





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       : str			     =       ["""gpt2"""]
_UpperCAmelCase       : Optional[int]			     =       """gpt2"""

if is_tf_available():

    class      lowerCAmelCase							(      tf.Module    ):
     def __init__(   self						:   Dict    ,  UpperCAmelCase						:   int			)    ->			Union[str, Any]:
         super().__init__()
         lowerCamelCase__					:   Optional[int]      				=	tokenizer
         lowerCamelCase__					:   Tuple      				=	AutoConfig.from_pretrained(UpperCAmelCase			)
         lowerCamelCase__					:   List[str]      				=	TFGPTaLMHeadModel.from_config(UpperCAmelCase			)







     @tf.function(input_signature=(tf.TensorSpec((None,)    ,  tf.string    ,  name='text'			),)			)
     def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   Optional[Any]			)    ->			str:
         lowerCamelCase__					:   str      				=	self.tokenizer(UpperCAmelCase			)
         lowerCamelCase__					:   int      				=	tokenized['input_ids'].to_tensor()

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

         lowerCamelCase__					:   List[Any]      				=	self.model(input_ids=UpperCAmelCase    ,  attention_mask=UpperCAmelCase			)['logits']

         return outputs

@require_tf
@require_keras_nlp
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Dict			)    ->			List[Any]:
     super().setUp()

     lowerCamelCase__					:   Tuple      				=	[GPTaTokenizer.from_pretrained(UpperCAmelCase			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
     lowerCamelCase__					:   str      				=	[TFGPTaTokenizer.from_pretrained(UpperCAmelCase			) for checkpoint in TOKENIZER_CHECKPOINTS]
     assert len(self.tokenizers			) == len(self.tf_tokenizers			)

     lowerCamelCase__					:   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',
     ]
     lowerCamelCase__					:   Tuple      				=	list(zip(self.test_sentences    ,  self.test_sentences[::-1]			)			)
 def   A_			(   self						:   Union[str, Any]			)    ->			int:
     for tokenizer, tf_tokenizer in zip(self.tokenizers    ,  self.tf_tokenizers			):
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   Tuple      				=	tokenizer([test_inputs]    ,  return_tensors='tf'			)
             lowerCamelCase__					:   Optional[int]      				=	tf_tokenizer([test_inputs]			)

             for key in python_outputs.keys():
                 # convert them to numpy to avoid messing with ragged tensors
                 lowerCamelCase__					:   Union[str, Any]      				=	python_outputs[key].numpy()
                 lowerCamelCase__					:   Union[str, 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   A_			(   self						:   Any			)    ->			Dict:
     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   List[Any]      				=	tf.function(UpperCAmelCase			)
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   Tuple      				=	tf.constant(UpperCAmelCase			)
             lowerCamelCase__					:   Optional[int]      				=	compiled_tokenizer(UpperCAmelCase			)
             lowerCamelCase__					:   List[Any]      				=	tf_tokenizer(UpperCAmelCase			)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]			)			)
 @slow
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   int      				=	ModelToSave(tokenizer=UpperCAmelCase			)
         lowerCamelCase__					:   Optional[int]      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
         lowerCamelCase__					:   Optional[Any]      				=	model.serving(UpperCAmelCase			)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             lowerCamelCase__					:   Any      				=	Path(UpperCAmelCase			) / 'saved.model'
             tf.saved_model.save(UpperCAmelCase    ,  UpperCAmelCase    ,  signatures={'serving_default': model.serving}			)
             lowerCamelCase__					:   str      				=	tf.saved_model.load(UpperCAmelCase			)
         lowerCamelCase__					:   str      				=	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   A_			(   self						:   int			)    ->			Dict:
     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   Optional[Any]      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
         lowerCamelCase__					:   Union[str, Any]      				=	tf_tokenizer(UpperCAmelCase			)  # Build model with some sample inputs

         lowerCamelCase__					:   Optional[Any]      				=	tf_tokenizer.get_config()
         lowerCamelCase__					:   Any      				=	TFGPTaTokenizer.from_config(UpperCAmelCase			)
         lowerCamelCase__					:   str      				=	model_from_config(UpperCAmelCase			)

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







 @slow
 def   A_			(   self						:   Dict			)    ->			Optional[int]:
     for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         lowerCamelCase__					:   Dict      				=	123123

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

             lowerCamelCase__					:   List[Any]      				=	out['input_ids'].numpy().shape[1]

             assert out_length == max_length












from ..utils import DummyObject, requires_backends

class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Tuple    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Optional[int]			)    ->			List[str]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[str]			)    ->			int:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Any    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   str    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Dict			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Dict			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[str]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   int			)    ->			Dict:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[int]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[str]			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Tuple    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)





