

def   a   (    A__			:     str   ,     A__			:     int  )					->   str:





      """simple docstring"""



      _lowercase			 =[[] for _ in range(A__  )]
      _lowercase			 =key - 1

      if key <= 0:
            raise ValueError('Height of grid can\'t be 0 or negative'  )
      if key == 1 or len(A__  ) <= key:
            return input_string

      for position, character in enumerate(A__  ):
            _lowercase			 =position % (lowest * 2)  # puts it in bounds
            _lowercase			 =min(A__   ,     lowest * 2 - num  )  # creates zigzag pattern
            temp_grid[num].append(A__  )
      _lowercase			 =[''.join(A__  ) for row in temp_grid]
      _lowercase			 =''.join(A__  )

      return output_string
def   a   (    A__			:     str   ,     A__			:     int  )					->   str:





      """simple docstring"""



      _lowercase			 =[]
      _lowercase			 =key - 1

      if key <= 0:
            raise ValueError('Height of grid can\'t be 0 or negative'  )
      if key == 1:
            return input_string

      _lowercase			 =[[] for _ in range(A__  )]  # generates template
      for position in range(len(A__  )  ):
            _lowercase			 =position % (lowest * 2)  # puts it in bounds
            _lowercase			 =min(A__   ,     lowest * 2 - num  )  # creates zigzag pattern
            temp_grid[num].append('*'  )

      _lowercase			 =0
      for row in temp_grid:  # fills in the characters
            _lowercase			 =input_string[counter : counter + len(A__  )]
            grid.append(list(A__  )  )
            counter += len(A__  )

      _lowercase			 =''  # reads as zigzag
      for position in range(len(A__  )  ):
            _lowercase			 =position % (lowest * 2)  # puts it in bounds
            _lowercase			 =min(A__   ,     lowest * 2 - num  )  # creates zigzag pattern
            output_string += grid[num][0]
            grid[num].pop(0  )
      return output_string
def   a   (    A__			:     str  )					->   dict[int, str]:





      """simple docstring"""



      _lowercase			 ={}
      for key_guess in range(1   ,     len(A__  )  ):  # tries every key
            _lowercase			 =decrypt(A__   ,     A__  )
      return results


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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   a   (    A__			:     List[Any]  )					->   Union[str, Any]:





      """simple docstring"""



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





@require_tf
class 				__lowerCAmelCase  :




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        pass




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =TFVisionTextDualEncoderModel(lowerCAmelCase      )

        _lowercase			 =model(input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase      )

        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							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_vision_text_model(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase							,     text_model=lowerCAmelCase      )

        _lowercase			 =model(input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase      )

        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							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_vision_text_model(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 ={'vision_model': vision_model, 'text_model': text_model}
        _lowercase			 =TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase      )

        _lowercase			 =model(input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase      )

        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							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_vision_text_model(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase							,     text_model=lowerCAmelCase      )

        _lowercase			 =model(input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase      )
        _lowercase			 =output[0].numpy()

        with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(lowerCAmelCase      )
              _lowercase			 =TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase      )

              _lowercase			 =model(input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase      )
              _lowercase			 =after_output[0].numpy()
              _lowercase			 =np.amax(np.abs(out_a - out_a      )      )
              self.assertLessEqual(lowerCAmelCase							,     1e-5      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_vision_text_model(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase							,     text_model=lowerCAmelCase      )

        _lowercase			 =model(
            input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase							,     output_attentions=lowerCAmelCase      )

        _lowercase			 =output.vision_model_output.attentions
        self.assertEqual(len(lowerCAmelCase      )							,     vision_config.num_hidden_layers      )

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

        _lowercase			 =output.text_model_output.attentions
        self.assertEqual(len(lowerCAmelCase      )							,     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							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_model(**lowerCAmelCase      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =self.prepare_config_and_inputs()
        self.check_model_from_pretrained_configs(**lowerCAmelCase      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =self.prepare_config_and_inputs()
        self.check_save_load(**lowerCAmelCase      )




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =self.prepare_config_and_inputs()
        self.check_vision_text_output_attention(**lowerCAmelCase      )




  @slow
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_pretrained_model_and_inputs()

        _lowercase			 =model_a(**lowerCAmelCase      )
        _lowercase			 =outputs[0].numpy()

        with tempfile.TemporaryDirectory() as tmp_dirname:
              model_a.save_pretrained(lowerCAmelCase      )
              _lowercase			 =TFVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase      )

              _lowercase			 =model_a(**lowerCAmelCase      )
              _lowercase			 =after_outputs[0].numpy()
              _lowercase			 =np.amax(np.abs(out_a - out_a      )      )
              self.assertLessEqual(lowerCAmelCase							,     1e-5      )





@require_tf
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            'hf-internal-testing/tiny-random-vit'							,     'hf-internal-testing/tiny-random-bert'      )
        _lowercase			 =13
        _lowercase			 =floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]      )
        _lowercase			 =ids_tensor([batch_size, 4]							,     model.text_model.config.vocab_size      )
        _lowercase			 =random_attention_mask([batch_size, 4]      )
        _lowercase			 ={'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

        return model, inputs




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =TFViTModel(lowerCAmelCase							,     name='vision_model'      )
        _lowercase			 =TFBertModel(lowerCAmelCase							,     name='text_model'      )
        return vision_model, text_model




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






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

        _lowercase , _lowercase , _lowercase			 =vision_config_and_inputs

        (
            (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , 
        )			 =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  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            'Rocketknight1/tiny-random-deit-tf'							,     'hf-internal-testing/tiny-random-roberta'      )
        _lowercase			 =13
        _lowercase			 =floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]      )
        _lowercase			 =ids_tensor([batch_size, 4]							,     model.text_model.config.vocab_size      )
        _lowercase			 =random_attention_mask([batch_size, 4]      )
        _lowercase			 ={'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

        return model, inputs




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     **lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.get_vision_text_model(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =TFVisionTextDualEncoderModel(vision_model=lowerCAmelCase							,     text_model=lowerCAmelCase      )

        _lowercase			 =model(
            input_ids=lowerCAmelCase							,     pixel_values=lowerCAmelCase							,     attention_mask=lowerCAmelCase							,     output_attentions=lowerCAmelCase      )

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

        _lowercase			 =output.text_model_output.attentions
        self.assertEqual(len(lowerCAmelCase      )							,     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							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =TFDeiTModel(lowerCAmelCase							,     name='vision_model'      )
        _lowercase			 =TFRobertaModel(lowerCAmelCase							,     name='text_model'      )
        return vision_model, text_model




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






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

        _lowercase , _lowercase , _lowercase			 =vision_config_and_inputs

        (
            (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , 
        )			 =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  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
            'Rocketknight1/tiny-random-clip-tf'							,     'hf-internal-testing/tiny-random-bert'      )
        _lowercase			 =13
        _lowercase			 =floats_tensor(
            [
                batch_size,
                model.vision_model.config.num_channels,
                model.vision_model.config.image_size,
                model.vision_model.config.image_size,
            ]      )
        _lowercase			 =ids_tensor([batch_size, 4]							,     model.text_model.config.vocab_size      )
        _lowercase			 =random_attention_mask([batch_size, 4]      )
        _lowercase			 ={'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

        return model, inputs




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =TFCLIPVisionModel(lowerCAmelCase							,     name='vision_model'      )
        _lowercase			 =TFBertModel(lowerCAmelCase							,     name='text_model'      )
        return vision_model, text_model




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






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

        _lowercase , _lowercase			 =vision_config_and_inputs

        (
            (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , (
            _lowercase
        ) , 
        )			 =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 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =TFVisionTextDualEncoderModel.from_pretrained(
            'clip-italian/clip-italian'							,     logit_scale_init_value=1.0							,     from_pt=lowerCAmelCase      )
        _lowercase			 =VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'      )

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

        _lowercase			 =model(**lowerCAmelCase      )

        # 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])							,     )

        _lowercase			 =np.array([[1.2284727, 0.3104122]]      )

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


