



'''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							:Tuple				    =					['''gpt2''']
lowerCamelCase							:Optional[Any]				    =					'''gpt2'''

if is_tf_available():







						class 	_lowerCAmelCase							(			tf.Module ):


							def __init__(self       ,      lowercase							):
										super().__init__()
										A_	:						List[Any]	=     tokenizer
										A_	:						Tuple	=     AutoConfig.from_pretrained(lowercase							)
										A_	:						Optional[Any]	=     TFGPTaLMHeadModel.from_config(lowercase							)


							@tf.function(input_signature=(tf.TensorSpec((None,)       ,      tf.string       ,      name="""text"""							),)							)
							def     _a	(self       ,      lowercase							):
										A_	:						List[str]	=     self.tokenizer(lowercase							)
										A_	:						Any	=     tokenized["""input_ids"""].to_tensor()

										A_	:						Optional[int]	=     tf.cast(input_ids_dense > 0       ,      tf.intaa							)
										# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

										A_	:						Any	=     self.model(input_ids=lowercase       ,      attention_mask=lowercase							)["""logits"""]

										return outputs







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


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

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

				A_	:						Dict	=     [
				    """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""",
				]
				A_	:						List[str]	=     list(zip(self.test_sentences       ,      self.test_sentences[::-1]							)							)


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

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

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


	@slow
	def     _a	(self							):
				for tf_tokenizer in self.tf_tokenizers:
							A_	:						Optional[Any]	=     tf.function(lowercase							)
							for test_inputs in self.test_sentences:
										A_	:						Optional[Any]	=     tf.constant(lowercase							)
										A_	:						str	=     compiled_tokenizer(lowercase							)
										A_	:						Optional[Any]	=     tf_tokenizer(lowercase							)

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


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

							A_	:						Union[str, Any]	=     tf_tokenizer.get_config()
							A_	:						Tuple	=     TFGPTaTokenizer.from_config(lowercase							)
							A_	:						Optional[Any]	=     model_from_config(lowercase							)

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


	@slow
	def     _a	(self							):
				for tf_tokenizer in self.tf_tokenizers:
							# for the test to run
							A_	:						Optional[int]	=     123123

							for max_length in [3, 5, 1024]:
										A_	:						int	=     tf.convert_to_tensor([self.test_sentences[0]]							)
										A_	:						Tuple	=     tf_tokenizer(lowercase       ,      max_length=lowercase							)

										A_	:						Dict	=     out["""input_ids"""].numpy().shape[1]

										assert out_length == max_length




'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not sentence:
						return ""
			A_	:						Optional[int]	=     dict(zip(lowerCamelCase__						,      lowerCamelCase__    )    )
			return lower_to_upper.get(sentence[0]						,      sentence[0]    ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()