code: import random
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
       import torch
  
  if is_vision_available():
       import PIL
  
       from transformers import FlavaImageProcessor
       from transformers.image_utils import PILImageResampling
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )
  else:
       _SCREAMING_SNAKE_CASE   						=    _SCREAMING_SNAKE_CASE   						=    _SCREAMING_SNAKE_CASE   						=    _SCREAMING_SNAKE_CASE   						=    None
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def __init__(					self  :   Union[str, Any]      ,      lowerCAmelCase  :   List[Any]      ,      lowerCAmelCase  :   List[Any]=7      ,      lowerCAmelCase  :   List[str]=3      ,      lowerCAmelCase  :   int=30      ,      lowerCAmelCase  :   Optional[int]=400      ,      lowerCAmelCase  :   Optional[Any]=True      ,      lowerCAmelCase  :   Optional[int]=None      ,      lowerCAmelCase  :   Union[str, Any]=True      ,      lowerCAmelCase  :   Union[str, Any]=None      ,      lowerCAmelCase  :   str=None      ,      lowerCAmelCase  :   Optional[Any]=True      ,      lowerCAmelCase  :   Union[str, Any]=1 / 255      ,      lowerCAmelCase  :   Dict=True      ,      lowerCAmelCase  :   Dict=FLAVA_IMAGE_MEAN      ,      lowerCAmelCase  :   Optional[int]=FLAVA_IMAGE_STD      ,      lowerCAmelCase  :   Tuple=14      ,      lowerCAmelCase  :   int=75      ,      lowerCAmelCase  :   str=None      ,      lowerCAmelCase  :   str=16      ,      lowerCAmelCase  :   str=0.3      ,      lowerCAmelCase  :   List[str]=None      ,      lowerCAmelCase  :   Tuple=True      ,      lowerCAmelCase  :   Optional[Any]=None      ,      lowerCAmelCase  :   Union[str, Any]=None      ,      lowerCAmelCase  :   Optional[Any]=True      ,      lowerCAmelCase  :   List[str]=None      ,      lowerCAmelCase  :   Any=True      ,      lowerCAmelCase  :   Any=True      ,      lowerCAmelCase  :   Optional[Any]=FLAVA_CODEBOOK_MEAN      ,      lowerCAmelCase  :   Any=FLAVA_CODEBOOK_STD      ,      )	-> Optional[Any]:
        a__     		=     size if size is not None else {"height": 224, "width": 224}
        a__     		=     crop_size if crop_size is not None else {"height": 224, "width": 224}
        a__     		=     codebook_size if codebook_size is not None else {"height": 112, "width": 112}
        a__     		=     codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
  
        a__     		=     parent
        a__     		=     batch_size
        a__     		=     num_channels
        a__     		=     do_resize
        a__     		=     do_rescale
        a__     		=     rescale_factor
        a__     		=     min_resolution
        a__     		=     max_resolution
        a__     		=     size
        a__     		=     resample if resample is not None else PILImageResampling.BICUBIC
        a__     		=     do_normalize
        a__     		=     image_mean
        a__     		=     image_std
        a__     		=     do_center_crop
        a__     		=     crop_size
  
        a__     		=     input_size_patches
        a__     		=     total_mask_patches
        a__     		=     mask_group_max_patches
        a__     		=     mask_group_min_patches
        a__     		=     mask_group_min_aspect_ratio
        a__     		=     mask_group_max_aspect_ratio
  
        a__     		=     codebook_do_resize
        a__     		=     codebook_size
        a__     		=     codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
        a__     		=     codebook_do_center_crop
        a__     		=     codebook_crop_size
        a__     		=     codebook_do_map_pixels
        a__     		=     codebook_do_normalize
        a__     		=     codebook_image_mean
        a__     		=     codebook_image_std
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> int:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "resample": self.resample,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "input_size_patches": self.input_size_patches,
            "total_mask_patches": self.total_mask_patches,
            "mask_group_max_patches": self.mask_group_max_patches,
            "mask_group_min_patches": self.mask_group_min_patches,
            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
            "codebook_do_resize": self.codebook_do_resize,
            "codebook_size": self.codebook_size,
            "codebook_resample": self.codebook_resample,
            "codebook_do_center_crop": self.codebook_do_center_crop,
            "codebook_crop_size": self.codebook_crop_size,
            "codebook_do_map_pixels": self.codebook_do_map_pixels,
            "codebook_do_normalize": self.codebook_do_normalize,
            "codebook_image_mean": self.codebook_image_mean,
            "codebook_image_std": self.codebook_image_std,
        }
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Any:
        return (self.size["height"], self.size["width"])
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> List[str]:
        return (
            (self.input_size_patches, self.input_size_patches)
            if not isinstance(self.input_size_patches      ,      lowerCAmelCase   )
            else self.input_size_patches
        )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> List[Any]:
        return (self.codebook_size["height"], self.codebook_size["width"])
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					UpperCAmelCase_				(							_a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Any						 =							FlavaImageProcessor if is_vision_available() else None
      UpperCAmelCase_			:       str						 =							None
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> int:
        a__     		=     FlavaImageProcessingTester(self   )
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   int   )	-> Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Dict:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "image_mean"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "image_std"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_normalize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_resize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "resample"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "crop_size"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_center_crop"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_rescale"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "rescale_factor"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "masking_generator"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_do_resize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_size"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_resample"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_do_center_crop"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_crop_size"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_do_map_pixels"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_do_normalize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_image_mean"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "codebook_image_std"   )   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Union[str, Any]:
        a__     		=     self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size      ,      {"height": 224, "width": 224}   )
        self.assertEqual(image_processor.crop_size      ,      {"height": 224, "width": 224}   )
        self.assertEqual(image_processor.codebook_size      ,      {"height": 112, "width": 112}   )
        self.assertEqual(image_processor.codebook_crop_size      ,      {"height": 112, "width": 112}   )
  
        a__     		=     self.image_processing_class.from_dict(
            self.image_processor_dict      ,      size=42      ,      crop_size=84      ,      codebook_size=33      ,      codebook_crop_size=66   )
        self.assertEqual(image_processor.size      ,      {"height": 42, "width": 42}   )
        self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}   )
        self.assertEqual(image_processor.codebook_size      ,      {"height": 33, "width": 33}   )
        self.assertEqual(image_processor.codebook_crop_size      ,      {"height": 66, "width": 66}   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> List[str]:
        pass
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> int:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase   )
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase      ,      PIL.Image.Image   )
  
        # Test not batched input
        a__     		=     image_processing(image_inputs[0]      ,      return_tensors="pt"   )
  
        # Test no bool masked pos
        self.assertFalse("bool_masked_pos" in encoded_images   )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
  
        self.assertEqual(
            encoded_images.pixel_values.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_tensors="pt"   )
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
  
        # Test no bool masked pos
        self.assertFalse("bool_masked_pos" in encoded_images   )
  
        self.assertEqual(
            encoded_images.pixel_values.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict      ,      lowerCAmelCase  :   Tuple      ,      lowerCAmelCase  :   int   )	-> List[Any]:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random tensors
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase      ,      **lowerCAmelCase   )
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase      ,      lowerCAmelCase   )
  
        # Test not batched input
        a__     		=     image_processing(image_inputs[0]      ,      return_tensors="pt"   )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        a__     		=     image_processing(lowerCAmelCase      ,      return_image_mask=lowerCAmelCase      ,      return_tensors="pt"   )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape      ,      (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
        # Test masking
        a__     		=     image_processing(lowerCAmelCase      ,      return_image_mask=lowerCAmelCase      ,      return_tensors="pt"   )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
        a__		,a__     		=     self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape      ,      (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            )      ,      )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> Tuple:
        self._test_call_framework(np.ndarray      ,      prepare_kwargs={"numpify": True}   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> Optional[Any]:
        self._test_call_framework(torch.Tensor      ,      prepare_kwargs={"torchify": True}   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> Tuple:
        # Initialize image_processing
        random.seed(1234   )
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase      ,      torchify=lowerCAmelCase   )
  
        # Test not batched input
        a__     		=     image_processing(image_inputs[0]      ,      return_image_mask=lowerCAmelCase      ,      return_tensors="pt"   )
        self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,      75   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> str:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase   )
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase      ,      PIL.Image.Image   )
  
        # Test not batched input
        a__     		=     image_processing(image_inputs[0]      ,      return_codebook_pixels=lowerCAmelCase      ,      return_tensors="pt"   )
        a__		,a__     		=     self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_codebook_pixels=lowerCAmelCase      ,      return_tensors="pt"   )
        a__		,a__     		=     self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  