


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


A			:						Optional[int]        =  ['gpt2']
A			:						Any        =  'gpt2'

if is_tf_available():



	class 				A       (			tf.Module				):
						'''simple docstring'''






						def __init__(self       :		List[str] ,   _UpperCAmelCase       :		List[Any]						)		->  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       :		Dict ,   _UpperCAmelCase       :		Tuple						)		->  str:




								"""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				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[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       :		Optional[int]						)		->  Union[str, Any]:




							"""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       :		Optional[Any]						)		->  Union[str, Any]:




							"""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       :		Tuple						)		->  int:




							"""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       :		int						)		->  Optional[Any]:




							"""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]						)		->  str:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									lowercase__	     =						12_3123

									for max_length in [3, 5, 1024]:
											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





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,   """prajjwal1/bert-tiny"""						)
							lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""						)

							lowercase__	     =						bertabert.config.encoder.vocab_size
							lowercase__	     =						tokenizer.sep_token_id
							lowercase__	     =						tokenizer.cls_token_id
							lowercase__	     =						128

							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""train[:1%]"""						)
							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""validation[:1%]"""						)

							lowercase__	     =						train_dataset.select(range(32						)						)
							lowercase__	     =						val_dataset.select(range(16						)						)

							lowercase__	     =						4

							def _map_to_encoder_decoder_inputs(_UpperCAmelCase       :		Any						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__	     =						tokenizer(batch["""article"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=512						)
									lowercase__	     =						tokenizer(batch["""highlights"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=128						)
									lowercase__	     =						inputs.input_ids
									lowercase__	     =						inputs.attention_mask

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						outputs.attention_mask

									assert all(len(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
									assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

									return batch

							def _compute_metrics(_UpperCAmelCase       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						train_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							train_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							# same for validation dataset
							lowercase__	     =						val_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							val_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						SeqaSeqTrainingArguments(
							    output_dir=_UpperCAmelCase ,   per_device_train_batch_size=_UpperCAmelCase ,   per_device_eval_batch_size=_UpperCAmelCase ,   predict_with_generate=_UpperCAmelCase ,   evaluation_strategy="""steps""" ,   do_train=_UpperCAmelCase ,   do_eval=_UpperCAmelCase ,   warmup_steps=0 ,   eval_steps=2 ,   logging_steps=2 ,   )

							# instantiate trainer
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

