


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        _a							(			tf.Module			):

										def __init__(							self:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Any:





												"""simple docstring"""







												super().__init__()
												lowercase__            =      tokenizer
												lowercase__            =      AutoConfig.from_pretrained(UpperCamelCase_							)
												lowercase__            =      TFGPTaLMHeadModel.from_config(UpperCamelCase_							)


										@tf.function(input_signature=(tf.TensorSpec((None,)    ,						tf.string    ,						name='''text'''							),)							)
										def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Tuple							)			->   Union[str, Any]:





												"""simple docstring"""







												lowercase__            =      self.tokenizer(UpperCamelCase_							)
												lowercase__            =      tokenized['''input_ids'''].to_tensor()

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

												lowercase__            =      self.model(input_ids=UpperCamelCase_    ,						attention_mask=UpperCamelCase_							)['''logits''']

												return outputs


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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Any:





									"""simple docstring"""







									super().setUp()

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

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

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







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

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





									"""simple docstring"""







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

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

							@slow
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Tuple:





									"""simple docstring"""







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





									"""simple docstring"""







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

											lowercase__            =      tf_tokenizer.get_config()
											lowercase__            =      TFGPTaTokenizer.from_config(UpperCamelCase_							)
											lowercase__            =      model_from_config(UpperCamelCase_							)

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


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





									"""simple docstring"""







									for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											lowercase__            =      123_123

											for max_length in [3, 5, 1_024]:
													lowercase__            =      tf.convert_to_tensor([self.test_sentences[0]]							)
													lowercase__            =      tf_tokenizer(UpperCamelCase_    ,						max_length=UpperCamelCase_							)

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

													assert out_length == max_length






class        _a							:

							def __init__(							self:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      ''''''
									lowercase__            =      ''''''
									lowercase__            =      []

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									if m == -1:
											return n + 1
									elif n == -1:
											return m + 1
									elif self.dp[m][n] > -1:
											return self.dp[m][n]
									else:
											if self.worda[m] == self.worda[n]:
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
											else:
													lowercase__            =      self.__min_dist_top_down_dp(UpperCamelCase_    ,						n - 1							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						UpperCamelCase_							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
													lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

											return self.dp[m][n]

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      [[-1 for _ in range(len(UpperCamelCase_							)							)] for _ in range(len(UpperCamelCase_							)							)]

									return self.__min_dist_top_down_dp(len(UpperCamelCase_							) - 1    ,						len(UpperCamelCase_							) - 1							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

									for i in range(m + 1							):
											for j in range(n + 1							):
													if i == 0:  # first string is empty
															lowercase__            =      j
													elif j == 0:  # second string is empty
															lowercase__            =      i
													elif worda[i - 1] == worda[j - 1]:  # last characters are equal
															lowercase__            =      self.dp[i - 1][j - 1]
													else:
															lowercase__            =      self.dp[i][j - 1]
															lowercase__            =      self.dp[i - 1][j]
															lowercase__            =      self.dp[i - 1][j - 1]
															lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)
									return self.dp[m][n]


if __name__ == "__main__":
			lowerCAmelCase       =							EditDistance()

			print('****************** Testing Edit Distance DP Algorithm ******************')
			print()

			lowerCAmelCase       =							input('Enter the first string: ').strip()
			lowerCAmelCase       =							input('Enter the second string: ').strip()

			print()
			print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
			print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
			print()
			print('*************** End of Testing Edit Distance DP Algorithm ***************')


