



import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
     from transformers import (
         FlaxBertModel,
         FlaxCLIPVisionModel,
         FlaxVisionTextDualEncoderModel,
         FlaxViTModel,
         VisionTextDualEncoderConfig,
         VisionTextDualEncoderProcessor,
     )
     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )


if is_torch_available():
     import torch

     from transformers import VisionTextDualEncoderModel

if is_vision_available():
     from PIL import Image

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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





@require_flax
class  lowerCamelCase__	:
     """simple docstring"""



     def     _a   (self					,      __a					,      __a     ):






           '''simple docstring'''



           pass







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a=None					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		VisionTextDualEncoderConfig.from_vision_text_configs(__a					,      __a     )

           lowerCamelCase             =		FlaxVisionTextDualEncoderModel(__a     )

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

           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					,      __a					,      __a					,      __a					,      __a					,      __a=None					,      **__a     ):






           '''simple docstring'''



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

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

           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					,      __a					,      __a					,      __a					,      __a					,      __a=None					,      **__a     ):






           '''simple docstring'''



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

           lowerCamelCase             =		model(input_ids=__a					,      pixel_values=__a					,      attention_mask=__a     )
           lowerCamelCase             =		output[0]

           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(__a     )
                 lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_pretrained(__a     )

                 lowerCamelCase             =		model(input_ids=__a					,      pixel_values=__a					,      attention_mask=__a     )
                 lowerCamelCase             =		after_output[0]
                 lowerCamelCase             =		np.amax(np.abs(out_a - out_a     )     )
                 self.assertLessEqual(__a					,      1E-3     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a=None					,      **__a     ):






           '''simple docstring'''



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

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

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

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

           lowerCamelCase             =		output.text_model_output.attentions
           self.assertEqual(len(__a     )					,      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					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           pt_model.to(__a     )
           pt_model.eval()

           # prepare inputs
           lowerCamelCase             =		inputs_dict
           lowerCamelCase             =		{k: torch.tensor(v.tolist()     ) for k, v in flax_inputs.items()}

           with torch.no_grad():
                 lowerCamelCase             =		pt_model(**__a     ).to_tuple()

           lowerCamelCase             =		fx_model(**__a     ).to_tuple()
           self.assertEqual(len(__a     )					,      len(__a     )					,      "Output lengths differ between Flax and PyTorch"     )
           for fx_output, pt_output in zip(fx_outputs[:4]					,      pt_outputs[:4]     ):
                 self.assert_almost_equals(__a					,      pt_output.numpy()					,      4E-2     )

           # PT -> Flax
           with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(__a     )
                 lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_pretrained(__a					,      from_pt=__a     )

           lowerCamelCase             =		fx_model_loaded(**__a     ).to_tuple()
           self.assertEqual(len(__a     )					,      len(__a     )					,      "Output lengths differ between Flax and PyTorch"     )
           for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]					,      pt_outputs[:4]     ):
                 self.assert_almost_equals(__a					,      pt_output.numpy()					,      4E-2     )

           # Flax -> PT
           with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(__a     )
                 lowerCamelCase             =		VisionTextDualEncoderModel.from_pretrained(__a					,      from_flax=__a     )

           pt_model_loaded.to(__a     )
           pt_model_loaded.eval()

           with torch.no_grad():
                 lowerCamelCase             =		pt_model_loaded(**__a     ).to_tuple()

           self.assertEqual(len(__a     )					,      len(__a     )					,      "Output lengths differ between Flax and PyTorch"     )
           for fx_output, pt_output_loaded in zip(fx_outputs[:4]					,      pt_outputs_loaded[:4]     ):
                 self.assert_almost_equals(__a					,      pt_output_loaded.numpy()					,      4E-2     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		VisionTextDualEncoderConfig.from_vision_text_configs(__a					,      __a     )

           lowerCamelCase             =		VisionTextDualEncoderModel(__a     )
           lowerCamelCase             =		FlaxVisionTextDualEncoderModel(__a     )

           lowerCamelCase             =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __a     )
           lowerCamelCase             =		fx_state

           self.check_pt_flax_equivalence(__a					,      __a					,      __a     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		VisionTextDualEncoderConfig.from_vision_text_configs(__a					,      __a     )

           lowerCamelCase             =		VisionTextDualEncoderModel(__a     )
           lowerCamelCase             =		FlaxVisionTextDualEncoderModel(__a     )

           lowerCamelCase             =		load_flax_weights_in_pytorch_model(__a					,      fx_model.params     )

           self.check_pt_flax_equivalence(__a					,      __a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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







     @is_pt_flax_cross_test
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           lowerCamelCase             =		config_inputs_dict.pop("vision_config"     )
           lowerCamelCase             =		config_inputs_dict.pop("text_config"     )

           lowerCamelCase             =		config_inputs_dict

           self.check_equivalence_pt_to_flax(__a					,      __a					,      __a     )
           self.check_equivalence_flax_to_pt(__a					,      __a					,      __a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.get_pretrained_model_and_inputs()

           lowerCamelCase             =		model_a(**__a     )
           lowerCamelCase             =		outputs[0]

           with tempfile.TemporaryDirectory() as tmp_dirname:
                 model_a.save_pretrained(__a     )
                 lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_pretrained(__a     )

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





@require_flax
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               "hf-internal-testing/tiny-random-vit"					,      "hf-internal-testing/tiny-bert"					,      vision_from_pt=__a					,      text_from_pt=__a					,      )
           lowerCamelCase             =		13
           lowerCamelCase             =		floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]     )
           lowerCamelCase             =		ids_tensor([batch_size, 4]					,      model.config.text_config.vocab_size     )
           lowerCamelCase             =		random_attention_mask([batch_size, 4]     )
           lowerCamelCase             =		{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

           return model, inputs







     def     _a   (self					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxViTModel(__a     )
           lowerCamelCase             =		FlaxBertModel(__a     )
           return vision_model, text_model







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxViTModelTester(self     )
           lowerCamelCase             =		FlaxBertModelTester(self     )
           lowerCamelCase             =		vit_model_tester.prepare_config_and_inputs()
           lowerCamelCase             =		bert_model_tester.prepare_config_and_inputs()

           lowerCamelCase      ,				lowerCamelCase             =		vision_config_and_inputs

           lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }





@require_torch
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               "hf-internal-testing/tiny-random-clip"					,      "hf-internal-testing/tiny-bert"					,      vision_from_pt=__a					,      text_from_pt=__a					,      )
           lowerCamelCase             =		13
           lowerCamelCase             =		floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]     )
           lowerCamelCase             =		ids_tensor([batch_size, 4]					,      model.config.text_config.vocab_size     )
           lowerCamelCase             =		random_attention_mask([batch_size, 4]     )
           lowerCamelCase             =		{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

           return model, inputs







     def     _a   (self					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxCLIPVisionModel(__a     )
           lowerCamelCase             =		FlaxBertModel(__a     )
           return vision_model, text_model







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxCLIPVisionModelTester(self     )
           lowerCamelCase             =		FlaxBertModelTester(self     )
           lowerCamelCase             =		clip_model_tester.prepare_config_and_inputs()
           lowerCamelCase             =		bert_model_tester.prepare_config_and_inputs()

           lowerCamelCase      ,				lowerCamelCase             =		vision_config_and_inputs

           lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }





@require_flax
@require_vision
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"					,      logit_scale_init_value=1.0     )
           lowerCamelCase             =		VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"     )

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

           lowerCamelCase             =		model(**__a     )

           # 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             =		np.array([[1.228_4727, 0.310_4122]]     )

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




def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
            return 0
      elif n == 2:
            return 1
      else:
            lowerCamelCase             =		[0, 1]
            for i in range(2 , n + 1			):
                  sequence.append(sequence[i - 1] + sequence[i - 2]			)

            return sequence[n]

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		0
      lowerCamelCase             =		2

      while digits < n:
            index += 1
            lowerCamelCase             =		len(str(fibonacci(UpperCAmelCase__			)			)			)

      return index

def      __lowercase(						UpperCAmelCase__ = 1000			):


      """simple docstring"""



      return fibonacci_digits_index(UpperCAmelCase__			)


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