


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__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.config_class(**self.inputs_dict     )
						self.parent.assertTrue(hasattr(snake_case      ,						"hidden_sizes"     )     )
						self.parent.assertTrue(hasattr(snake_case      ,						"num_attention_heads"     )     )







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=64      ,						snake_case=3      ,						snake_case=3      ,						snake_case=2      ,						snake_case=1      ,						snake_case=16      ,						snake_case=[128, 256, 384]      ,						snake_case=[4, 6, 8]      ,						snake_case=[2, 3, 4]      ,						snake_case=[16, 16, 16]      ,						snake_case=0      ,						snake_case=[2, 2, 2]      ,						snake_case=[2, 2, 2]      ,						snake_case=0.02      ,						snake_case=True      ,						snake_case=True      ,						snake_case=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 				snake_case__				(							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 				snake_case__				(							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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       LevitModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


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







			def 				snake_case__				(							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__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Dict    =		(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase		:					Tuple    =		(
			    {
			        'feature-extraction': LevitModel,
			        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					Dict    =		False
			_UpperCamelCase		:					Union[str, Any]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					Dict    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       LevitModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							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 				snake_case__				(							self     ):

						'''simple docstring'''


						return




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

						'''simple docstring'''


						pass




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

						'''simple docstring'''


						pass




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

						'''simple docstring'''


						pass




			def 				snake_case__				(							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(snake_case     )
									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]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states

									UpperCamelCase__					=       len(self.model_tester.depths     ) + 1
									self.assertEqual(len(snake_case     )      ,						snake_case     )

									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(snake_case      ,						snake_case      ,						snake_case     )

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

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )




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

						'''simple docstring'''


						pass




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       super()._prepare_for_class(snake_case      ,						snake_case      ,						return_labels=snake_case     )

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

						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case     )




			def 				snake_case__				(							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(snake_case     )
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
												continue
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.train()
									UpperCamelCase__					=       self._prepare_for_class(snake_case      ,						snake_case      ,						return_labels=snake_case     )
									UpperCamelCase__					=       model(**snake_case     ).loss
									loss.backward()




			def 				snake_case__				(							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(snake_case     ) or not model_class.supports_gradient_checkpointing:
												continue
									# LevitForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "LevitForImageClassificationWithTeacher":
												continue
									UpperCamelCase__					=       model_class(snake_case     )
									model.gradient_checkpointing_enable()
									model.to(snake_case     )
									model.train()
									UpperCamelCase__					=       self._prepare_for_class(snake_case      ,						snake_case      ,						return_labels=snake_case     )
									UpperCamelCase__					=       model(**snake_case     ).loss
									loss.backward()




			def 				snake_case__				(							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(snake_case     ),
									    ]
									    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(snake_case     )
															model.to(snake_case     )
															model.train()

															UpperCamelCase__					=       self._prepare_for_class(snake_case      ,						snake_case      ,						return_labels=snake_case     )

															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=snake_case     ) as warning_list:
																		UpperCamelCase__					=       model(**snake_case     ).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 				snake_case__				(							self     ):

						'''simple docstring'''


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



def        UpperCamelCase_(				)->		Tuple:
			UpperCamelCase__					=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
						    snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_img()
						UpperCamelCase__					=       image_processor(images=snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

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

						UpperCamelCase__					=       torch.tensor([1.0448, -0.3745, -1.8317]     ).to(snake_case     )

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






def        UpperCamelCase_(				_A   :str  )->		str:
			UpperCamelCase__					=       ""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups
def        UpperCamelCase_(				_A   :str  )->		dict[str, str]:
			UpperCamelCase__					=       [chr(i + 65  ) for i in range(26  )]
			# Remove duplicate characters from key
			UpperCamelCase__					=       remove_duplicates(key.upper()  )
			UpperCamelCase__					=       len(_A  )
			# First fill cipher with key characters
			UpperCamelCase__					=       {alphabet[i]: char for i, char in enumerate(_A  )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(_A  )				,     26  ):
						UpperCamelCase__					=       alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									UpperCamelCase__					=       alphabet[i - offset]
						UpperCamelCase__					=       char
			return cipher_alphabet
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			return "".join(cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			UpperCamelCase__					=       {v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				)->		None:
			UpperCamelCase__					=       input("Enter message to encode or decode: "  ).strip()
			UpperCamelCase__					=       input("Enter keyword: "  ).strip()
			UpperCamelCase__					=       input("Encipher or decipher? E/D:"  ).strip()[0].lower()
			try:
						UpperCamelCase__					=       {"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"  )
			UpperCamelCase__					=       create_cipher_map(_A  )
			print(func(_A				,     _A  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()


