


'''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


_lowercase    :    Dict			     =  ["""gpt2"""]
_lowercase    :    Union[str, Any]			     =  """gpt2"""

if is_tf_available():






	class  UpperCamelCase__(     tf.Module ):




					def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   )->  Optional[Any]:

											"""simple docstring"""




											super().__init__()
											UpperCAmelCase         =			tokenizer
											UpperCAmelCase         =			AutoConfig.from_pretrained(lowerCAmelCase   )
											UpperCAmelCase         =			TFGPTaLMHeadModel.from_config(lowerCAmelCase   )





					@tf.function(input_signature=(tf.TensorSpec((None,)   ,					tf.string   ,					name='''text'''   ),)   )
					def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				str   )->  List[str]:

											"""simple docstring"""




											UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   )
											UpperCAmelCase         =			tokenized['input_ids'].to_tensor()

											UpperCAmelCase         =			tf.cast(input_ids_dense > 0   ,					tf.intaa   )
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											UpperCAmelCase         =			self.model(input_ids=lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )['logits']

											return outputs






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




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										super().setUp()

										UpperCAmelCase         =			[GPTaTokenizer.from_pretrained(lowerCAmelCase   ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										UpperCAmelCase         =			[TFGPTaTokenizer.from_pretrained(lowerCAmelCase   ) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers   ) == len(self.tf_tokenizers   )

										UpperCAmelCase         =			[
										    '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',
										]
										UpperCAmelCase         =			list(zip(self.test_sentences   ,					self.test_sentences[::-1]   )   )




				def   a__(	self    :				int   )->  Tuple:

										"""simple docstring"""




										for tokenizer, tf_tokenizer in zip(self.tokenizers   ,					self.tf_tokenizers   ):
																for test_inputs in self.test_sentences:
																						UpperCAmelCase         =			tokenizer([test_inputs]   ,					return_tensors='''tf'''   )
																						UpperCAmelCase         =			tf_tokenizer([test_inputs]   )

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												UpperCAmelCase         =			python_outputs[key].numpy()
																												UpperCAmelCase         =			tf_outputs[key].numpy()

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




				@slow
				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
																UpperCAmelCase         =			tf.function(lowerCAmelCase   )
																for test_inputs in self.test_sentences:
																						UpperCAmelCase         =			tf.constant(lowerCAmelCase   )
																						UpperCAmelCase         =			compiled_tokenizer(lowerCAmelCase   )
																						UpperCAmelCase         =			tf_tokenizer(lowerCAmelCase   )

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




				@slow
				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
																UpperCAmelCase         =			ModelToSave(tokenizer=lowerCAmelCase   )
																UpperCAmelCase         =			tf.convert_to_tensor([self.test_sentences[0]]   )
																UpperCAmelCase         =			model.serving(lowerCAmelCase   )  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						UpperCAmelCase         =			Path(lowerCAmelCase   ) / 'saved.model'
																						tf.saved_model.save(lowerCAmelCase   ,					lowerCAmelCase   ,					signatures={'''serving_default''': model.serving}   )
																						UpperCAmelCase         =			tf.saved_model.load(lowerCAmelCase   )
																UpperCAmelCase         =			loaded_model.signatures['serving_default'](lowerCAmelCase   )['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    :				List[str]   )->  Tuple:

										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
																UpperCAmelCase         =			tf.convert_to_tensor([self.test_sentences[0]]   )
																UpperCAmelCase         =			tf_tokenizer(lowerCAmelCase   )  # Build model with some sample inputs

																UpperCAmelCase         =			tf_tokenizer.get_config()
																UpperCAmelCase         =			TFGPTaTokenizer.from_config(lowerCAmelCase   )
																UpperCAmelCase         =			model_from_config(lowerCAmelCase   )

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





				@slow
				def   a__(	self    :				Tuple   )->  Union[str, Any]:

										"""simple docstring"""




										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																UpperCAmelCase         =			123123

																for max_length in [3, 5, 1024]:
																						UpperCAmelCase         =			tf.convert_to_tensor([self.test_sentences[0]]   )
																						UpperCAmelCase         =			tf_tokenizer(lowerCAmelCase   ,					max_length=lowerCAmelCase   )

																						UpperCAmelCase         =			out['input_ids'].numpy().shape[1]

																						assert out_length == max_length





'''simple docstring'''



import functools


def    lowerCamelCase__   ( A     :    list[int]   ,     A     :    list[int]  ):




						'''simple docstring'''






						if not isinstance(A   ,     A  ) or not all(isinstance(A   ,     A  ) for day in days  ):
												raise ValueError('''The parameter days should be a list of integers'''  )

						if len(A  ) != 3 or not all(isinstance(A   ,     A  ) for cost in costs  ):
												raise ValueError('''The parameter costs should be a list of three integers'''  )

						if len(A  ) == 0:
												return 0

						if min(A  ) <= 0:
												raise ValueError('''All days elements should be greater than 0'''  )

						if max(A  ) >= 3_66:
												raise ValueError('''All days elements should be less than 366'''  )

						UpperCAmelCase         =			set(A  )

						@functools.cache
						def dynamic_programming(A     :    int  ) -> int:
												if index > 3_65:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1  )

												return min(
												    costs[0] + dynamic_programming(index + 1  )   ,     costs[1] + dynamic_programming(index + 7  )   ,     costs[2] + dynamic_programming(index + 30  )   ,     )

						return dynamic_programming(1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





