




"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class _lowerCamelCase    (					__a       ,	__a       ,	__a       ,	unittest.TestCase						):
   _lowerCamelCase    :Union[str, Any]     				=     StableDiffusionInpaintPipeline
   _lowerCamelCase    :Optional[int]     				=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _lowerCamelCase    :Tuple     				=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _lowerCamelCase    :str     				=     frozenset(
       []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _lowerCamelCase    :Optional[Any]     				=     frozenset([]						)






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

         """simple docstring"""
         torch.manual_seed(0   )
         lowerCAmelCase__      :			int   =	UNetaDConditionModel(
             block_out_channels=(32, 64)   ,     layers_per_block=2   ,     sample_size=32   ,     in_channels=9   ,     out_channels=4   ,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,     cross_attention_dim=32   ,     attention_head_dim=(2, 4)   ,     use_linear_projection=a__   ,     )
         lowerCAmelCase__      :			Optional[int]   =	PNDMScheduler(skip_prk_steps=a__   )
         torch.manual_seed(0   )
         lowerCAmelCase__      :			Union[str, Any]   =	AutoencoderKL(
             block_out_channels=[32, 64]   ,     in_channels=3   ,     out_channels=3   ,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,     latent_channels=4   ,     sample_size=1_28   ,     )
         torch.manual_seed(0   )
         lowerCAmelCase__      :			Optional[int]   =	CLIPTextConfig(
             bos_token_id=0   ,     eos_token_id=2   ,     hidden_size=32   ,     intermediate_size=37   ,     layer_norm_eps=1E-0_5   ,     num_attention_heads=4   ,     num_hidden_layers=5   ,     pad_token_id=1   ,     vocab_size=10_00   ,     hidden_act="""gelu"""   ,     projection_dim=5_12   ,     )
         lowerCAmelCase__      :			Dict   =	CLIPTextModel(a__   )
         lowerCAmelCase__      :			int   =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

         lowerCAmelCase__      :			int   =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[Any]=0   )			->						Any:

         """simple docstring"""
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         lowerCAmelCase__      :			Any   =	floats_tensor((1, 3, 32, 32)   ,     rng=random.Random(a__   )   ).to(a__   )
         lowerCAmelCase__      :			str   =	image.cpu().permute(0   ,     2   ,     3   ,     1   )[0]
         lowerCAmelCase__      :			List[str]   =	Image.fromarray(np.uinta(a__   )   ).convert("""RGB"""   ).resize((64, 64)   )
         lowerCAmelCase__      :			Tuple   =	Image.fromarray(np.uinta(image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )
         if str(a__   ).startswith("""mps"""   ):
               lowerCAmelCase__      :			List[Any]   =	torch.manual_seed(a__   )
         else:
               lowerCAmelCase__      :			str   =	torch.Generator(device=a__   ).manual_seed(a__   )
         lowerCAmelCase__      :			Dict   =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": init_image,
             """mask_image""": mask_image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs






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

         """simple docstring"""
         lowerCAmelCase__      :			str   =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase__      :			Union[str, Any]   =	self.get_dummy_components()
         lowerCAmelCase__      :			Tuple   =	StableDiffusionInpaintPipeline(**a__   )
         lowerCAmelCase__      :			Any   =	sd_pipe.to(a__   )
         sd_pipe.set_progress_bar_config(disable=a__   )

         lowerCAmelCase__      :			int   =	self.get_dummy_inputs(a__   )
         lowerCAmelCase__      :			Tuple   =	sd_pipe(**a__   ).images
         lowerCAmelCase__      :			int   =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         lowerCAmelCase__      :			int   =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2






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

         """simple docstring"""
         super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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






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

         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-inpaint/init_image.png"""   )
         lowerCAmelCase__      :			Any   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
         lowerCAmelCase__      :			str   =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
             """/yellow_cat_sitting_on_a_park_bench.npy"""   )

         lowerCAmelCase__      :			str   =	"""stabilityai/stable-diffusion-2-inpainting"""
         lowerCAmelCase__      :			List[str]   =	StableDiffusionInpaintPipeline.from_pretrained(a__   ,     safety_checker=a__   )
         pipe.to(a__   )
         pipe.set_progress_bar_config(disable=a__   )
         pipe.enable_attention_slicing()

         lowerCAmelCase__      :			Optional[int]   =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

         lowerCAmelCase__      :			str   =	torch.manual_seed(0   )
         lowerCAmelCase__      :			Dict   =	pipe(
             prompt=a__   ,     image=a__   ,     mask_image=a__   ,     generator=a__   ,     output_type="""np"""   ,     )
         lowerCAmelCase__      :			Any   =	output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image   ).max() < 9E-3






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

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-inpaint/init_image.png"""   )
         lowerCAmelCase__      :			Tuple   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
         lowerCAmelCase__      :			Dict   =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
             """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""   )

         lowerCAmelCase__      :			List[str]   =	"""stabilityai/stable-diffusion-2-inpainting"""
         lowerCAmelCase__      :			Any   =	StableDiffusionInpaintPipeline.from_pretrained(
             a__   ,     torch_dtype=torch.floataa   ,     safety_checker=a__   ,     )
         pipe.to(a__   )
         pipe.set_progress_bar_config(disable=a__   )
         pipe.enable_attention_slicing()

         lowerCAmelCase__      :			List[str]   =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

         lowerCAmelCase__      :			str   =	torch.manual_seed(0   )
         lowerCAmelCase__      :			Tuple   =	pipe(
             prompt=a__   ,     image=a__   ,     mask_image=a__   ,     generator=a__   ,     output_type="""np"""   ,     )
         lowerCAmelCase__      :			Tuple   =	output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image   ).max() < 5E-1






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

         """simple docstring"""
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__      :			Tuple   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/sd2-inpaint/init_image.png"""   )
         lowerCAmelCase__      :			Any   =	load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )

         lowerCAmelCase__      :			List[Any]   =	"""stabilityai/stable-diffusion-2-inpainting"""
         lowerCAmelCase__      :			Tuple   =	PNDMScheduler.from_pretrained(a__   ,     subfolder="""scheduler"""   )
         lowerCAmelCase__      :			int   =	StableDiffusionInpaintPipeline.from_pretrained(
             a__   ,     safety_checker=a__   ,     scheduler=a__   ,     torch_dtype=torch.floataa   ,     )
         pipe.to(a__   )
         pipe.set_progress_bar_config(disable=a__   )
         pipe.enable_attention_slicing(1   )
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__      :			str   =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

         lowerCAmelCase__      :			int   =	torch.manual_seed(0   )
         lowerCAmelCase__      :			Tuple   =	pipe(
             prompt=a__   ,     image=a__   ,     mask_image=a__   ,     generator=a__   ,     num_inference_steps=2   ,     output_type="""np"""   ,     )

         lowerCAmelCase__      :			Tuple   =	torch.cuda.max_memory_allocated()
         # make sure that less than 2.65 GB is allocated
         assert mem_bytes < 2.65 * 10**9











"""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   )   )





