"""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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Any	=   [
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   [
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



