



"""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 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  int											=    tempfile.mkdtemp()

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

         _UpperCAmelCase							:  Union[str, Any]											=    BlipProcessor(A       ,	A		)

         processor.save_pretrained(self.tmpdirname		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					Any:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,	**A		).tokenizer





     def  __lowerCAmelCase	(					self       ,	**A		)							->					List[str]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,	**A		).image_processor





     def  __lowerCAmelCase	(					self		)							->					Dict:
         shutil.rmtree(self.tmpdirname		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  str											=    [np.random.randint(2_5_5       ,	size=(3, 3_0, 4_0_0)       ,	dtype=np.uinta		)]

         _UpperCAmelCase							:  int											=    [Image.fromarray(np.moveaxis(A       ,	0       ,	-1		)		) for x in image_inputs]

         return image_inputs





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  int											=    BlipProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()		)
         processor.save_pretrained(self.tmpdirname		)

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

         _UpperCAmelCase							:  Optional[int]											=    BlipProcessor.from_pretrained(
             self.tmpdirname       ,	bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''       ,	do_normalize=A       ,	padding_value=1.0		)

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

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





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  int											=    self.get_image_processor()
         _UpperCAmelCase							:  Tuple											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[str]											=    BlipProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  int											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  int											=    image_processor(A       ,	return_tensors='''np'''		)
         _UpperCAmelCase							:  List[Any]											=    processor(images=A       ,	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  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  Optional[Any]											=    self.get_image_processor()
         _UpperCAmelCase							:  int											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[Any]											=    BlipProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  Dict											=    '''lower newer'''

         _UpperCAmelCase							:  Union[str, Any]											=    processor(text=A		)

         _UpperCAmelCase							:  List[Any]											=    tokenizer(A       ,	return_token_type_ids=A		)

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





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Any											=    self.get_image_processor()
         _UpperCAmelCase							:  List[str]											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[str]											=    BlipProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  int											=    '''lower newer'''
         _UpperCAmelCase							:  List[str]											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  Union[str, Any]											=    processor(text=A       ,	images=A		)

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

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





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  int											=    self.get_image_processor()
         _UpperCAmelCase							:  str											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[Any]											=    BlipProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  Any											=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _UpperCAmelCase							:  List[Any]											=    processor.batch_decode(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    tokenizer.batch_decode(A		)

         self.assertListEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Union[str, Any]											=    self.get_image_processor()
         _UpperCAmelCase							:  int											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[str]											=    BlipProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  Optional[Any]											=    '''lower newer'''
         _UpperCAmelCase							:  str											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  Union[str, Any]											=    processor(text=A       ,	images=A		)

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







"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


