
'''simple docstring'''






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor




class     __snake_case		:
					def __init__(     self,    A,    A=13,    A=30,    A=2,    A=3,    A=True,    A=True,    A=32,    A=5,    A=4,    A=37,    A="gelu",    A=0.1,    A=0.1,    A=10,    A=0.02,    A=3,    A=None,    A=2,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= parent
									lowerCamelCase					:			Dict									= batch_size
									lowerCamelCase					:			Optional[Any]									= image_size
									lowerCamelCase					:			List[str]									= patch_size
									lowerCamelCase					:			List[Any]									= num_channels
									lowerCamelCase					:			Optional[int]									= is_training
									lowerCamelCase					:			Optional[Any]									= use_labels
									lowerCamelCase					:			int									= hidden_size
									lowerCamelCase					:			Any									= num_hidden_layers
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Optional[Any]									= intermediate_size
									lowerCamelCase					:			Tuple									= hidden_act
									lowerCamelCase					:			List[Any]									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			int									= type_sequence_label_size
									lowerCamelCase					:			Dict									= initializer_range
									lowerCamelCase					:			str									= scope
									lowerCamelCase					:			List[Any]									= encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									lowerCamelCase					:			Dict									= (image_size // patch_size) ** 2
									lowerCamelCase					:			Dict									= num_patches + 2
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									lowerCamelCase					:			List[Any]									= None
									if self.use_labels:
													lowerCamelCase					:			Tuple									= ids_tensor([self.batch_size],    self.type_sequence_label_size      )

									lowerCamelCase					:			Union[str, Any]									= self.get_config()

									return config, pixel_values, labels
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return DeiTConfig(
									    image_size=self.image_size,    patch_size=self.patch_size,    num_channels=self.num_channels,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    intermediate_size=self.intermediate_size,    hidden_act=self.hidden_act,    hidden_dropout_prob=self.hidden_dropout_prob,    attention_probs_dropout_prob=self.attention_probs_dropout_prob,    is_decoder=A,    initializer_range=self.initializer_range,    encoder_stride=self.encoder_stride,    )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DeiTModel(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			Optional[int]									= model(A      )
									self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= DeiTForMaskedImageModeling(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			Dict									= model(A      )
									self.parent.assertEqual(
									    result.reconstruction.shape,    (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

									# test greyscale images
									lowerCamelCase					:			int									= 1
									lowerCamelCase					:			List[str]									= DeiTForMaskedImageModeling(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			Union[str, Any]									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									lowerCamelCase					:			Any									= model(A      )
									self.parent.assertEqual(result.reconstruction.shape,    (self.batch_size, 1, self.image_size, self.image_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.type_sequence_label_size
									lowerCamelCase					:			Optional[int]									= DeiTForImageClassification(A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			Tuple									= model(A,    labels=A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									lowerCamelCase					:			Optional[int]									= 1
									lowerCamelCase					:			Optional[int]									= DeiTForImageClassification(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			Tuple									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									lowerCamelCase					:			Optional[int]									= model(A,    labels=A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.prepare_config_and_inputs()
									(
									    (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, 
									)					:			str									= config_and_inputs
									lowerCamelCase					:			List[str]									= {'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(
					    (
					        DeiTModel,
					        DeiTForImageClassification,
					        DeiTForImageClassificationWithTeacher,
					        DeiTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					_lowerCAmelCase			     =	(
					    {
					        '''feature-extraction''': DeiTModel,
					        '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
					    }
					    if is_torch_available()
					    else {}
					)

					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DeiTModelTester(self      )
									lowerCamelCase					:			Union[str, Any]									= ConfigTester(self,    config_class=A,    has_text_modality=A,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					@unittest.skip(reason='DeiT does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			Optional[int]									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[Any]									= model_class(A      )
													self.assertIsInstance(model.get_input_embeddings(),    (nn.Module)      )
													lowerCamelCase					:			Dict									= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(A,    nn.Linear      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			Any									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[Any]									= model_class(A      )
													lowerCamelCase					:			str									= inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase					:			Dict									= [*signature.parameters.keys()]

													lowerCamelCase					:			List[str]									= ['pixel_values']
													self.assertListEqual(arg_names[:1],    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= super()._prepare_for_class(A,    A,    return_labels=A      )

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

									return inputs_dict
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.model_tester.is_training:
													return

									lowerCamelCase			, lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase					:			List[str]									= True

									for model_class in self.all_model_classes:
													# DeiTForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(A      )
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																	continue
													lowerCamelCase					:			int									= model_class(A      )
													model.to(A      )
													model.train()
													lowerCamelCase					:			Optional[int]									= self._prepare_for_class(A,    A,    return_labels=A      )
													lowerCamelCase					:			str									= model(**A      ).loss
													loss.backward()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
													return

									lowerCamelCase					:			Optional[Any]									= False
									lowerCamelCase					:			str									= True

									for model_class in self.all_model_classes:
													if model_class in get_values(A      ) or not model_class.supports_gradient_checkpointing:
																	continue
													# DeiTForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																	continue
													lowerCamelCase					:			Optional[int]									= model_class(A      )
													model.gradient_checkpointing_enable()
													model.to(A      )
													model.train()
													lowerCamelCase					:			Any									= self._prepare_for_class(A,    A,    return_labels=A      )
													lowerCamelCase					:			Tuple									= model(**A      ).loss
													loss.backward()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase			, lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs_for_common()

									lowerCamelCase					:			Optional[int]									= [
									    {'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(A      ),
													        *get_values(A      ),
													    ]
													    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
													):
																	continue

													for problem_type in problem_types:
																	with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''      ):
																					lowerCamelCase					:			List[str]									= problem_type['title']
																					lowerCamelCase					:			Tuple									= problem_type['num_labels']

																					lowerCamelCase					:			Tuple									= model_class(A      )
																					model.to(A      )
																					model.train()

																					lowerCamelCase					:			List[str]									= self._prepare_for_class(A,    A,    return_labels=A      )

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

																					lowerCamelCase					:			str									= 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=A      ) as warning_list:
																									lowerCamelCase					:			List[str]									= model(**A      ).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 DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			int									= DeiTModel.from_pretrained(A      )
													self.assertIsNotNone(A      )





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			int									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
				return image




@require_torch
@require_vision
class     __snake_case		(   unittest.TestCase):
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return (
									    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'      )
									    if is_vision_available()
									    else None
									)
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'      ).to(
									    A      )

									lowerCamelCase					:			Dict									= self.default_image_processor
									lowerCamelCase					:			Tuple									= prepare_img()
									lowerCamelCase					:			Optional[Any]									= image_processor(images=A,    return_tensors='pt'      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			Optional[int]									= model(**A      )

									# verify the logits
									lowerCamelCase					:			Optional[int]									= torch.Size((1, 1000)      )
									self.assertEqual(outputs.logits.shape,    A      )

									lowerCamelCase					:			Union[str, Any]									= torch.tensor([-1.0266, 0.1912, -1.2861]      ).to(A      )

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




					@slow
					@require_accelerate
					@require_torch_gpu
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= DeiTModel.from_pretrained(
									    'facebook/deit-base-distilled-patch16-224',    torch_dtype=torch.floataa,    device_map='auto'      )
									lowerCamelCase					:			str									= self.default_image_processor

									lowerCamelCase					:			Union[str, Any]									= prepare_img()
									lowerCamelCase					:			Optional[int]									= image_processor(images=A,    return_tensors='pt'      )
									lowerCamelCase					:			str									= inputs.pixel_values.to(A      )

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
													lowerCamelCase					:			int									= model(A      )


'''simple docstring'''






from __future__ import annotations

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

A			      =      (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A			      =      [ord(letter) for letter in string.ascii_lowercase]
A			      =      {ord(char) for char in VALID_CHARS}

A			      =      ["the", "be", "to", "of", "and", "in", "that", "have"]





def    UpperCAmelCase							(  UpperCAmelCase__							:							list[int]       ,							UpperCAmelCase__							:							tuple[int, ...]):
				lowerCamelCase					:			str									= ""
				lowerCamelCase					:			int
				lowerCamelCase					:			int
				lowerCamelCase					:			int

				for keychar, cipherchar in zip(cycle(UpperCAmelCase__)       ,							UpperCAmelCase__):
								lowerCamelCase					:			Optional[Any]									= cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(UpperCAmelCase__)

				return decoded





def    UpperCAmelCase							(  UpperCAmelCase__							:							list[int]):
				lowerCamelCase					:			list[str]									= []
				for key in product(UpperCAmelCase__       ,							repeat=3):
								lowerCamelCase					:			Dict									= try_key(UpperCAmelCase__       ,							UpperCAmelCase__)
								if encoded is not None:
												possibles.append(UpperCAmelCase__)
				return possibles





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





def    UpperCAmelCase							(  UpperCAmelCase__							:							str = "p059_cipher.txt"):
				lowerCamelCase					:			list[int]
				lowerCamelCase					:			list[str]
				lowerCamelCase					:			str
				lowerCamelCase					:			str
				lowerCamelCase					:			str									= Path(UpperCAmelCase__).parent.joinpath(UpperCAmelCase__).read_text(encoding='utf-8')

				lowerCamelCase					:			Tuple									= [int(UpperCAmelCase__) for number in data.strip().split(',')]

				lowerCamelCase					:			str									= filter_valid_chars(UpperCAmelCase__)
				for common_word in COMMON_WORDS:
								lowerCamelCase					:			List[str]									= filter_common_word(UpperCAmelCase__       ,							UpperCAmelCase__)
								if len(UpperCAmelCase__) == 1:
												break

				lowerCamelCase					:			Dict									= possibles[0]
				return sum(ord(UpperCAmelCase__) for char in decoded_text)


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