

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 SCREAMING_SNAKE_CASE     (  tf.Module			):
     """simple docstring"""






     def __init__(      self			:   Dict	,							lowerCAmelCase			:   Optional[int]   )							->     str:






        """simple docstring"""







        super().__init__()
        __lowerCAmelCase  :      str    =						tokenizer
        __lowerCAmelCase  :      Union[str, Any]    =						AutoConfig.from_pretrained(lowerCAmelCase   )
        __lowerCAmelCase  :      Union[str, Any]    =						TFGPTaLMHeadModel.from_config(lowerCAmelCase   )







     @tf.function(input_signature=(tf.TensorSpec((None,)	,							tf.string	,							name="""text"""   ),)   )
     def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Tuple   )							->     str:






        """simple docstring"""







        __lowerCAmelCase  :      Union[str, Any]    =						self.tokenizer(lowerCAmelCase   )
        __lowerCAmelCase  :      Union[str, Any]    =						tokenized["""input_ids"""].to_tensor()

        __lowerCAmelCase  :      List[str]    =						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=lowerCAmelCase	,							attention_mask=lowerCAmelCase   )["""logits"""]

        return outputs



@require_tf
@require_keras_nlp
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     super().setUp()

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

     __lowerCAmelCase  :      Optional[Any]    =						[
         """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  :      str    =						list(zip(self.test_sentences	,							self.test_sentences[::-1]   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     for tokenizer, tf_tokenizer in zip(self.tokenizers	,							self.tf_tokenizers   ):
        for test_inputs in self.test_sentences:
           __lowerCAmelCase  :      Union[str, Any]    =						tokenizer([test_inputs]	,							return_tensors="""tf"""   )
           __lowerCAmelCase  :      Tuple    =						tf_tokenizer([test_inputs]   )

           for key in python_outputs.keys():
              # convert them to numpy to avoid messing with ragged tensors
              __lowerCAmelCase  :      Any    =						python_outputs[key].numpy()
              __lowerCAmelCase  :      Dict    =						tf_outputs[key].numpy()

              self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape   )   )
              self.assertTrue(tf.reduce_all(tf.cast(lowerCAmelCase	,							tf.intaa   ) == tf_outputs_values   )   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Any:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        __lowerCAmelCase  :      int    =						tf.function(lowerCAmelCase   )
        for test_inputs in self.test_sentences:
           __lowerCAmelCase  :      Optional[int]    =						tf.constant(lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[int]    =						compiled_tokenizer(lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[Any]    =						tf_tokenizer(lowerCAmelCase   )

           for key in eager_outputs.keys():
              self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]   )   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[str]:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        __lowerCAmelCase  :      Any    =						ModelToSave(tokenizer=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						tf.convert_to_tensor([self.test_sentences[0]]   )
        __lowerCAmelCase  :      Optional[int]    =						model.serving(lowerCAmelCase   )  # Build model with some sample inputs
        with TemporaryDirectory() as tempdir:
           __lowerCAmelCase  :      int    =						Path(lowerCAmelCase   ) / """saved.model"""
           tf.saved_model.save(lowerCAmelCase	,							lowerCAmelCase	,							signatures={"""serving_default""": model.serving}   )
           __lowerCAmelCase  :      Union[str, Any]    =						tf.saved_model.load(lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[Any]    =						loaded_model.signatures["""serving_default"""](lowerCAmelCase   )["""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 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[str]:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        __lowerCAmelCase  :      Dict    =						tf.convert_to_tensor([self.test_sentences[0]]   )
        __lowerCAmelCase  :      Optional[int]    =						tf_tokenizer(lowerCAmelCase   )  # Build model with some sample inputs

        __lowerCAmelCase  :      List[Any]    =						tf_tokenizer.get_config()
        __lowerCAmelCase  :      Optional[int]    =						TFGPTaTokenizer.from_config(lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						model_from_config(lowerCAmelCase   )

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







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[str]:






     """simple docstring"""







     for tf_tokenizer in self.tf_tokenizers:
        # for the test to run
        __lowerCAmelCase  :      Optional[int]    =						12_31_23

        for max_length in [3, 5, 10_24]:
           __lowerCAmelCase  :      Dict    =						tf.convert_to_tensor([self.test_sentences[0]]   )
           __lowerCAmelCase  :      int    =						tf_tokenizer(lowerCAmelCase	,							max_length=lowerCAmelCase   )

           __lowerCAmelCase  :      Tuple    =						out["""input_ids"""].numpy().shape[1]

           assert out_length == max_length





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


