






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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @slow
     @require_torch
     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   str						    =     EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"       ,			"prajjwal1/bert-tiny"   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     BertTokenizer.from_pretrained("bert-base-uncased"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     bertabert.config.encoder.vocab_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tokenizer.sep_token_id
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tokenizer.cls_token_id
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     128

       SCREAMING_SNAKE_CASE							:   Dict						    =     datasets.load_dataset("cnn_dailymail"       ,			"3.0.0"       ,			split="train[:1%]"   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     datasets.load_dataset("cnn_dailymail"       ,			"3.0.0"       ,			split="validation[:1%]"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     train_dataset.select(range(32   )   )
       SCREAMING_SNAKE_CASE							:   int						    =     val_dataset.select(range(16   )   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     4

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

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     outputs.input_ids
         SCREAMING_SNAKE_CASE							:   List[str]						    =     outputs.input_ids.copy()
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
         ]
         SCREAMING_SNAKE_CASE							:   str						    =     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_							:						Optional[Any]   ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     pred.label_ids
         SCREAMING_SNAKE_CASE							:   Any						    =     pred.predictions

         # all unnecessary tokens are removed
         SCREAMING_SNAKE_CASE							:   str						    =     tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_   )

         SCREAMING_SNAKE_CASE							:   str						    =     sum([int(pred_str[i] == label_str[i]   ) for i in range(len(UpperCAmelCase_   )   )]   ) / len(UpperCAmelCase_   )

         return {"accuracy": accuracy}

       # map train dataset
       SCREAMING_SNAKE_CASE							:   Dict						    =     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
       SCREAMING_SNAKE_CASE							:   int						    =     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"]       ,			)

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_auto_remove_tmp_dir()

       SCREAMING_SNAKE_CASE							:   str						    =     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
       SCREAMING_SNAKE_CASE							:   List[str]						    =     SeqaSeqTrainer(
           model=UpperCAmelCase_       ,			args=UpperCAmelCase_       ,			compute_metrics=_compute_metrics       ,			train_dataset=UpperCAmelCase_       ,			eval_dataset=UpperCAmelCase_       ,			tokenizer=UpperCAmelCase_       ,			)

       # start training
       trainer.train()










"""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


__snake_case       =       ["""gpt2"""]
__snake_case       =       """gpt2"""

if is_tf_available():







  class _lowerCAmelCase							( tf.Module      ):







     def __init__(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

         '''simple docstring'''




         super().__init__()
         snake_case       : Dict    =       tokenizer
         snake_case       : List[str]    =       AutoConfig.from_pretrained(UpperCamelCase__		)
         snake_case       : List[str]    =       TFGPTaLMHeadModel.from_config(UpperCamelCase__		)







     @tf.function(input_signature=(tf.TensorSpec((None,)		,      tf.string		,      name="text"		),)		)
     def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[int]:

         '''simple docstring'''




         snake_case       : List[Any]    =       self.tokenizer(UpperCamelCase__		)
         snake_case       : Tuple    =       tokenized["input_ids"].to_tensor()

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

         snake_case       : int    =       self.model(input_ids=UpperCamelCase__		,      attention_mask=UpperCamelCase__		)["logits"]

         return outputs







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







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       super().setUp()

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

       snake_case       : Optional[int]    =       [
           "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       : str    =       list(zip(self.test_sentences		,      self.test_sentences[::-1]		)		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




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

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

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           snake_case       : str    =       tf.function(UpperCamelCase__		)
           for test_inputs in self.test_sentences:
               snake_case       : str    =       tf.constant(UpperCamelCase__		)
               snake_case       : Optional[int]    =       compiled_tokenizer(UpperCamelCase__		)
               snake_case       : Dict    =       tf_tokenizer(UpperCamelCase__		)

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







   @slow
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




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

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           snake_case       : Any    =       tf.convert_to_tensor([self.test_sentences[0]]		)
           snake_case       : List[str]    =       tf_tokenizer(UpperCamelCase__		)  # Build model with some sample inputs

           snake_case       : Dict    =       tf_tokenizer.get_config()
           snake_case       : Union[str, Any]    =       TFGPTaTokenizer.from_config(UpperCamelCase__		)
           snake_case       : Optional[Any]    =       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		)      ->						Tuple:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           snake_case       : List[str]    =       12_3123

           for max_length in [3, 5, 1024]:
               snake_case       : str    =       tf.convert_to_tensor([self.test_sentences[0]]		)
               snake_case       : List[Any]    =       tf_tokenizer(UpperCamelCase__		,      max_length=UpperCamelCase__		)

               snake_case       : str    =       out["input_ids"].numpy().shape[1]

               assert out_length == max_length





