
"""simple docstring"""





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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					Any=3       ,   _snake_case						:					Optional[Any]=32       ,   _snake_case						:					str=3       ,   _snake_case						:					Dict=10       ,   _snake_case						:					Union[str, Any]=[8, 16, 32, 64]       ,   _snake_case						:					List[str]=[1, 1, 2, 1]       ,   _snake_case						:					Dict=True       ,   _snake_case						:					List[str]=True       ,   _snake_case						:					Optional[Any]="relu"       ,   _snake_case						:					Any=3       ,   _snake_case						:					Optional[int]=None       ,   _snake_case						:					int=["stage2", "stage3", "stage4"]       ,   _snake_case						:					Tuple=[2, 3, 4]       ,   _snake_case						:					List[Any]=1       ,   )      -> List[str]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						num_channels
								A_				 =						embeddings_size
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_act
								A_				 =						num_labels
								A_				 =						scope
								A_				 =						len(_snake_case	)
								A_				 =						out_features
								A_				 =						out_indices
								A_				 =						num_groups




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

								A_				 =						self.get_config()

								return config, pixel_values, labels




			def      lowerCamelCase__     (				self						:					str	)      -> List[Any]:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Any       ,   _snake_case						:					Optional[int]	)      -> int:







								"""simple docstring"""
								A_				 =						BitModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,   )




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					Any       ,   _snake_case						:					List[Any]       ,   _snake_case						:					str	)      -> Dict:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						BitForImageClassification(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					int       ,   _snake_case						:					List[str]       ,   _snake_case						:					Union[str, Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						BitBackbone(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)

								# 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
								A_				 =						None
								A_				 =						BitBackbone(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)

								# 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      lowerCamelCase__     (				self						:					Optional[int]	)      -> Dict:







								"""simple docstring"""
								A_				 =						self.prepare_config_and_inputs()
								A_ ,    A_ ,    A_				 =						config_and_inputs
								A_				 =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







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

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Dict	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						BitModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case	)




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""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      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[Any]:







								"""simple docstring"""
								return




			@unittest.skip(reason="Bit does not output attentions"	)
			def      lowerCamelCase__     (				self						:					str	)      -> Union[str, Any]:







								"""simple docstring"""
								pass




			@unittest.skip(reason="Bit does not use inputs_embeds"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[int]:







								"""simple docstring"""
								pass




			@unittest.skip(reason="Bit does not support input and output embeddings"	)
			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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

													A_				 =						["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> str:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_snake_case	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Union[str, Any]:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													for name, module in model.named_modules():
																		if isinstance(_snake_case       ,   (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      lowerCamelCase__     (				self						:					Tuple	)      -> Optional[Any]:







								"""simple docstring"""
								def check_hidden_states_output(_snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Dict	):
													A_				 =						model_class(_snake_case	)
													model.to(_snake_case	)
													model.eval()

													with torch.no_grad():
																		A_				 =						model(**self._prepare_for_class(_snake_case       ,   _snake_case	)	)

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

													A_				 =						self.model_tester.num_stages
													self.assertEqual(len(_snake_case	)       ,   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]       ,   )

								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						["preactivation", "bottleneck"]
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		A_				 =						layer_type
																		A_				 =						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"]
																		A_				 =						True

																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)




			@unittest.skip(reason="Bit does not use feedforward chunking"	)
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[str]:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Any	)      -> Dict:







								"""simple docstring"""
								for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_				 =						BitModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)





def 							A_     ():

					'''simple docstring'''





					A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					return image



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




			"""simple docstring"""







			@cached_property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								return (
								    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	) if is_vision_available() else None
								)






			@slow
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_snake_case	)

								A_				 =						self.default_image_processor
								A_				 =						prepare_img()
								A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

								# forward pass
								with torch.no_grad():
													A_				 =						model(**_snake_case	)

								# verify the logits
								A_				 =						torch.Size((1, 1_000)	)
								self.assertEqual(outputs.logits.shape       ,   _snake_case	)

								A_				 =						torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]	).to(_snake_case	)

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





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







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

			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					int	)      -> Tuple:







								"""simple docstring"""
								A_				 =						BitModelTester(self	)






"""simple docstring"""





from __future__ import annotations
def 							A_     (__a				,   __a = None				,   __a = None				,   __a = False				,   ):

					'''simple docstring'''





					A_				 =						cipher_alphabet or [chr(__a				) for i in range(97				,   123				)]

					# If the argument is None or the user provided an empty dictionary
					if not frequencies_dict:
										# Frequencies of letters in the english language (how much they show up)
										A_				 =						{
										    "a": 0.08497,
										    "b": 0.01492,
										    "c": 0.02202,
										    "d": 0.04253,
										    "e": 0.11162,
										    "f": 0.02228,
										    "g": 0.02015,
										    "h": 0.06094,
										    "i": 0.07546,
										    "j": 0.00153,
										    "k": 0.01292,
										    "l": 0.04025,
										    "m": 0.02406,
										    "n": 0.06749,
										    "o": 0.07507,
										    "p": 0.01929,
										    "q": 0.00095,
										    "r": 0.07587,
										    "s": 0.06327,
										    "t": 0.09356,
										    "u": 0.02758,
										    "v": 0.00978,
										    "w": 0.02560,
										    "x": 0.00150,
										    "y": 0.01994,
										    "z": 0.00077,
										}
					else:
										# Custom frequencies dictionary
										A_				 =						frequencies_dict

					if not case_sensitive:
										A_				 =						ciphertext.lower()

					# Chi squared statistic values
					A_				 =						{}

					# cycle through all of the shifts
					for shift in range(len(__a				)				):
										A_				 =						""

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				A_				 =						(alphabet_letters.index(letter.lower()				) - shift) % len(
																				    __a				)
																				decrypted_with_shift += (
																				    alphabet_letters[new_key].upper()
																				    if case_sensitive and letter.isupper()
																				    else alphabet_letters[new_key]
																				)
															except ValueError:
																				# Append the character if it isn't in the alphabet
																				decrypted_with_shift += letter

										A_				 =						0.0

										# Loop through each letter in the decoded message with the shift
										for letter in decrypted_with_shift:
															if case_sensitive:
																				A_				 =						letter.lower()
																				if letter in frequencies:
																									# Get the amount of times the letter occurs in the message
																									A_				 =						decrypted_with_shift.lower().count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value
															else:
																				if letter.lower() in frequencies:
																									# Get the amount of times the letter occurs in the message
																									A_				 =						decrypted_with_shift.count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
										A_				 =						(
										    chi_squared_statistic,
										    decrypted_with_shift,
										)

					# Get the most likely cipher by finding the cipher with the smallest chi squared
					# statistic
					def chi_squared_statistic_values_sorting_key(__a				) -> tuple[float, str]:
										return chi_squared_statistic_values[key]

					A_				 =						min(
					    __a				,   key=__a				,   )

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    A_
					) ,    (
					    A_
					) ,    
					)				 =						chi_squared_statistic_values[most_likely_cipher]

					# Return the data on the most likely shift
					return (
					    most_likely_cipher,
					    most_likely_cipher_chi_squared_value,
					    decoded_most_likely_cipher,
					)




