


'''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


UpperCAmelCase_		:   List[str]							   =		['gpt2']
UpperCAmelCase_		:   Optional[int]							   =		'gpt2'

if is_tf_available():





				class     lowercase__  (						tf.Module					):





											'''simple docstring'''


											def __init__(    self    ,					__snake_case  ):
															super().__init__()
															_SCREAMING_SNAKE_CASE :       Any     		=      tokenizer
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      AutoConfig.from_pretrained(__snake_case  )
															_SCREAMING_SNAKE_CASE :       str     		=      TFGPTaLMHeadModel.from_config(__snake_case  )






											@tf.function(input_signature=(tf.TensorSpec((None,)    ,					tf.string    ,					name="""text"""  ),)  )
											def UpperCAmelCase_	(    self    ,					__snake_case  ):
															_SCREAMING_SNAKE_CASE :       int     		=      self.tokenizer(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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 :       List[str]     		=      self.model(input_ids=__snake_case    ,					attention_mask=__snake_case  )["""logits"""]

															return outputs





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





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											super().setUp()

											_SCREAMING_SNAKE_CASE :       List[str]     		=      [GPTaTokenizer.from_pretrained(__snake_case  ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											_SCREAMING_SNAKE_CASE :       Dict     		=      [TFGPTaTokenizer.from_pretrained(__snake_case  ) 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 :       Any     		=      list(zip(self.test_sentences    ,					self.test_sentences[::-1]  )  )






							def UpperCAmelCase_	(    self  ):
											for tokenizer, tf_tokenizer in zip(self.tokenizers    ,					self.tf_tokenizers  ):
															for test_inputs in self.test_sentences:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tokenizer([test_inputs]    ,					return_tensors="""tf"""  )
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      tf_tokenizer([test_inputs]  )

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

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






							@slow
							def UpperCAmelCase_	(    self  ):
											for tf_tokenizer in self.tf_tokenizers:
															_SCREAMING_SNAKE_CASE :       Dict     		=      tf.function(__snake_case  )
															for test_inputs in self.test_sentences:
																			_SCREAMING_SNAKE_CASE :       Any     		=      tf.constant(__snake_case  )
																			_SCREAMING_SNAKE_CASE :       int     		=      compiled_tokenizer(__snake_case  )
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      tf_tokenizer(__snake_case  )

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






							@slow
							def UpperCAmelCase_	(    self  ):
											for tf_tokenizer in self.tf_tokenizers:
															_SCREAMING_SNAKE_CASE :       Any     		=      ModelToSave(tokenizer=__snake_case  )
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      tf.convert_to_tensor([self.test_sentences[0]]  )
															_SCREAMING_SNAKE_CASE :       int     		=      model.serving(__snake_case  )  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      Path(__snake_case  ) / """saved.model"""
																			tf.saved_model.save(__snake_case    ,					__snake_case    ,					signatures={"""serving_default""": model.serving}  )
																			_SCREAMING_SNAKE_CASE :       int     		=      tf.saved_model.load(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      loaded_model.signatures["""serving_default"""](__snake_case  )["""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 UpperCAmelCase_	(    self  ):
											for tf_tokenizer in self.tf_tokenizers:
															_SCREAMING_SNAKE_CASE :       int     		=      tf.convert_to_tensor([self.test_sentences[0]]  )
															_SCREAMING_SNAKE_CASE :       List[str]     		=      tf_tokenizer(__snake_case  )  # Build model with some sample inputs

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

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






							@slow
							def UpperCAmelCase_	(    self  ):
											for tf_tokenizer in self.tf_tokenizers:
															# for the test to run
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      12_3123

															for max_length in [3, 5, 1024]:
																			_SCREAMING_SNAKE_CASE :       Tuple     		=      tf.convert_to_tensor([self.test_sentences[0]]  )
																			_SCREAMING_SNAKE_CASE :       List[str]     		=      tf_tokenizer(__snake_case    ,					max_length=__snake_case  )

																			_SCREAMING_SNAKE_CASE :       Tuple     		=      out["""input_ids"""].numpy().shape[1]

																			assert out_length == max_length








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




