






'''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__ =			['gpt2']
UpperCamelCase__ =			'gpt2'

if is_tf_available():


 class        _UpperCAmelCase						(		tf.Module			):




     def __init__(			self  :       str  ,     a  :       str			):






        '''simple docstring'''






        super().__init__()
        lowercase_	:    List[str]           =     tokenizer
        lowercase_	:    Optional[Any]           =     AutoConfig.from_pretrained(a			)
        lowercase_	:    str           =     TFGPTaLMHeadModel.from_config(a			)





     @tf.function(input_signature=(tf.TensorSpec((None,)  ,     tf.string  ,     name="text"			),)			)
     def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Optional[int]			):






        '''simple docstring'''






        lowercase_	:    str           =     self.tokenizer(a			)
        lowercase_	:    str           =     tokenized["input_ids"].to_tensor()

        lowercase_	:    Union[str, Any]           =     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 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       super().setUp()

       lowercase_	:    Dict           =     [GPTaTokenizer.from_pretrained(a			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       lowercase_	:    Optional[int]           =     [TFGPTaTokenizer.from_pretrained(a			) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers			) == len(self.tf_tokenizers			)

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




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       for tokenizer, tf_tokenizer in zip(self.tokenizers  ,     self.tf_tokenizers			):
          for test_inputs in self.test_sentences:
             lowercase_	:    Union[str, Any]           =     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_	:    List[Any]           =     python_outputs[key].numpy()
                lowercase_	:    int           =     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 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       for tf_tokenizer in self.tf_tokenizers:
          lowercase_	:    Union[str, Any]           =     tf.function(a			)
          for test_inputs in self.test_sentences:
             lowercase_	:    Optional[int]           =     tf.constant(a			)
             lowercase_	:    Union[str, Any]           =     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 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       for tf_tokenizer in self.tf_tokenizers:
          lowercase_	:    List[Any]           =     ModelToSave(tokenizer=a			)
          lowercase_	:    Any           =     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_	:    str           =     Path(a			) / "saved.model"
             tf.saved_model.save(a  ,     a  ,     signatures={"serving_default": model.serving}			)
             lowercase_	:    str           =     tf.saved_model.load(a			)
          lowercase_	:    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 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       for tf_tokenizer in self.tf_tokenizers:
          lowercase_	:    Tuple           =     tf.convert_to_tensor([self.test_sentences[0]]			)
          lowercase_	:    List[str]           =     tf_tokenizer(a			)  # Build model with some sample inputs

          lowercase_	:    Optional[Any]           =     tf_tokenizer.get_config()
          lowercase_	:    int           =     TFGPTaTokenizer.from_config(a			)
          lowercase_	:    str           =     model_from_config(a			)

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





    @slow
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       for tf_tokenizer in self.tf_tokenizers:
          # for the test to run
          lowercase_	:    Any           =     1_2_3_1_2_3

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

             lowercase_	:    int           =     out["input_ids"].numpy().shape[1]

             assert out_length == max_length












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




