'''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     a__      (   tf.Module ):






       def __init__( self		:		str  ,				a		:		Union[str, Any]    ):





           """simple docstring"""



           super().__init__()
           __lowerCamelCase		=  tokenizer
           __lowerCamelCase		=  AutoConfig.from_pretrained(a    )
           __lowerCamelCase		=  TFGPTaLMHeadModel.from_config(a    )






       @tf.function(input_signature=(tf.TensorSpec((None,)  ,				tf.string  ,				name='''text'''    ),)    )
       def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Tuple    ):





           """simple docstring"""



           __lowerCamelCase		=  self.tokenizer(a    )
           __lowerCamelCase		=  tokenized['''input_ids'''].to_tensor()

           __lowerCamelCase		=  tf.cast(input_ids_dense > 0  ,				tf.intaa    )
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           __lowerCamelCase		=  self.model(input_ids=a  ,				attention_mask=a    )['''logits''']

           return outputs




@require_tf
@require_keras_nlp
class     a__      (   unittest.TestCase ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         super().setUp()

         __lowerCamelCase		=  [GPTaTokenizer.from_pretrained(a    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         __lowerCamelCase		=  [TFGPTaTokenizer.from_pretrained(a    ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

         __lowerCamelCase		=  [
             '''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		=  list(zip(self.test_sentences  ,				self.test_sentences[::-1]    )    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         for tokenizer, tf_tokenizer in zip(self.tokenizers  ,				self.tf_tokenizers    ):
             for test_inputs in self.test_sentences:
                 __lowerCamelCase		=  tokenizer([test_inputs]  ,				return_tensors='''tf'''    )
                 __lowerCamelCase		=  tf_tokenizer([test_inputs]    )

                 for key in python_outputs.keys():
                     # convert them to numpy to avoid messing with ragged tensors
                     __lowerCamelCase		=  python_outputs[key].numpy()
                     __lowerCamelCase		=  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    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             __lowerCamelCase		=  tf.function(a    )
             for test_inputs in self.test_sentences:
                 __lowerCamelCase		=  tf.constant(a    )
                 __lowerCamelCase		=  compiled_tokenizer(a    )
                 __lowerCamelCase		=  tf_tokenizer(a    )

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






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             __lowerCamelCase		=  ModelToSave(tokenizer=a    )
             __lowerCamelCase		=  tf.convert_to_tensor([self.test_sentences[0]]    )
             __lowerCamelCase		=  model.serving(a    )  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                 __lowerCamelCase		=  Path(a    ) / '''saved.model'''
                 tf.saved_model.save(a  ,				a  ,				signatures={'''serving_default''': model.serving}    )
                 __lowerCamelCase		=  tf.saved_model.load(a    )
             __lowerCamelCase		=  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    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



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

             __lowerCamelCase		=  tf_tokenizer.get_config()
             __lowerCamelCase		=  TFGPTaTokenizer.from_config(a    )
             __lowerCamelCase		=  model_from_config(a    )

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






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             __lowerCamelCase		=  12_31_23

             for max_length in [3, 5, 10_24]:
                 __lowerCamelCase		=  tf.convert_to_tensor([self.test_sentences[0]]    )
                 __lowerCamelCase		=  tf_tokenizer(a  ,				max_length=a    )

                 __lowerCamelCase		=  out['''input_ids'''].numpy().shape[1]

                 assert out_length == max_length







'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       tempfile.mkdtemp()

          __A						: Optional[int]      =       BlipImageProcessor()
          __A						: List[str]      =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

          __A						: Any      =       BlipProcessor(_UpperCAmelCase						,	_UpperCAmelCase)

          processor.save_pretrained(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).tokenizer


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).image_processor


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       [np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta)]

          __A						: Union[str, Any]      =       [Image.fromarray(np.moveaxis(_UpperCAmelCase						,	0						,	-1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       BlipProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: int      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: List[Any]      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       BlipProcessor.from_pretrained(
              self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.get_image_processor()
          __A						: str      =       self.get_tokenizer()

          __A						: int      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: List[str]      =       self.prepare_image_inputs()

          __A						: str      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       processor(images=_UpperCAmelCase						,	return_tensors='np')

          for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: Tuple      =       self.get_tokenizer()

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'

          __A						: Dict      =       processor(text=_UpperCAmelCase)

          __A						: Tuple      =       tokenizer(_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]						,	encoded_processor[key])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'
          __A						: int      =       self.prepare_image_inputs()

          __A						: List[Any]      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	['pixel_values', 'input_ids', 'attention_mask'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Tuple      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: List[str]      =       processor.batch_decode(_UpperCAmelCase)
          __A						: List[str]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: Union[str, Any]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'
          __A						: str      =       self.prepare_image_inputs()

          __A						: Optional[int]      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
          self.assertListEqual(list(inputs.keys())						,	['pixel_values', 'input_ids', 'attention_mask'])