





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image



class 				_UpperCAmelCase    :

  """simple docstring"""


  def __init__(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Any=3					,  lowerCAmelCase_ :		str=3_2					,  lowerCAmelCase_ :		Optional[int]=3					,  lowerCAmelCase_ :		str=1_0					,  lowerCAmelCase_ :		Any=[8, 1_6, 3_2, 6_4]					,  lowerCAmelCase_ :		Union[str, Any]=[1, 1, 2, 1]					,  lowerCAmelCase_ :		Optional[Any]=True					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any="relu"					,  lowerCAmelCase_ :		List[str]=3					,  lowerCAmelCase_ :		Any=None					,  lowerCAmelCase_ :		Optional[Any]=["stage2", "stage3", "stage4"]					,  lowerCAmelCase_ :		List[str]=[2, 3, 4]					,  lowerCAmelCase_ :		List[Any]=1					,  )							->						Union[str, Any]:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	embeddings_size
     __lowerCAmelCase								=	hidden_sizes
     __lowerCAmelCase								=	depths
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	num_labels
     __lowerCAmelCase								=	scope
     __lowerCAmelCase								=	len(lowerCAmelCase_     )
     __lowerCAmelCase								=	out_features
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	num_groups






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

     __lowerCAmelCase								=	self.get_config()

     return config, pixel_values, labels






  def 				lowercase		(				self :		Union[str, Any]     )							->						Any:
     return BitConfig(
         num_channels=self.num_channels					,  embeddings_size=self.embeddings_size					,  hidden_sizes=self.hidden_sizes					,  depths=self.depths					,  hidden_act=self.hidden_act					,  num_labels=self.num_labels					,  out_features=self.out_features					,  out_indices=self.out_indices					,  num_groups=self.num_groups					,  )






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Union[str, Any]     )							->						int:
     __lowerCAmelCase								=	BitModel(config=lowerCAmelCase_     )
     model.to(lowerCAmelCase_     )
     model.eval()
     __lowerCAmelCase								=	model(lowerCAmelCase_     )
     self.parent.assertEqual(
         result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,  )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	self.num_labels
     __lowerCAmelCase								=	BitForImageClassification(lowerCAmelCase_     )
     model.to(lowerCAmelCase_     )
     model.eval()
     __lowerCAmelCase								=	model(lowerCAmelCase_					,  labels=lowerCAmelCase_     )
     self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)     )






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		List[Any]     )							->						Dict:
     __lowerCAmelCase								=	BitBackbone(config=lowerCAmelCase_     )
     model.to(lowerCAmelCase_     )
     model.eval()
     __lowerCAmelCase								=	model(lowerCAmelCase_     )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps     )					,  len(config.out_features     )     )
     self.parent.assertListEqual(list(result.feature_maps[0].shape     )					,  [self.batch_size, self.hidden_sizes[1], 4, 4]     )

     # verify channels
     self.parent.assertEqual(len(model.channels     )					,  len(config.out_features     )     )
     self.parent.assertListEqual(model.channels					,  config.hidden_sizes[1:]     )

     # verify backbone works with out_features=None
     __lowerCAmelCase								=	None
     __lowerCAmelCase								=	BitBackbone(config=lowerCAmelCase_     )
     model.to(lowerCAmelCase_     )
     model.eval()
     __lowerCAmelCase								=	model(lowerCAmelCase_     )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps     )					,  1     )
     self.parent.assertListEqual(list(result.feature_maps[0].shape     )					,  [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

     # verify channels
     self.parent.assertEqual(len(model.channels     )					,  1     )
     self.parent.assertListEqual(model.channels					,  [config.hidden_sizes[-1]]     )






  def 				lowercase		(				self :		List[Any]     )							->						str:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	config_and_inputs
     __lowerCAmelCase								=	{'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a_        = (
      {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a_        = False
  a_        = False
  a_        = False
  a_        = False
  a_        = False






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     __lowerCAmelCase								=	BitModelTester(self     )
     __lowerCAmelCase								=	ConfigTester(self					,  config_class=lowerCAmelCase_					,  has_text_modality=lowerCAmelCase_     )






  def 				lowercase		(				self :		List[str]     )							->						Dict:
     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 				lowercase		(				self :		List[str]     )							->						Optional[int]:
     return






  @unittest.skip(reason='Bit does not output attentions'     )
  def 				lowercase		(				self :		Tuple     )							->						Optional[int]:
     pass






  @unittest.skip(reason='Bit does not use inputs_embeds'     )
  def 				lowercase		(				self :		str     )							->						Optional[int]:
     pass






  @unittest.skip(reason='Bit does not support input and output embeddings'     )
  def 				lowercase		(				self :		int     )							->						Dict:
     pass






  def 				lowercase		(				self :		Any     )							->						Any:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCAmelCase								=	['pixel_values']
        self.assertListEqual(arg_names[:1]					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						List[Any]:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*lowerCAmelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Tuple:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase								=	model_class(config=lowerCAmelCase_     )
        for name, module in model.named_modules():
           if isinstance(lowerCAmelCase_					,  (nn.BatchNormad, nn.GroupNorm)     ):
              self.assertTrue(
                  torch.all(module.weight == 1     )					,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,  )
              self.assertTrue(
                  torch.all(module.bias == 0     )					,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,  )






  def 				lowercase		(				self :		Optional[int]     )							->						int:
     def check_hidden_states_output(lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Any     ):
        __lowerCAmelCase								=	model_class(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase								=	model(**self._prepare_for_class(lowerCAmelCase_					,  lowerCAmelCase_     )     )

        __lowerCAmelCase								=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        __lowerCAmelCase								=	self.model_tester.num_stages
        self.assertEqual(len(lowerCAmelCase_     )					,  expected_num_stages + 1     )

        # Bit's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]     )					,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,  )

     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase								=	['preactivation', 'bottleneck']
     for model_class in self.all_model_classes:
        for layer_type in layers_type:
           __lowerCAmelCase								=	layer_type
           __lowerCAmelCase								=	True
           check_hidden_states_output(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )

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

           check_hidden_states_output(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )






  @unittest.skip(reason='Bit does not use feedforward chunking'     )
  def 				lowercase		(				self :		List[str]     )							->						Dict:
     pass






  def 				lowercase		(				self :		Optional[Any]     )							->						List[str]:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_     )






  @slow
  def 				lowercase		(				self :		List[str]     )							->						Dict:
     for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase								=	BitModel.from_pretrained(lowerCAmelCase_     )
        self.assertIsNotNone(lowerCAmelCase_     )





def   a_    ( ):
   __lowerCAmelCase								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



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

  """simple docstring"""


  @cached_property
  def 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     return (
         BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ) if is_vision_available() else None
     )






  @slow
  def 				lowercase		(				self :		Optional[Any]     )							->						List[Any]:
     __lowerCAmelCase								=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCAmelCase_     )

     __lowerCAmelCase								=	self.default_image_processor
     __lowerCAmelCase								=	prepare_img()
     __lowerCAmelCase								=	image_processor(images=lowerCAmelCase_					,  return_tensors='pt'     ).to(lowerCAmelCase_     )

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase								=	model(**lowerCAmelCase_     )

     # verify the logits
     __lowerCAmelCase								=	torch.Size((1, 1_0_0_0)     )
     self.assertEqual(outputs.logits.shape					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]     ).to(lowerCAmelCase_     )

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





@require_torch
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (BitBackbone,) if is_torch_available() else ()
  a_        = BitConfig

  a_        = False






  def 				lowercase		(				self :		Tuple     )							->						Optional[int]:
     __lowerCAmelCase								=	BitModelTester(self     )








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_snake_case  :    str						    =  (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case  :    list[int]						    =  [ord(letter) for letter in string.ascii_lowercase]
_snake_case  :    set[int]						    =  {ord(char) for char in VALID_CHARS}

_snake_case  :    list[str]						    =  ["the", "be", "to", "of", "and", "in", "that", "have"]

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

   for keychar, cipherchar in zip(cycle(lowerCAmelCase_	),	lowerCAmelCase_	):
      __lowerCAmelCase								=	cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(lowerCAmelCase_	)

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


if __name__ == "__main__":
     print(F"""{solution() = }""")

