
"""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 __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= tempfile.mkdtemp()

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

      __UpperCamelCase       						= BlipProcessor(__UpperCAmelCase			,  __UpperCAmelCase   )

      processor.save_pretrained(self.tmpdirname   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

      return image_inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __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=__UpperCAmelCase			,  padding_value=1.0   )

      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= image_processor(__UpperCAmelCase			,  return_tensors='np'   )
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'

      __UpperCamelCase       						= processor(text=__UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer(__UpperCAmelCase			,  return_token_type_ids=__UpperCAmelCase   )

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

      __UpperCamelCase       						= processor.batch_decode(__UpperCAmelCase   )
      __UpperCamelCase       						= tokenizer.batch_decode(__UpperCAmelCase   )

      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

      __UpperCamelCase       						= 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']   )





"""simple docstring"""

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



