





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 :    Union[str, Any]  							= ['''gpt2''']
_lowerCamelCase :    Optional[Any]  							= '''gpt2'''

if is_tf_available():


		class 						lowerCamelCase       (tf.Module      ):







									"""simple docstring"""




									def __init__( self     :					str,			_UpperCAmelCase     :					Optional[int]						)				->			Union[str, Any]:





																"""simple docstring"""
																super().__init__()
																SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer
																SCREAMING_SNAKE_CASE__       :      int     =						AutoConfig.from_pretrained(_UpperCAmelCase						)
																SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						TFGPTaLMHeadModel.from_config(_UpperCAmelCase						)






									@tf.function(input_signature=(tf.TensorSpec((None,),			tf.string,			name="text"						),)						)
									def  A_     ( self     :					int,			_UpperCAmelCase     :					List[Any]						)				->			List[str]:





																"""simple docstring"""
																SCREAMING_SNAKE_CASE__       :      Dict     =						self.tokenizer(_UpperCAmelCase						)
																SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenized["input_ids"].to_tensor()

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

																SCREAMING_SNAKE_CASE__       :      str     =						self.model(input_ids=_UpperCAmelCase,			attention_mask=_UpperCAmelCase						)["logits"]

																return outputs


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







							"""simple docstring"""




							def  A_     ( self     :					List[str]						)				->			Dict:





														"""simple docstring"""
														super().setUp()

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

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[
														    "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",
														]
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						list(zip(self.test_sentences,			self.test_sentences[::-1]						)						)






							def  A_     ( self     :					Optional[int]						)				->			Tuple:





														"""simple docstring"""
														for tokenizer, tf_tokenizer in zip(self.tokenizers,			self.tf_tokenizers						):
																					for test_inputs in self.test_sentences:
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer([test_inputs],			return_tensors="tf"						)
																												SCREAMING_SNAKE_CASE__       :      int     =						tf_tokenizer([test_inputs]						)

																												for key in python_outputs.keys():
																																			# convert them to numpy to avoid messing with ragged tensors
																																			SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						python_outputs[key].numpy()
																																			SCREAMING_SNAKE_CASE__       :      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  A_     ( self     :					str						)				->			Dict:





														"""simple docstring"""
														for tf_tokenizer in self.tf_tokenizers:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						tf.function(_UpperCAmelCase						)
																					for test_inputs in self.test_sentences:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						tf.constant(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      str     =						compiled_tokenizer(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						tf_tokenizer(_UpperCAmelCase						)

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






							@slow
							def  A_     ( self     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														for tf_tokenizer in self.tf_tokenizers:
																					SCREAMING_SNAKE_CASE__       :      Any     =						ModelToSave(tokenizer=_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						tf.convert_to_tensor([self.test_sentences[0]]						)
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model.serving(_UpperCAmelCase						)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						Path(_UpperCAmelCase						) / "saved.model"
																												tf.saved_model.save(_UpperCAmelCase,			_UpperCAmelCase,			signatures={"serving_default": model.serving}						)
																												SCREAMING_SNAKE_CASE__       :      int     =						tf.saved_model.load(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      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  A_     ( self     :					int						)				->			int:





														"""simple docstring"""
														for tf_tokenizer in self.tf_tokenizers:
																					SCREAMING_SNAKE_CASE__       :      int     =						tf.convert_to_tensor([self.test_sentences[0]]						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						tf_tokenizer(_UpperCAmelCase						)  # Build model with some sample inputs

																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						tf_tokenizer.get_config()
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						TFGPTaTokenizer.from_config(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model_from_config(_UpperCAmelCase						)

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






							@slow
							def  A_     ( self     :					List[Any]						)				->			List[Any]:





														"""simple docstring"""
														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1_2_3_1_2_3

																					for max_length in [3, 5, 1_0_2_4]:
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tf.convert_to_tensor([self.test_sentences[0]]						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						tf_tokenizer(_UpperCAmelCase,			max_length=_UpperCAmelCase						)

																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						out["input_ids"].numpy().shape[1]

																												assert out_length == max_length













_lowerCamelCase :    dict[tuple[int, int, int], int]  							= {}




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    int:

							'''simple docstring'''






							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							SCREAMING_SNAKE_CASE__       :      Tuple     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				late + 1       )

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							SCREAMING_SNAKE_CASE__       :      Dict     =						_calculate(days - 1			,				absent + 1			,				0       )

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							SCREAMING_SNAKE_CASE__       :      Any     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				0       )

							SCREAMING_SNAKE_CASE__       :      str     =						state_late + state_absent + state_ontime

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						prizestrings
							return prizestrings




def _a (  SCREAMING_SNAKE_CASE__      :  int = 30       )	->    int:

							'''simple docstring'''






							return _calculate(SCREAMING_SNAKE_CASE__			,				absent=0			,				late=0       )


if __name__ == "__main__":
		print(solution())






