"""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     :		Optional[Any]											=					["gpt2"]
_UpperCamelCase     :		List[Any]											=					"gpt2"

if is_tf_available():


       class   UpperCAmelCase_			(					tf.Module):


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


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

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

                    lowercase__    :	Optional[Any]     					=			self.model(input_ids=a	,    attention_mask=a							)['logits']

                    return outputs


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


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             super().setUp()

             lowercase__    :	List[Any]     					=			[GPTaTokenizer.from_pretrained(a							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
             lowercase__    :	int     					=			[TFGPTaTokenizer.from_pretrained(a							) for checkpoint in TOKENIZER_CHECKPOINTS]
             assert len(self.tokenizers							) == len(self.tf_tokenizers							)

             lowercase__    :	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',
             ]
             lowercase__    :	Union[str, Any]     					=			list(zip(self.test_sentences	,    self.test_sentences[::-1]							)							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             for tokenizer, tf_tokenizer in zip(self.tokenizers	,    self.tf_tokenizers							):
                    for test_inputs in self.test_sentences:
                           lowercase__    :	str     					=			tokenizer([test_inputs]	,    return_tensors='tf'							)
                           lowercase__    :	List[str]     					=			tf_tokenizer([test_inputs]							)

                           for key in python_outputs.keys():
                                  # convert them to numpy to avoid messing with ragged tensors
                                  lowercase__    :	Optional[int]     					=			python_outputs[key].numpy()
                                  lowercase__    :	List[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(a	,    tf.intaa							) == tf_outputs_values							)							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             for tf_tokenizer in self.tf_tokenizers:
                    lowercase__    :	Union[str, Any]     					=			tf.function(a							)
                    for test_inputs in self.test_sentences:
                           lowercase__    :	List[str]     					=			tf.constant(a							)
                           lowercase__    :	Tuple     					=			compiled_tokenizer(a							)
                           lowercase__    :	Union[str, Any]     					=			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:
                    lowercase__    :	str     					=			ModelToSave(tokenizer=a							)
                    lowercase__    :	Tuple     					=			tf.convert_to_tensor([self.test_sentences[0]]							)
                    lowercase__    :	Optional[int]     					=			model.serving(a							)  # Build model with some sample inputs
                    with TemporaryDirectory() as tempdir:
                           lowercase__    :	List[Any]     					=			Path(a							) / 'saved.model'
                           tf.saved_model.save(a	,    a	,    signatures={'serving_default': model.serving}							)
                           lowercase__    :	int     					=			tf.saved_model.load(a							)
                    lowercase__    :	Optional[Any]     					=			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							) ->      Dict:
             for tf_tokenizer in self.tf_tokenizers:
                    lowercase__    :	List[str]     					=			tf.convert_to_tensor([self.test_sentences[0]]							)
                    lowercase__    :	Optional[Any]     					=			tf_tokenizer(a							)  # Build model with some sample inputs

                    lowercase__    :	List[Any]     					=			tf_tokenizer.get_config()
                    lowercase__    :	Dict     					=			TFGPTaTokenizer.from_config(a							)
                    lowercase__    :	Optional[Any]     					=			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							) ->      Any:
             for tf_tokenizer in self.tf_tokenizers:
                    # for the test to run
                    lowercase__    :	Optional[Any]     					=			1_2_3_1_2_3

                    for max_length in [3, 5, 1_0_2_4]:
                           lowercase__    :	Tuple     					=			tf.convert_to_tensor([self.test_sentences[0]]							)
                           lowercase__    :	List[Any]     					=			tf_tokenizer(a	,    max_length=a							)

                           lowercase__    :	Optional[Any]     					=			out['input_ids'].numpy().shape[1]

                           assert out_length == max_length


"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

