if sub_memo is not None:
				_a					:				Tuple				=							sub_memo.get(lowerCAmelCase_  )

				if jumps is not None and len(lowerCAmelCase_  ) > 0:
						# find and make the largest jump without going over
						_a					:				Any				=							-1
						for _k in range(len(lowerCAmelCase_  ) - 1						,      -1						,      -1  ):
								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
										_a					:				Any				=							_k
										break

						if max_jump >= 0:
								_a	, _a	, _a					:				Tuple				=							jumps[max_jump]
								# since the difference between jumps is cached, add c
								_a					:				Union[str, Any]				=							diff + c
								for j in range(min(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  ):
										_a	, _a					:				Dict				=							divmod(lowerCAmelCase_						,      10  )
								if new_c > 0:
										add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

				else:
						_a					:				Tuple				=							[]
		else:
				_a					:				Any				=							{c: []}
				_a					:				Optional[Any]				=							sub_memo

		if dn >= max_dn or c + diff >= base[k]:
				return diff, dn

		if k > ks[0]:
				while True:
						# keep doing smaller jumps
						_a	, _a					:				Dict				=							next_term(lowerCAmelCase_						,      k - 1						,      i + dn						,      lowerCAmelCase_  )
						diff += _diff
						dn += terms_jumped

						if dn >= max_dn or c + diff >= base[k]:
								break
		else:
				# would be too small a jump, just compute sequential terms instead
				_a	, _a					:				Any				=							compute(lowerCAmelCase_						,      lowerCAmelCase_						,      i + dn						,      lowerCAmelCase_  )
				diff += _diff
				dn += terms_jumped

		_a					:				Tuple				=							sub_memo[c]

		# keep jumps sorted by # of terms skipped
		_a					:				Any				=							0
		while j < len(lowerCAmelCase_  ):
				if jumps[j][1] > dn:
						break
				j += 1

		# cache the jump for this value digitsum(b) and c
		sub_memo[c].insert(lowerCAmelCase_						,      (diff, dn, k)  )
		return (diff, dn)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if i >= n:
				return 0, i
		if k > len(lowerCAmelCase_  ):
				a_i.extend([0 for _ in range(k - len(lowerCAmelCase_  )  )]  )

		# note: a_i -> b * 10^k + c
		# ds_b -> digitsum(b)
		# ds_c -> digitsum(c)
		_a					:				Any				=							i
		_a	, _a	, _a					:				Optional[int]				=							0, 0, 0
		for j in range(len(lowerCAmelCase_  )  ):
				if j >= k:
						ds_b += a_i[j]
				else:
						ds_c += a_i[j]

		while i < n:
				i += 1
				_a					:				Any				=							ds_c + ds_b
				diff += addend
				_a					:				int				=							0
				for j in range(lowerCAmelCase_  ):
						_a					:				Optional[Any]				=							a_i[j] + addend
						_a	, _a					:				Tuple				=							divmod(lowerCAmelCase_						,      10  )

						ds_c += a_i[j]

				if addend > 0:
						break

		if addend > 0:
				add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		return diff, i - start_i



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  ):
				_a					:				Optional[Any]				=							digits[j] + addend
				if s >= 10:
						_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
						_a					:				List[str]				=							addend // 10 + quotient
				else:
						_a					:				Optional[Any]				=							s
						_a					:				Optional[Any]				=							addend // 10

				if addend == 0:
						break

		while addend > 0:
				_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
				digits.append(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_ = 10**15  )      ->				int:
		_a					:				Dict				=							[1]
		_a					:				int				=							1
		_a					:				Tuple				=							0
		while True:
				_a	, _a					:				str				=							next_term(lowerCAmelCase_						,      20						,      i + dn						,      lowerCAmelCase_  )
				dn += terms_jumped
				if dn == n - i:
						break

		_a					:				Union[str, Any]				=							0
		for j in range(len(lowerCAmelCase_  )  ):
				a_n += digits[j] * 10**j
		return a_n


if __name__ == "__main__":
							print(f"""{solution() = }""")




