






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  							=					["gpt2"]
lowercase  							=					"gpt2"

if is_tf_available():




	class  UpperCamelCase_      (			tf.Module				):



		'''simple docstring'''




		def __init__(       self       ,			a					)       ->    List[str]:
									super().__init__()
									snake_case_				=							tokenizer
									snake_case_				=							AutoConfig.from_pretrained(a					)
									snake_case_				=							TFGPTaLMHeadModel.from_config(a					)






		@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='text'					),)					)
		def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
									snake_case_				=							self.tokenizer(a					)
									snake_case_				=							tokenized['input_ids'].to_tensor()

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

									snake_case_				=							self.model(input_ids=a       ,			attention_mask=a					)['logits']

									return outputs




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



	'''simple docstring'''




	def 							_UpperCamelCase			(       self					)       ->    Any:
								super().setUp()

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

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




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers					):
															for test_inputs in self.test_sentences:
																						snake_case_				=							tokenizer([test_inputs]       ,			return_tensors='tf'					)
																						snake_case_				=							tf_tokenizer([test_inputs]					)

																						for key in python_outputs.keys():
																													# convert them to numpy to avoid messing with ragged tensors
																													snake_case_				=							python_outputs[key].numpy()
																													snake_case_				=							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 							_UpperCamelCase			(       self					)       ->    Dict:
								for tf_tokenizer in self.tf_tokenizers:
															snake_case_				=							tf.function(a					)
															for test_inputs in self.test_sentences:
																						snake_case_				=							tf.constant(a					)
																						snake_case_				=							compiled_tokenizer(a					)
																						snake_case_				=							tf_tokenizer(a					)

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




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

															snake_case_				=							tf_tokenizer.get_config()
															snake_case_				=							TFGPTaTokenizer.from_config(a					)
															snake_case_				=							model_from_config(a					)

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






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								for tf_tokenizer in self.tf_tokenizers:
															# for the test to run
															snake_case_				=							12_31_23

															for max_length in [3, 5, 10_24]:
																						snake_case_				=							tf.convert_to_tensor([self.test_sentences[0]]					)
																						snake_case_				=							tf_tokenizer(a       ,			max_length=a					)

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

																						assert out_length == max_length













import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase  							=					logging.getLogger(__name__)




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=0.0       ,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''whether to use adafactor'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default='''linear'''       ,					metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}       ,					)





