


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  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def   lowercase_       ( self							)    ->			Optional[Any]:
										lowerCAmelCase_       :					List[Any]			 = tempfile.mkdtemp()

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

										lowerCAmelCase_       :					Optional[Any]			 = BlipProcessor(lowerCAmelCase__      ,  lowerCAmelCase__							)

										processor.save_pretrained(self.tmpdirname							)




						def   lowercase_       ( self      ,  **__lowercase							)    ->			List[Any]:
										return AutoProcessor.from_pretrained(self.tmpdirname      ,  **lowerCAmelCase__							).tokenizer




						def   lowercase_       ( self      ,  **__lowercase							)    ->			List[Any]:
										return AutoProcessor.from_pretrained(self.tmpdirname      ,  **lowerCAmelCase__							).image_processor




						def   lowercase_       ( self							)    ->			Any:
										shutil.rmtree(self.tmpdirname							)




						def   lowercase_       ( self							)    ->			Optional[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = [np.random.randint(2_5_5      ,  size=(3, 3_0, 4_0_0)      ,  dtype=np.uinta							)]

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

										return image_inputs




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					List[str]			 = BlipProcessor(tokenizer=self.get_tokenizer()      ,  image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										lowerCAmelCase_       :					List[str]			 = self.get_tokenizer(bos_token='''(BOS)'''      ,  eos_token='''(EOS)'''							)
										lowerCAmelCase_       :					Union[str, Any]			 = self.get_image_processor(do_normalize=lowerCAmelCase__      ,  padding_value=1.0							)

										lowerCAmelCase_       :					Optional[Any]			 = BlipProcessor.from_pretrained(
										    self.tmpdirname      ,  bos_token='''(BOS)'''      ,  eos_token='''(EOS)'''      ,  do_normalize=lowerCAmelCase__      ,  padding_value=1.0							)

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

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




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					str			 = self.get_image_processor()
										lowerCAmelCase_       :					int			 = self.get_tokenizer()

										lowerCAmelCase_       :					Optional[Any]			 = BlipProcessor(tokenizer=lowerCAmelCase__      ,  image_processor=lowerCAmelCase__							)

										lowerCAmelCase_       :					Dict			 = self.prepare_image_inputs()

										lowerCAmelCase_       :					List[str]			 = image_processor(lowerCAmelCase__      ,  return_tensors='''np'''							)
										lowerCAmelCase_       :					Union[str, Any]			 = processor(images=lowerCAmelCase__      ,  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   lowercase_       ( self							)    ->			Optional[Any]:
										lowerCAmelCase_       :					int			 = self.get_image_processor()
										lowerCAmelCase_       :					int			 = self.get_tokenizer()

										lowerCAmelCase_       :					Dict			 = BlipProcessor(tokenizer=lowerCAmelCase__      ,  image_processor=lowerCAmelCase__							)

										lowerCAmelCase_       :					Dict			 = "lower newer"

										lowerCAmelCase_       :					Any			 = processor(text=lowerCAmelCase__							)

										lowerCAmelCase_       :					Optional[int]			 = tokenizer(lowerCAmelCase__      ,  return_token_type_ids=lowerCAmelCase__							)

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




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					List[Any]			 = self.get_image_processor()
										lowerCAmelCase_       :					Any			 = self.get_tokenizer()

										lowerCAmelCase_       :					Dict			 = BlipProcessor(tokenizer=lowerCAmelCase__      ,  image_processor=lowerCAmelCase__							)

										lowerCAmelCase_       :					List[str]			 = "lower newer"
										lowerCAmelCase_       :					List[str]			 = self.prepare_image_inputs()

										lowerCAmelCase_       :					Union[str, Any]			 = processor(text=lowerCAmelCase__      ,  images=lowerCAmelCase__							)

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

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




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					Optional[int]			 = self.get_image_processor()
										lowerCAmelCase_       :					Tuple			 = self.get_tokenizer()

										lowerCAmelCase_       :					Optional[Any]			 = BlipProcessor(tokenizer=lowerCAmelCase__      ,  image_processor=lowerCAmelCase__							)

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

										lowerCAmelCase_       :					List[str]			 = processor.batch_decode(lowerCAmelCase__							)
										lowerCAmelCase_       :					Dict			 = tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__      ,  lowerCAmelCase__							)




						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					int			 = self.get_image_processor()
										lowerCAmelCase_       :					str			 = self.get_tokenizer()

										lowerCAmelCase_       :					Union[str, Any]			 = BlipProcessor(tokenizer=lowerCAmelCase__      ,  image_processor=lowerCAmelCase__							)

										lowerCAmelCase_       :					Optional[Any]			 = "lower newer"
										lowerCAmelCase_       :					int			 = self.prepare_image_inputs()

										lowerCAmelCase_       :					Optional[int]			 = processor(text=lowerCAmelCase__      ,  images=lowerCAmelCase__							)

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






from typing import TYPE_CHECKING

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


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






