
"""simple docstring"""

import itertools
import string
from collections.abc import Generator, Iterable






def 							A						(    snake_case				:Iterable[str]    , snake_case				:int		)				->     Generator[tuple[str, ...], None, None]:
 __UpperCamelCase      =	iter(snake_case		)
 while True:
  __UpperCamelCase      =	tuple(itertools.islice(snake_case    , snake_case		)		)
  if not chunk:
   return
  yield chunk






def 							A						(    snake_case				:str		)				->     str:
 __UpperCamelCase      =	''.join([c.upper() for c in dirty if c in string.ascii_letters]		)
 __UpperCamelCase      =	''

 if len(snake_case		) < 2:
  return dirty

 for i in range(len(snake_case		) - 1		):
  clean += dirty[i]

  if dirty[i] == dirty[i + 1]:
   clean += "X"

 clean += dirty[-1]

 if len(snake_case		) & 1:
  clean += "X"

 return clean






def 							A						(    snake_case				:str		)				->     list[str]:
 # I and J are used interchangeably to allow
 # us to use a 5x5 table (25 letters)
 __UpperCamelCase      =	'ABCDEFGHIKLMNOPQRSTUVWXYZ'
 # we're using a list instead of a '2d' array because it makes the math
 # for setting up the table and doing the actual encoding/decoding simpler
 __UpperCamelCase      =	[]

 # copy key chars into the table if they are in `alphabet` ignoring duplicates
 for char in key.upper():
  if char not in table and char in alphabet:
   table.append(snake_case		)

    # fill the rest of the table in with the remaining alphabet chars
 for char in alphabet:
  if char not in table:
   table.append(snake_case		)

 return table






def 							A						(    snake_case				:str    , snake_case				:str		)				->     str:
 __UpperCamelCase      =	generate_table(snake_case		)
 __UpperCamelCase      =	prepare_input(snake_case		)
 __UpperCamelCase      =	''

 # https://en.wikipedia.org/wiki/Playfair_cipher#Description
 for chara, chara in chunker(snake_case    , 2		):
  __UpperCamelCase  ,     __UpperCamelCase      =	divmod(table.index(snake_case		)    , 5		)
  __UpperCamelCase  ,     __UpperCamelCase      =	divmod(table.index(snake_case		)    , 5		)

  if rowa == rowa:
   ciphertext += table[rowa * 5 + (cola + 1) % 5]
   ciphertext += table[rowa * 5 + (cola + 1) % 5]
  elif cola == cola:
   ciphertext += table[((rowa + 1) % 5) * 5 + cola]
   ciphertext += table[((rowa + 1) % 5) * 5 + cola]
  else:  # rectangle
   ciphertext += table[rowa * 5 + cola]
   ciphertext += table[rowa * 5 + cola]

 return ciphertext






def 							A						(    snake_case				:str    , snake_case				:str		)				->     str:
 __UpperCamelCase      =	generate_table(snake_case		)
 __UpperCamelCase      =	''

 # https://en.wikipedia.org/wiki/Playfair_cipher#Description
 for chara, chara in chunker(snake_case    , 2		):
  __UpperCamelCase  ,     __UpperCamelCase      =	divmod(table.index(snake_case		)    , 5		)
  __UpperCamelCase  ,     __UpperCamelCase      =	divmod(table.index(snake_case		)    , 5		)

  if rowa == rowa:
   plaintext += table[rowa * 5 + (cola - 1) % 5]
   plaintext += table[rowa * 5 + (cola - 1) % 5]
  elif cola == cola:
   plaintext += table[((rowa - 1) % 5) * 5 + cola]
   plaintext += table[((rowa - 1) % 5) * 5 + cola]
  else:  # rectangle
   plaintext += table[rowa * 5 + cola]
   plaintext += table[rowa * 5 + cola]

 return plaintext





"""simple docstring"""

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          LevitForImageClassification,
          LevitForImageClassificationWithTeacher,
          LevitModel,
      )
      from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import LevitImageProcessor

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.config_class(**self.inputs_dict				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'hidden_sizes'				)				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'num_attention_heads'				)				)


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=13     ,					__UpperCAmelCase=64     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=[128, 256, 384]     ,					__UpperCAmelCase=[4, 6, 8]     ,					__UpperCAmelCase=[2, 3, 4]     ,					__UpperCAmelCase=[16, 16, 16]     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=2     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	kernel_size
      __UpperCamelCase      =	stride
      __UpperCamelCase      =	padding
      __UpperCamelCase      =	hidden_sizes
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	depths
      __UpperCamelCase      =	key_dim
      __UpperCamelCase      =	drop_path_rate
      __UpperCamelCase      =	patch_size
      __UpperCamelCase      =	attention_ratio
      __UpperCamelCase      =	mlp_ratio
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	[
          ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
          ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
      ]
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	num_labels
      __UpperCamelCase      =	initializer_range





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

      __UpperCamelCase      =	None
      if self.use_labels:
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					self.num_labels				)

      __UpperCamelCase      =	self.get_config()
      return config, pixel_values, labels





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return LevitConfig(
          image_size=self.image_size     ,					num_channels=self.num_channels     ,					kernel_size=self.kernel_size     ,					stride=self.stride     ,					padding=self.padding     ,					patch_size=self.patch_size     ,					hidden_sizes=self.hidden_sizes     ,					num_attention_heads=self.num_attention_heads     ,					depths=self.depths     ,					key_dim=self.key_dim     ,					drop_path_rate=self.drop_path_rate     ,					mlp_ratio=self.mlp_ratio     ,					attention_ratio=self.attention_ratio     ,					initializer_range=self.initializer_range     ,					down_ops=self.down_ops     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      __UpperCamelCase      =	(self.image_size, self.image_size)
      __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
      for _ in range(4				):
       __UpperCamelCase      =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
       __UpperCamelCase      =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,					(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	LevitForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.prepare_config_and_inputs()
      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	config_and_inputs
      __UpperCamelCase      =	{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     lowercase						       =						(
         {
             "feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=37				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return





     @unittest.skip(reason='Levit does not use inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Levit does not support input and output embeddings'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Levit does not output attentions'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       __UpperCamelCase      =	inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __UpperCamelCase      =	[*signature.parameters.keys()]

       __UpperCamelCase      =	['pixel_values']
       self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()

       with torch.no_grad():
        __UpperCamelCase      =	model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

       __UpperCamelCase      =	outputs.hidden_states

       __UpperCamelCase      =	len(self.model_tester.depths				) + 1
       self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)

       __UpperCamelCase      =	(self.model_tester.image_size, self.model_tester.image_size)
       __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
       for _ in range(4				):
        __UpperCamelCase      =	floor(
            (
                (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1				)
        __UpperCamelCase      =	floor(
            (
                (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1				)
       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)     ,					[
               height * width,
               self.model_tester.hidden_sizes[0],
           ]     ,					)

      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	True
       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __UpperCamelCase      =	True

       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




      __UpperCamelCase      =	super()._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

      if return_labels:
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        del inputs_dict["labels"]

      return inputs_dict





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if not self.model_tester.is_training:
       return

      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase      =	True

      for model_class in self.all_model_classes:
       # LevitForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(__UpperCAmelCase				)
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
       return

      __UpperCamelCase      =	False
      __UpperCamelCase      =	True

      for model_class in self.all_model_classes:
       if model_class in get_values(__UpperCAmelCase				) or not model_class.supports_gradient_checkpointing:
        continue
       # LevitForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.gradient_checkpointing_enable()
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      __UpperCamelCase      =	[
          {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
          {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
          {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
      ]

      for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(__UpperCAmelCase				),
           ]
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue

       for problem_type in problem_types:
        with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'				):
         __UpperCamelCase      =	problem_type['title']
         __UpperCamelCase      =	problem_type['num_labels']

         __UpperCamelCase      =	model_class(__UpperCAmelCase				)
         model.to(__UpperCAmelCase				)
         model.train()

         __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

         if problem_type["num_labels"] > 1:
          __UpperCamelCase      =	inputs['labels'].unsqueeze(1				).repeat(1     ,					problem_type['num_labels']				)

         __UpperCamelCase      =	inputs['labels'].to(problem_type['dtype']				)

         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
         # they have the same size." which is a symptom something in wrong for the regression problem.
         # See https://github.com/huggingface/transformers/issues/11780
         with warnings.catch_warnings(record=__UpperCAmelCase				) as warning_list:
          __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
         for w in warning_list:
          if "Using a target size that is different to the input size" in str(w.message				):
           raise ValueError(
               F'Something is going wrong in the regression problem: intercepted {w.message}'				)

         loss.backward()







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	LevitModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image





@require_torch
@require_vision
class __lowerCAmelCase		(				unittest.TestCase      ):





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
          __UpperCAmelCase				)

      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=__UpperCAmelCase     ,					return_tensors='pt'				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

      # verify the logits
      __UpperCamelCase      =	torch.Size((1, 1000)				)
      self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]				).to(__UpperCAmelCase				)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					__UpperCAmelCase     ,					atol=1E-4				)				)



