'''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	:       Any						       =	["gpt2"]
__lowerCAmelCase	:       Union[str, Any]						       =	"gpt2"

if is_tf_available():






					class 						A   (   tf.Module   ):





												def __init__(      self				:							Any     ,		__a				:							Any	)							->	Optional[int]:
													super().__init__()
													__UpperCAmelCase							=			tokenizer
													__UpperCAmelCase							=			AutoConfig.from_pretrained(__a	)
													__UpperCAmelCase							=			TFGPTaLMHeadModel.from_config(__a	)






												@tf.function(input_signature=(tf.TensorSpec((None,)     ,		tf.string     ,		name='''text'''	),)	)
												def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]	)							->	int:
													__UpperCAmelCase							=			self.tokenizer(__a	)
													__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=__a     ,		attention_mask=__a	)['''logits''']

													return outputs






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





							def       snake_case__						(      self				:							int	)							->	Dict:
								super().setUp()

								__UpperCAmelCase							=			[GPTaTokenizer.from_pretrained(__a	) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__UpperCAmelCase							=			[TFGPTaTokenizer.from_pretrained(__a	) 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       snake_case__						(      self				:							Optional[Any]	)							->	List[Any]:
								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(__a     ,		tf.intaa	) == tf_outputs_values	)	)





							@slow
							def       snake_case__						(      self				:							int	)							->	List[Any]:
								for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase							=			tf.function(__a	)
									for test_inputs in self.test_sentences:
										__UpperCAmelCase							=			tf.constant(__a	)
										__UpperCAmelCase							=			compiled_tokenizer(__a	)
										__UpperCAmelCase							=			tf_tokenizer(__a	)

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





							@slow
							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase							=			ModelToSave(tokenizer=__a	)
									__UpperCAmelCase							=			tf.convert_to_tensor([self.test_sentences[0]]	)
									__UpperCAmelCase							=			model.serving(__a	)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__UpperCAmelCase							=			Path(__a	) / '''saved.model'''
										tf.saved_model.save(__a     ,		__a     ,		signatures={'''serving_default''': model.serving}	)
										__UpperCAmelCase							=			tf.saved_model.load(__a	)
									__UpperCAmelCase							=			loaded_model.signatures['''serving_default'''](__a	)['''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       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase							=			tf.convert_to_tensor([self.test_sentences[0]]	)
									__UpperCAmelCase							=			tf_tokenizer(__a	)  # Build model with some sample inputs

									__UpperCAmelCase							=			tf_tokenizer.get_config()
									__UpperCAmelCase							=			TFGPTaTokenizer.from_config(__a	)
									__UpperCAmelCase							=			model_from_config(__a	)

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






							@slow
							def       snake_case__						(      self				:							Dict	)							->	List[str]:
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__UpperCAmelCase							=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__UpperCAmelCase							=			tf.convert_to_tensor([self.test_sentences[0]]	)
										__UpperCAmelCase							=			tf_tokenizer(__a     ,		max_length=__a	)

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

										assert out_length == max_length



'''simple docstring'''


from __future__ import annotations

import math





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			u
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			temp * (u - i)
	return temp





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			int(input('''enter the numbers of values: '''       )       )
	__UpperCAmelCase							=			[]
	for _ in range(UpperCamelCase__       ):
		y.append([]       )
	for i in range(UpperCamelCase__       ):
		for j in range(UpperCamelCase__       ):
			y[i].append(UpperCamelCase__       )
			__UpperCAmelCase							=			0

	print('''enter the values of parameters in a list: '''       )
	__UpperCAmelCase							=			list(map(UpperCamelCase__    ,					input().split()       )       )

	print('''enter the values of corresponding parameters: '''       )
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			float(input()       )

	__UpperCAmelCase							=			int(input('''enter the value to interpolate: '''       )       )
	__UpperCAmelCase							=			(value - x[0]) / (x[1] - x[0])

	# for calculating forward difference table

	for i in range(1    ,					UpperCamelCase__       ):
		for j in range(n - i       ):
			__UpperCAmelCase							=			y[j + 1][i - 1] - y[j][i - 1]

	__UpperCAmelCase							=			y[0][0]
	for i in range(1    ,					UpperCamelCase__       ):
		summ += (ucal(UpperCamelCase__    ,					UpperCamelCase__       ) * y[0][i]) / math.factorial(UpperCamelCase__       )

	print(f"""the value at {value} is {summ}"""       )


if __name__ == "__main__":
					main()
