


'''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    (   __SCREAMING_SNAKE_CASE		)					->       List[str]:
     """simple docstring"""




     if isinstance(UpperCamelCase__					,	collections.abc.Iterable		):
          return x
     return (x, x)


@require_tf
class 			lowerCAmelCase_    :





  """simple docstring"""



  def __a    (       self  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				Any					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]    ):





       '''simple docstring'''

       pass

  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       pass

  def __a    (       self  :				Dict    ):





       '''simple docstring'''

       pass

  def __a    (       self  :				int					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				int=None					,				**SCREAMING_SNAKE_CASE__  :				int    ):





       '''simple docstring'''

       __a   			= VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

       __a   			= TFVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE__    )

       __a   			= model(input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__    )

       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 __a    (       self  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				Any					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				str=None					,				**SCREAMING_SNAKE_CASE__  :				Tuple    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_vision_text_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__					,				text_model=SCREAMING_SNAKE_CASE__    )

       __a   			= model(input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__    )

       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 __a    (       self  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				Tuple=None					,				**SCREAMING_SNAKE_CASE__  :				Union[str, Any]    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_vision_text_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= {"""vision_model""": vision_model, """text_model""": text_model}
       __a   			= TFVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE__    )

       __a   			= model(input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__    )

       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 __a    (       self  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				Any=None					,				**SCREAMING_SNAKE_CASE__  :				Dict    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_vision_text_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__					,				text_model=SCREAMING_SNAKE_CASE__    )

       __a   			= model(input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__    )
       __a   			= output[0].numpy()

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(SCREAMING_SNAKE_CASE__    )
            __a   			= TFVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__    )

            __a   			= model(input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__    )
            __a   			= after_output[0].numpy()
            __a   			= np.amax(np.abs(out_a - out_a    )    )
            self.assertLessEqual(SCREAMING_SNAKE_CASE__					,				1E-5    )

  def __a    (       self  :				Dict					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				Dict=None					,				**SCREAMING_SNAKE_CASE__  :				Optional[Any]    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_vision_text_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__					,				text_model=SCREAMING_SNAKE_CASE__    )

       __a   			= model(
           input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				output_attentions=SCREAMING_SNAKE_CASE__    )

       __a   			= output.vision_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE__    )					,				vision_config.num_hidden_layers    )

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

       __a   			= output.text_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE__    )					,				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 __a    (       self  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				str    ):





       '''simple docstring'''

       __a   			= np.abs((a - b)    ).max()
       self.assertLessEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				f'''Difference between torch and flax is {diff} (>= {tol}).'''    )

  def __a    (       self  :				int    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_model(**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				int    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Optional[int]    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       self.check_save_load(**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE__    )


  @slow
  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_pretrained_model_and_inputs()

       __a   			= model_a(**SCREAMING_SNAKE_CASE__    )
       __a   			= outputs[0].numpy()

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(SCREAMING_SNAKE_CASE__    )
            __a   			= TFVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE__    )

            __a   			= model_a(**SCREAMING_SNAKE_CASE__    )
            __a   			= after_outputs[0].numpy()
            __a   			= np.amax(np.abs(out_a - out_a    )    )
            self.assertLessEqual(SCREAMING_SNAKE_CASE__					,				1E-5    )


@require_tf
class 			lowerCAmelCase_    (							lowercase_		,     unittest.TestCase   ):





  """simple docstring"""



  def __a    (       self  :				Union[str, Any]    ):





       '''simple docstring'''

       __a   			= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           """hf-internal-testing/tiny-random-vit"""					,				"""hf-internal-testing/tiny-random-bert"""    )
       __a   			= 1_3
       __a   			= floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ]    )
       __a   			= ids_tensor([batch_size, 4]					,				model.text_model.config.vocab_size    )
       __a   			= random_attention_mask([batch_size, 4]    )
       __a   			= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

       return model, inputs

  def __a    (       self  :				str					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				str    ):





       '''simple docstring'''

       __a   			= TFViTModel(SCREAMING_SNAKE_CASE__					,				name="""vision_model"""    )
       __a   			= TFBertModel(SCREAMING_SNAKE_CASE__					,				name="""text_model"""    )
       return vision_model, text_model


  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       __a   			= TFViTModelTester(self    )
       __a   			= TFBertModelTester(self    )
       __a   			= vit_model_tester.prepare_config_and_inputs()
       __a   			= bert_model_tester.prepare_config_and_inputs()

       __a			,    __a			,    __a   			= vision_config_and_inputs

       (
           (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,           
       )   			= 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_    (							lowercase_		,     unittest.TestCase   ):





  """simple docstring"""



  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       __a   			= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           """Rocketknight1/tiny-random-deit-tf"""					,				"""hf-internal-testing/tiny-random-roberta"""    )
       __a   			= 1_3
       __a   			= floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ]    )
       __a   			= ids_tensor([batch_size, 4]					,				model.text_model.config.vocab_size    )
       __a   			= random_attention_mask([batch_size, 4]    )
       __a   			= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

       return model, inputs

  def __a    (       self  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				int=None					,				**SCREAMING_SNAKE_CASE__  :				List[str]    ):





       '''simple docstring'''

       __a			,    __a   			= self.get_vision_text_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= TFVisionTextDualEncoderModel(vision_model=SCREAMING_SNAKE_CASE__					,				text_model=SCREAMING_SNAKE_CASE__    )

       __a   			= model(
           input_ids=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				output_attentions=SCREAMING_SNAKE_CASE__    )

       __a   			= output.vision_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE__    )					,				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)
       __a   			= to_atuple(vision_model.config.image_size    )
       __a   			= to_atuple(vision_model.config.patch_size    )
       __a   			= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       __a   			= num_patches + 2
       self.assertEqual(vision_attentions[0].shape[-3:]					,				(vision_config.num_attention_heads, seq_len, seq_len)    )

       __a   			= output.text_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE__    )					,				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 __a    (       self  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				Any					,				SCREAMING_SNAKE_CASE__  :				Optional[int]    ):





       '''simple docstring'''

       __a   			= TFDeiTModel(SCREAMING_SNAKE_CASE__					,				name="""vision_model"""    )
       __a   			= TFRobertaModel(SCREAMING_SNAKE_CASE__					,				name="""text_model"""    )
       return vision_model, text_model


  def __a    (       self  :				Optional[int]    ):





       '''simple docstring'''

       __a   			= TFDeiTModelTester(self    )
       __a   			= TFRobertaModelTester(self    )
       __a   			= vit_model_tester.prepare_config_and_inputs()
       __a   			= bert_model_tester.prepare_config_and_inputs()

       __a			,    __a			,    __a   			= vision_config_and_inputs

       (
           (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,           
       )   			= 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_    (							lowercase_		,     unittest.TestCase   ):





  """simple docstring"""



  def __a    (       self  :				List[Any]    ):





       '''simple docstring'''

       __a   			= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
           """Rocketknight1/tiny-random-clip-tf"""					,				"""hf-internal-testing/tiny-random-bert"""    )
       __a   			= 1_3
       __a   			= floats_tensor(
           [
               batch_size,
               model.vision_model.config.num_channels,
               model.vision_model.config.image_size,
               model.vision_model.config.image_size,
           ]    )
       __a   			= ids_tensor([batch_size, 4]					,				model.text_model.config.vocab_size    )
       __a   			= random_attention_mask([batch_size, 4]    )
       __a   			= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

       return model, inputs

  def __a    (       self  :				int					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Dict    ):





       '''simple docstring'''

       __a   			= TFCLIPVisionModel(SCREAMING_SNAKE_CASE__					,				name="""vision_model"""    )
       __a   			= TFBertModel(SCREAMING_SNAKE_CASE__					,				name="""text_model"""    )
       return vision_model, text_model


  def __a    (       self  :				int    ):





       '''simple docstring'''

       __a   			= TFCLIPVisionModelTester(self    )
       __a   			= TFBertModelTester(self    )
       __a   			= clip_model_tester.prepare_config_and_inputs()
       __a   			= bert_model_tester.prepare_config_and_inputs()

       __a			,    __a   			= vision_config_and_inputs

       (
           (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,    (
           __a       
       )			,           
       )   			= 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   ):





  """simple docstring"""



  @slow
  def __a    (       self  :				Union[str, Any]    ):





       '''simple docstring'''

       __a   			= TFVisionTextDualEncoderModel.from_pretrained(
           """clip-italian/clip-italian"""					,				logit_scale_init_value=1.0					,				from_pt=SCREAMING_SNAKE_CASE__    )
       __a   			= VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""    )

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

       __a   			= model(**SCREAMING_SNAKE_CASE__    )

       # 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])					,				)

       __a   			= np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]    )

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









def    __lowerCamelCase      (					UpperCamelCase__ = 1000 ):







							'''simple docstring'''






							snake_case_     							=       2**power
							snake_case_     							=       0
							while n:
														snake_case_							,       snake_case_     							=       r + n % 10, n // 10
							return r


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))




