


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
  import tensorflow as tf

  from transformers import TFBlipTextModel
  from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class    UpperCAmelCase_	:
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=12 ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=99 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=37 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=0 ,					__SCREAMING_SNAKE_CASE=None ,					):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		parent
              UpperCamelCase    :	List[Any]   	=		batch_size
              UpperCamelCase    :	List[Any]   	=		seq_length
              UpperCamelCase    :	Any   	=		is_training
              UpperCamelCase    :	Union[str, Any]   	=		use_input_mask
              UpperCamelCase    :	int   	=		use_labels
              UpperCamelCase    :	Optional[Any]   	=		vocab_size
              UpperCamelCase    :	Any   	=		hidden_size
              UpperCamelCase    :	Optional[int]   	=		projection_dim
              UpperCamelCase    :	Union[str, Any]   	=		num_hidden_layers
              UpperCamelCase    :	List[Any]   	=		num_attention_heads
              UpperCamelCase    :	Any   	=		intermediate_size
              UpperCamelCase    :	Dict   	=		dropout
              UpperCamelCase    :	List[str]   	=		attention_dropout
              UpperCamelCase    :	Any   	=		max_position_embeddings
              UpperCamelCase    :	int   	=		initializer_range
              UpperCamelCase    :	Dict   	=		scope
              UpperCamelCase    :	Optional[int]   	=		bos_token_id






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size						)

              UpperCamelCase    :	Union[str, Any]   	=		None
              if self.use_input_mask:
                     UpperCamelCase    :	Optional[int]   	=		random_attention_mask([self.batch_size, self.seq_length]						)

              if input_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		input_mask.numpy()
                     UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		input_mask.shape
                     UpperCamelCase    :	Tuple   	=		np.random.randint(1 ,					seq_length - 1 ,					size=(batch_size,)						)
                     for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		1
                            UpperCamelCase    :	Any   	=		0

              UpperCamelCase    :	Optional[int]   	=		self.get_config()

              return config, input_ids, tf.convert_to_tensor(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		TFBlipTextModel(config=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		model(__SCREAMING_SNAKE_CASE ,					attention_mask=__SCREAMING_SNAKE_CASE ,					training=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	str   	=		model(__SCREAMING_SNAKE_CASE ,					training=__SCREAMING_SNAKE_CASE						)
              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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.prepare_config_and_inputs()
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		config_and_inputs
              UpperCamelCase    :	List[Any]   	=		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              return config, inputs_dict




@require_tf
class    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					(TFBlipTextModel,) if is_tf_available() else ()
       __UpperCamelCase      :     List[Any]							=					False
       __UpperCamelCase      :     str							=					False
       __UpperCamelCase      :     int							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		BlipTextModelTester(self						)
              UpperCamelCase    :	Union[str, Any]   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					hidden_size=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.config_tester.run_common_tests()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''Blip does not use inputs_embeds'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	Tuple   	=		TFBlipTextModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE=True						):




              """simple docstring"""




              super().test_pt_tf_model_equivalence(allow_missing_keys=__SCREAMING_SNAKE_CASE						)





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

