




"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :List[Any]     				=     KandinskyVaaImgaImgPipeline
   _lowerCamelCase    :Any     				=     ["image_embeds", "negative_image_embeds", "image"]
   _lowerCamelCase    :Any     				=     [
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   _lowerCamelCase    :int     				=     [
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   _lowerCamelCase    :List[Any]     				=     False






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Dict:

         """simple docstring"""
         return 32






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         return 32






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						List[Any]:

         """simple docstring"""
         return self.time_input_dim






   @property
   def 	_lowerCAmelCase   (		self       :		Any   )			->						List[str]:

         """simple docstring"""
         return self.time_input_dim * 4






   @property
   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         return 1_00






   @property
   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         torch.manual_seed(0   )

         lowerCAmelCase__      :			Optional[int]   =	{
             """in_channels""": 4,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 8,
             """addition_embed_type""": """image""",
             """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
             """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
             """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
             """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
             """layers_per_block""": 1,
             """encoder_hid_dim""": self.text_embedder_hidden_size,
             """encoder_hid_dim_type""": """image_proj""",
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": None,
         }

         lowerCAmelCase__      :			Any   =	UNetaDConditionModel(**UpperCamelCase   )
         return model






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






   @property
   def 	_lowerCAmelCase   (		self       :		str   )			->						Dict:

         """simple docstring"""
         torch.manual_seed(0   )
         lowerCAmelCase__      :			Union[str, Any]   =	VQModel(**self.dummy_movq_kwargs   )
         return model






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	self.dummy_unet
         lowerCAmelCase__      :			Union[str, Any]   =	self.dummy_movq

         lowerCAmelCase__      :			Any   =	{
             """num_train_timesteps""": 10_00,
             """beta_schedule""": """linear""",
             """beta_start""": 0.0_0085,
             """beta_end""": 0.012,
             """clip_sample""": False,
             """set_alpha_to_one""": False,
             """steps_offset""": 0,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
         }

         lowerCAmelCase__      :			int   =	DDIMScheduler(**UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Dict=0   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	floats_tensor((1, self.text_embedder_hidden_size)   ,     rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	floats_tensor((1, self.text_embedder_hidden_size)   ,     rng=random.Random(seed + 1   )   ).to(
             UpperCamelCase   )
         # create init_image
         lowerCAmelCase__      :			Tuple   =	floats_tensor((1, 3, 64, 64)   ,     rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			int   =	image.cpu().permute(0   ,     2   ,     3   ,     1   )[0]
         lowerCAmelCase__      :			Tuple   =	Image.fromarray(np.uinta(UpperCamelCase   )   ).convert("""RGB"""   ).resize((2_56, 2_56)   )

         if str(UpperCamelCase   ).startswith("""mps"""   ):
               lowerCAmelCase__      :			Dict   =	torch.manual_seed(UpperCamelCase   )
         else:
               lowerCAmelCase__      :			int   =	torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	{
             """image""": init_image,
             """image_embeds""": image_embeds,
             """negative_image_embeds""": negative_image_embeds,
             """generator""": generator,
             """height""": 64,
             """width""": 64,
             """num_inference_steps""": 10,
             """guidance_scale""": 7.0,
             """strength""": 0.2,
             """output_type""": """np""",
         }
         return inputs






   def 	_lowerCAmelCase   (		self       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"""cpu"""

         lowerCAmelCase__      :			Union[str, Any]   =	self.get_dummy_components()

         lowerCAmelCase__      :			Union[str, Any]   =	self.pipeline_class(**UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	pipe.to(UpperCamelCase   )

         pipe.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	pipe(**self.get_dummy_inputs(UpperCamelCase   )   )
         lowerCAmelCase__      :			List[str]   =	output.images

         lowerCAmelCase__      :			Dict   =	pipe(
             **self.get_dummy_inputs(UpperCamelCase   )   ,     return_dict=UpperCamelCase   ,     )[0]

         lowerCAmelCase__      :			Optional[Any]   =	image[0, -3:, -3:, -1]
         lowerCAmelCase__      :			Union[str, Any]   =	image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         lowerCAmelCase__      :			Dict   =	np.array(
             [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]   )
         assert (
             np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






@slow
@require_torch_gpu
class _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		int   )			->						List[str]:

         """simple docstring"""
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinskyv22/kandinskyv22_img2img_frog.npy"""   )

         lowerCAmelCase__      :			Optional[int]   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
         lowerCAmelCase__      :			Dict   =	"""A red cartoon frog, 4k"""

         lowerCAmelCase__      :			Optional[int]   =	KandinskyVaaPriorPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-2-prior"""   ,     torch_dtype=torch.floataa   )
         pipe_prior.to(UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	KandinskyVaaImgaImgPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-2-decoder"""   ,     torch_dtype=torch.floataa   )
         lowerCAmelCase__      :			Tuple   =	pipeline.to(UpperCamelCase   )

         pipeline.set_progress_bar_config(disable=UpperCamelCase   )

         lowerCAmelCase__      :			str   =	torch.Generator(device="""cpu"""   ).manual_seed(0   )
         lowerCAmelCase__      :			List[str]   =	pipe_prior(
             UpperCamelCase   ,     generator=UpperCamelCase   ,     num_inference_steps=5   ,     negative_prompt=""""""   ,     ).to_tuple()

         lowerCAmelCase__      :			Union[str, Any]   =	pipeline(
             image=UpperCamelCase   ,     image_embeds=UpperCamelCase   ,     negative_image_embeds=UpperCamelCase   ,     generator=UpperCamelCase   ,     num_inference_steps=1_00   ,     height=7_68   ,     width=7_68   ,     strength=0.2   ,     output_type="""np"""   ,     )

         lowerCAmelCase__      :			Optional[int]   =	output.images[0]

         assert image.shape == (7_68, 7_68, 3)

         assert_mean_pixel_difference(UpperCamelCase   ,     UpperCamelCase   )











"""simple docstring"""


from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
  from transformers import (
      TFBertModel,
      TFCLIPVisionModel,
      TFDeiTModel,
      TFRobertaModel,
      TFVisionTextDualEncoderModel,
      TFViTModel,
      VisionTextDualEncoderConfig,
  )

if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      if isinstance(__UpperCAmelCase    ,		collections.abc.Iterable				):
            return x
      return (x, x)


@require_tf
class _lowerCamelCase    :






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		int   )			->						int:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[Any]:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Any=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	TFVisionTextDualEncoderModel(UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         self.assertEqual(output["""text_embeds"""].shape   ,     (input_ids.shape[0], config.projection_dim)   )
         self.assertEqual(output["""image_embeds"""].shape   ,     (pixel_values.shape[0], config.projection_dim)   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Any=None   ,     **UpperCamelCase       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         self.assertEqual(output["""text_embeds"""].shape   ,     (input_ids.shape[0], model.config.projection_dim)   )
         self.assertEqual(output["""image_embeds"""].shape   ,     (pixel_values.shape[0], model.config.projection_dim)   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		List[str]=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	{"""vision_model""": vision_model, """text_model""": text_model}
         lowerCAmelCase__      :			Optional[int]   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )

         self.assertEqual(output["""text_embeds"""].shape   ,     (input_ids.shape[0], model.config.projection_dim)   )
         self.assertEqual(output["""image_embeds"""].shape   ,     (pixel_values.shape[0], model.config.projection_dim)   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Tuple=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			List[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	output[0].numpy()

         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(UpperCamelCase   )
               lowerCAmelCase__      :			str   =	TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase   )

               lowerCAmelCase__      :			Optional[Any]   =	model(input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   )
               lowerCAmelCase__      :			int   =	after_output[0].numpy()
               lowerCAmelCase__      :			Tuple   =	np.amax(np.abs(out_a - out_a   )   )
               self.assertLessEqual(UpperCamelCase   ,     1E-5   )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		List[Any]=None   ,     **UpperCamelCase       :		Optional[int]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			int   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	model(
             input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   ,     output_attentions=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	output.vision_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     vision_config.num_hidden_layers   )

         # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
         lowerCAmelCase__      :			Optional[int]   =	to_atuple(vision_model.config.image_size   )
         lowerCAmelCase__      :			Any   =	to_atuple(vision_model.config.patch_size   )
         lowerCAmelCase__      :			List[str]   =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         lowerCAmelCase__      :			Tuple   =	num_patches + 1
         self.assertEqual(vision_attentions[0].shape[-3:]   ,     (vision_config.num_attention_heads, seq_len, seq_len)   )

         lowerCAmelCase__      :			List[str]   =	output.text_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     text_config.num_hidden_layers   )

         self.assertEqual(
             text_attentions[0].shape[-3:]   ,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,     )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		float   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	np.abs((a - b)   ).max()
         self.assertLessEqual(UpperCamelCase   ,     UpperCamelCase   ,     f"""Difference between torch and flax is {diff} (>= {tol})."""   )






   def 	_lowerCAmelCase   (		self       :		str   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_model(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	self.prepare_config_and_inputs()
         self.check_save_load(**UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**UpperCamelCase   )






   @slow
   def 	_lowerCAmelCase   (		self       :		str   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[str]   =	self.get_pretrained_model_and_inputs()

         lowerCAmelCase__      :			Union[str, Any]   =	model_a(**UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	outputs[0].numpy()

         with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(UpperCamelCase   )
               lowerCAmelCase__      :			int   =	TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase   )

               lowerCAmelCase__      :			List[str]   =	model_a(**UpperCamelCase   )
               lowerCAmelCase__      :			Dict   =	after_outputs[0].numpy()
               lowerCAmelCase__      :			List[str]   =	np.amax(np.abs(out_a - out_a   )   )
               self.assertLessEqual(UpperCamelCase   ,     1E-5   )






@require_tf
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """hf-internal-testing/tiny-random-vit"""   ,     """hf-internal-testing/tiny-random-bert"""   )
         lowerCAmelCase__      :			Optional[Any]   =	13
         lowerCAmelCase__      :			List[str]   =	floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]   )
         lowerCAmelCase__      :			Dict   =	ids_tensor([batch_size, 4]   ,     model.text_model.config.vocab_size   )
         lowerCAmelCase__      :			Optional[Any]   =	random_attention_mask([batch_size, 4]   )
         lowerCAmelCase__      :			Dict   =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		int   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFViTModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			Any   =	TFBertModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFViTModelTester(self   )
         lowerCAmelCase__      :			str   =	TFBertModelTester(self   )
         lowerCAmelCase__      :			Optional[int]   =	vit_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			List[Any]   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			List[Any]   =	text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }






@require_tf
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		int   )			->						str:

         """simple docstring"""
         # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
         # just reinitialize it.
         lowerCAmelCase__      :			Union[str, Any]   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """Rocketknight1/tiny-random-deit-tf"""   ,     """hf-internal-testing/tiny-random-roberta"""   )
         lowerCAmelCase__      :			str   =	13
         lowerCAmelCase__      :			Optional[int]   =	floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]   )
         lowerCAmelCase__      :			List[Any]   =	ids_tensor([batch_size, 4]   ,     model.text_model.config.vocab_size   )
         lowerCAmelCase__      :			Optional[Any]   =	random_attention_mask([batch_size, 4]   )
         lowerCAmelCase__      :			List[Any]   =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		str=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	self.get_vision_text_model(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	TFVisionTextDualEncoderModel(vision_model=UpperCamelCase   ,     text_model=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[Any]   =	model(
             input_ids=UpperCamelCase   ,     pixel_values=UpperCamelCase   ,     attention_mask=UpperCamelCase   ,     output_attentions=UpperCamelCase   )

         lowerCAmelCase__      :			Optional[int]   =	output.vision_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     vision_config.num_hidden_layers   )

         # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
         lowerCAmelCase__      :			Dict   =	to_atuple(vision_model.config.image_size   )
         lowerCAmelCase__      :			Any   =	to_atuple(vision_model.config.patch_size   )
         lowerCAmelCase__      :			Dict   =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         lowerCAmelCase__      :			Optional[Any]   =	num_patches + 2
         self.assertEqual(vision_attentions[0].shape[-3:]   ,     (vision_config.num_attention_heads, seq_len, seq_len)   )

         lowerCAmelCase__      :			Union[str, Any]   =	output.text_model_output.attentions
         self.assertEqual(len(UpperCamelCase   )   ,     text_config.num_hidden_layers   )

         self.assertEqual(
             text_attentions[0].shape[-3:]   ,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,     )






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		str   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	TFDeiTModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			str   =	TFRobertaModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	TFDeiTModelTester(self   )
         lowerCAmelCase__      :			Union[str, Any]   =	TFRobertaModelTester(self   )
         lowerCAmelCase__      :			Optional[int]   =	vit_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			Any   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			str   =	text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }






@require_tf
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             """Rocketknight1/tiny-random-clip-tf"""   ,     """hf-internal-testing/tiny-random-bert"""   )
         lowerCAmelCase__      :			Any   =	13
         lowerCAmelCase__      :			List[str]   =	floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ]   )
         lowerCAmelCase__      :			Optional[Any]   =	ids_tensor([batch_size, 4]   ,     model.text_model.config.vocab_size   )
         lowerCAmelCase__      :			str   =	random_attention_mask([batch_size, 4]   )
         lowerCAmelCase__      :			List[str]   =	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

         return model, inputs






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	TFCLIPVisionModel(UpperCamelCase   ,     name="""vision_model"""   )
         lowerCAmelCase__      :			List[str]   =	TFBertModel(UpperCamelCase   ,     name="""text_model"""   )
         return vision_model, text_model






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	TFCLIPVisionModelTester(self   )
         lowerCAmelCase__      :			int   =	TFBertModelTester(self   )
         lowerCAmelCase__      :			str   =	clip_model_tester.prepare_config_and_inputs()
         lowerCAmelCase__      :			Optional[int]   =	bert_model_tester.prepare_config_and_inputs()

         lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	vision_config_and_inputs

         (
             (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   (
             lowerCAmelCase__
         ) ,   
         )      :			str   =	text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }






@require_vision
@require_tf
class _lowerCamelCase    (					unittest.TestCase						):






   @slow
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	TFVisionTextDualEncoderModel.from_pretrained(
             """clip-italian/clip-italian"""   ,     logit_scale_init_value=1.0   ,     from_pt=UpperCamelCase   )
         lowerCAmelCase__      :			Any   =	VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""   )

         lowerCAmelCase__      :			Any   =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
         lowerCAmelCase__      :			Tuple   =	processor(
             text=["""una foto di un gatto""", """una foto di un cane"""]   ,     images=UpperCamelCase   ,     padding=UpperCamelCase   ,     return_tensors="""np"""   )

         lowerCAmelCase__      :			Tuple   =	model(**UpperCamelCase   )

         # verify the logits
         self.assertEqual(outputs.logits_per_image.shape   ,     (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])   )
         self.assertEqual(
             outputs.logits_per_text.shape   ,     (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])   ,     )

         lowerCAmelCase__      :			List[Any]   =	np.array([[1.228_4727, 0.310_4122]]   )

         self.assertTrue(np.allclose(outputs.logits_per_image.numpy()   ,     UpperCamelCase   ,     atol=1E-3   )   )





