"""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


_a      :		List[Any]           =   ['gpt2']
_a      :		List[str]           =   'gpt2'

if is_tf_available():
     class 						__A							( tf.Module		):






          def __init__(		self				,     a__  ):
            super().__init__()
            _lowerCAmelCase						:       int  			=     tokenizer
            _lowerCAmelCase						:       Tuple  			=     AutoConfig.from_pretrained(a__  )
            _lowerCAmelCase						:       Union[str, Any]  			=     TFGPTaLMHeadModel.from_config(a__  )






          @tf.function(input_signature=(tf.TensorSpec((None,)				,     tf.string				,     name="""text"""  ),)  )
          def     __A  (		self				,     a__  ):
            _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__  )
            _lowerCAmelCase						:       List[str]  			=     tokenized["""input_ids"""].to_tensor()

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

            _lowerCAmelCase						:       List[str]  			=     self.model(input_ids=a__				,     attention_mask=a__  )["""logits"""]

            return outputs
@require_tf
@require_keras_nlp
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       super().setUp()

       _lowerCAmelCase						:       Dict  			=     [GPTaTokenizer.from_pretrained(a__  ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       _lowerCAmelCase						:       str  			=     [TFGPTaTokenizer.from_pretrained(a__  ) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

       _lowerCAmelCase						:       str  			=     [
           """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						:       Dict  			=     list(zip(self.test_sentences				,     self.test_sentences[::-1]  )  )






     def     __A  (		self  ):
       for tokenizer, tf_tokenizer in zip(self.tokenizers				,     self.tf_tokenizers  ):
         for test_inputs in self.test_sentences:
           _lowerCAmelCase						:       Dict  			=     tokenizer([test_inputs]				,     return_tensors="""tf"""  )
           _lowerCAmelCase						:       Dict  			=     tf_tokenizer([test_inputs]  )

           for key in python_outputs.keys():
             # convert them to numpy to avoid messing with ragged tensors
             _lowerCAmelCase						:       List[str]  			=     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(a__				,     tf.intaa  ) == tf_outputs_values  )  )






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

         _lowerCAmelCase						:       Optional[int]  			=     tf_tokenizer.get_config()
         _lowerCAmelCase						:       List[str]  			=     TFGPTaTokenizer.from_config(a__  )
         _lowerCAmelCase						:       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     __A  (		self  ):
       for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         _lowerCAmelCase						:       List[str]  			=     123123

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

           _lowerCAmelCase						:       Union[str, Any]  			=     out["""input_ids"""].numpy().shape[1]

           assert out_length == max_length





"""simple docstring"""




_a      :		List[str]           =   {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}




