
"""simple docstring"""






import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import BlipTextModel
      from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST





class 							snake_case       :







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=12       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=99       ,							__UpperCAmelCase=32       ,							__UpperCAmelCase=32       ,							__UpperCAmelCase=5       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=37       ,							__UpperCAmelCase=0.1       ,							__UpperCAmelCase=0.1       ,							__UpperCAmelCase=5_12       ,							__UpperCAmelCase=0.02       ,							__UpperCAmelCase=0       ,							__UpperCAmelCase=None       ,							)   ->Tuple:
      a_						   =     parent
      a_						   =     batch_size
      a_						   =     seq_length
      a_						   =     is_training
      a_						   =     use_input_mask
      a_						   =     use_labels
      a_						   =     vocab_size
      a_						   =     hidden_size
      a_						   =     projection_dim
      a_						   =     num_hidden_layers
      a_						   =     num_attention_heads
      a_						   =     intermediate_size
      a_						   =     dropout
      a_						   =     attention_dropout
      a_						   =     max_position_embeddings
      a_						   =     initializer_range
      a_						   =     scope
      a_						   =     bos_token_id







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size)

      a_						   =     None
      if self.use_input_mask:
         a_						   =     random_attention_mask([self.batch_size, self.seq_length])

      if input_mask is not None:
         a_   ,			a_						   =     input_mask.shape
         a_						   =     np.random.randint(1       ,							seq_length - 1       ,							size=(batch_size,))
         for batch_idx, start_index in enumerate(__UpperCAmelCase):
            a_						   =     1
            a_						   =     0

      a_						   =     self.get_config()

      return config, input_ids, input_mask







   def    UpperCAmelCase__	( self)   ->Any:
      return BlipTextConfig(
          vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							projection_dim=self.projection_dim       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							dropout=self.dropout       ,							attention_dropout=self.attention_dropout       ,							max_position_embeddings=self.max_position_embeddings       ,							initializer_range=self.initializer_range       ,							bos_token_id=self.bos_token_id       ,							)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     BlipTextModel(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      with torch.no_grad():
         a_						   =     model(__UpperCAmelCase       ,							attention_mask=__UpperCAmelCase)
         a_						   =     model(__UpperCAmelCase)
      self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertEqual(result.pooler_output.shape       ,							(self.batch_size, self.hidden_size))







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     self.prepare_config_and_inputs()
      a_   ,			a_   ,			a_						   =     config_and_inputs
      a_						   =     {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict






@require_torch
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Tuple     					=     (BlipTextModel,) if is_torch_available() else ()
   a_					:		List[Any]     					=     False
   a_					:		Tuple     					=     False
   a_					:		int     					=     False







   def    UpperCAmelCase__	( self)   ->Optional[int]:
      a_						   =     BlipTextModelTester(self)
      a_						   =     ConfigTester(self       ,							config_class=__UpperCAmelCase       ,							hidden_size=37)







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      self.config_tester.run_common_tests()







   def    UpperCAmelCase__	( self)   ->List[Any]:
      a_						   =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->List[Any]:
      pass







   def    UpperCAmelCase__	( self)   ->str:
      pass







   @unittest.skip(reason="Blip does not use inputs_embeds")
   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
   def    UpperCAmelCase__	( self)   ->str:
      pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
   def    UpperCAmelCase__	( self)   ->str:
      pass







   @slow
   def    UpperCAmelCase__	( self)   ->List[Any]:
      for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_						   =     BlipTextModel.from_pretrained(__UpperCAmelCase)
         self.assertIsNotNone(__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase)

"""simple docstring"""






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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=10 ,   UpperCAmelCase=10 ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Union[str, Any]:

   """simple docstring"""



   a_						   =     []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(255 ,   size=(image_processor_tester.num_channels, width, height) ,   dtype=np.uinta     )     )

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      a_						   =     [Image.fromarray(np.moveaxis(UpperCAmelCase ,   0 ,   -1     )     ) for frame in video]

   if torchify:
      a_						   =     [torch.from_numpy(UpperCAmelCase     ) for frame in video]

   return video
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=False ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Optional[Any]:

   """simple docstring"""



   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   a_						   =     []
   for i in range(image_processor_tester.batch_size     ):
      if equal_resolution:
         a_						   =     a_						   =     image_processor_tester.max_resolution
      else:
         a_   ,			a_						   =     np.random.choice(
             np.arange(image_processor_tester.min_resolution ,   image_processor_tester.max_resolution     ) ,   2     )
         a_						   =     prepare_video(
             image_processor_tester=UpperCAmelCase ,   width=UpperCAmelCase ,   height=UpperCAmelCase ,   numpify=UpperCAmelCase ,   torchify=UpperCAmelCase ,   )
      video_inputs.append(UpperCAmelCase     )

   return video_inputs





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							)   ->str:
      a_						   =     size if size is not None else {"shortest_edge": 18}
      a_						   =     crop_size if crop_size is not None else {"height": 18, "width": 18}

      a_						   =     parent
      a_						   =     batch_size
      a_						   =     num_channels
      a_						   =     num_frames
      a_						   =     image_size
      a_						   =     min_resolution
      a_						   =     max_resolution
      a_						   =     do_resize
      a_						   =     size
      a_						   =     do_normalize
      a_						   =     image_mean
      a_						   =     image_std
      a_						   =     do_center_crop
      a_						   =     crop_size







   def    UpperCAmelCase__	( self)   ->Tuple:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		int     					=     TvltImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     TvltImageProcessorTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->int:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_center_crop"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))







   def    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							Image.Image)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->int:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							np.ndarray)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							torch.Tensor)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)