
"""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				):




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       tempfile.mkdtemp()

          UpperCAmelCase          =       BlipImageProcessor()
          UpperCAmelCase          =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'				)

          UpperCAmelCase          =       BlipProcessor(lowercase_ ,							lowercase_				)

          processor.save_pretrained(self.tmpdirname				)




      def      a_   (				self ,							**lowercase_				)    ->       List[str]:
          return AutoProcessor.from_pretrained(self.tmpdirname ,							**lowercase_				).tokenizer




      def      a_   (				self ,							**lowercase_				)    ->       Any:
          return AutoProcessor.from_pretrained(self.tmpdirname ,							**lowercase_				).image_processor




      def      a_   (				self				)    ->       Union[str, Any]:
          shutil.rmtree(self.tmpdirname				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       [np.random.randint(2_5_5 ,							size=(3, 3_0, 4_0_0) ,							dtype=np.uinta				)]

          UpperCAmelCase          =       [Image.fromarray(np.moveaxis(lowercase_ ,							0 ,							-1				)				) for x in image_inputs]

          return image_inputs




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       BlipProcessor(tokenizer=self.get_tokenizer() ,							image_processor=self.get_image_processor()				)
          processor.save_pretrained(self.tmpdirname				)

          UpperCAmelCase          =       self.get_tokenizer(bos_token='(BOS)' ,							eos_token='(EOS)'				)
          UpperCAmelCase          =       self.get_image_processor(do_normalize=lowercase_ ,							padding_value=1.0				)

          UpperCAmelCase          =       BlipProcessor.from_pretrained(
              self.tmpdirname ,							bos_token='(BOS)' ,							eos_token='(EOS)' ,							do_normalize=lowercase_ ,							padding_value=1.0				)

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

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




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       BlipProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       image_processor(lowercase_ ,							return_tensors='np'				)
          UpperCAmelCase          =       processor(images=lowercase_ ,							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      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       BlipProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       'lower newer'

          UpperCAmelCase          =       processor(text=lowercase_				)

          UpperCAmelCase          =       tokenizer(lowercase_ ,							return_token_type_ids=lowercase_				)

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




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       BlipProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       'lower newer'
          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       processor(text=lowercase_ ,							images=lowercase_				)

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

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




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       BlipProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCAmelCase          =       processor.batch_decode(lowercase_				)
          UpperCAmelCase          =       tokenizer.batch_decode(lowercase_				)

          self.assertListEqual(lowercase_ ,							lowercase_				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.get_image_processor()
          UpperCAmelCase          =       self.get_tokenizer()

          UpperCAmelCase          =       BlipProcessor(tokenizer=lowercase_ ,							image_processor=lowercase_				)

          UpperCAmelCase          =       'lower newer'
          UpperCAmelCase          =       self.prepare_image_inputs()

          UpperCAmelCase          =       processor(text=lowercase_ ,							images=lowercase_				)

          # 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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

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



