"""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


__lowercase		:		Any														=							["gpt2"]
__lowercase		:		int														=							"gpt2"

if is_tf_available():



       class      _A	(						tf.Module					):




           """simple docstring"""




           def __init__(		self							:     List[str]	,      A_							:     Any    )    ->       Tuple:
                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  lowercase					(		self							:     Dict	,      A_							:     List[Any]    )    ->       Any:
                __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      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, 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  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         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  lowercase					(		self							:     Dict    )    ->       int:
         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  lowercase					(		self							:     Tuple    )    ->       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  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         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  lowercase					(		self							:     Tuple    )    ->       int:
         for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              __snake_case				=							123_123

              for max_length in [3, 5, 1_024]:
                   __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
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}