



'''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 _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Union[str, Any]=3  ,       _lowerCAmelCase  :  List[Any]=3_2  ,       _lowerCAmelCase  :  str=3  ,       _lowerCAmelCase  :  List[Any]=1_0  ,       _lowerCAmelCase  :  Tuple=[8, 1_6, 3_2, 6_4]  ,       _lowerCAmelCase  :  List[Any]=[1, 1, 2, 1]  ,       _lowerCAmelCase  :  Any=True  ,       _lowerCAmelCase  :  Optional[int]=True  ,       _lowerCAmelCase  :  Tuple="relu"  ,       _lowerCAmelCase  :  Tuple=3  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  str=["stage2", "stage3", "stage4"]  ,       _lowerCAmelCase  :  Any=[2, 3, 4]  ,       _lowerCAmelCase  :  int=1  ,       ):







											'''simple docstring'''

											__lowercase       	=parent
											__lowercase       	=batch_size
											__lowercase       	=image_size
											__lowercase       	=num_channels
											__lowercase       	=embeddings_size
											__lowercase       	=hidden_sizes
											__lowercase       	=depths
											__lowercase       	=is_training
											__lowercase       	=use_labels
											__lowercase       	=hidden_act
											__lowercase       	=num_labels
											__lowercase       	=scope
											__lowercase       	=len(_lowerCAmelCase)
											__lowercase       	=out_features
											__lowercase       	=out_indices
											__lowercase       	=num_groups

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

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

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

											__lowercase       	=self.get_config()

											return config, pixel_values, labels

				def 				__lowerCamelCase     (							self  :  Optional[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  :  Union[str, Any]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											__lowercase       	=BitModel(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=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 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.num_labels
											__lowercase       	=BitForImageClassification(_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=model(_lowerCAmelCase  ,       labels=_lowerCAmelCase)
											self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.num_labels))

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  int):







											'''simple docstring'''

											__lowercase       	=BitBackbone(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=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
											__lowercase       	=None
											__lowercase       	=BitBackbone(config=_lowerCAmelCase)
											model.to(_lowerCAmelCase)
											model.eval()
											__lowercase       	=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 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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





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





				'''simple docstring'''





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

				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False
				lowerCAmelCase__					    =    False

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=BitModelTester(self)
											__lowercase       	=ConfigTester(self  ,       config_class=_lowerCAmelCase  ,       has_text_modality=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''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  :  Union[str, Any]):







											'''simple docstring'''

											return

				@unittest.skip(reason='Bit does not output attentions')
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											pass

				@unittest.skip(reason='Bit does not use inputs_embeds')
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											pass

				@unittest.skip(reason='Bit does not support input and output embeddings')
				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowercase       	=['pixel_values']
																		self.assertListEqual(arg_names[:1]  ,       _lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase	,				__lowercase       	=self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		__lowercase       	=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 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											def check_hidden_states_output(_lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  List[Any]):
																		__lowercase       	=model_class(_lowerCAmelCase)
																		model.to(_lowerCAmelCase)
																		model.eval()

																		with torch.no_grad():
																									__lowercase       	=model(**self._prepare_for_class(_lowerCAmelCase  ,       _lowerCAmelCase))

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

																		__lowercase       	=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]  ,       )

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

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

																									check_hidden_states_output(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

				@unittest.skip(reason='Bit does not use feedforward chunking')
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase)

				@slow
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowercase       	=BitModel.from_pretrained(_lowerCAmelCase)
																		self.assertIsNotNone(_lowerCAmelCase)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image





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





				'''simple docstring'''





				@cached_property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

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

				@slow
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_lowerCAmelCase)

											__lowercase       	=self.default_image_processor
											__lowercase       	=prepare_img()
											__lowercase       	=image_processor(images=_lowerCAmelCase  ,       return_tensors='pt').to(_lowerCAmelCase)

											# forward pass
											with torch.no_grad():
																		__lowercase       	=model(**_lowerCAmelCase)

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

											__lowercase       	=torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(_lowerCAmelCase)

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





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





				'''simple docstring'''





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

				lowerCAmelCase__					    =    False

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=BitModelTester(self)








'''simple docstring'''



from __future__ import annotations



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = False  ,   ):



							"""simple docstring"""



							__lowercase       	=cipher_alphabet or [chr(_lowerCAmelCase					) 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)
														__lowercase       	={
														    'a': 0.0_84_97,
														    'b': 0.0_14_92,
														    'c': 0.0_22_02,
														    'd': 0.0_42_53,
														    'e': 0.1_11_62,
														    'f': 0.0_22_28,
														    'g': 0.0_20_15,
														    'h': 0.0_60_94,
														    'i': 0.0_75_46,
														    'j': 0.0_01_53,
														    'k': 0.0_12_92,
														    'l': 0.0_40_25,
														    'm': 0.0_24_06,
														    'n': 0.0_67_49,
														    'o': 0.0_75_07,
														    'p': 0.0_19_29,
														    'q': 0.0_00_95,
														    'r': 0.0_75_87,
														    's': 0.0_63_27,
														    't': 0.0_93_56,
														    'u': 0.0_27_58,
														    'v': 0.0_09_78,
														    'w': 0.0_25_60,
														    'x': 0.0_01_50,
														    'y': 0.0_19_94,
														    'z': 0.0_00_77,
														}
							else:
														# Custom frequencies dictionary
														__lowercase       	=frequencies_dict

							if not case_sensitive:
														__lowercase       	=ciphertext.lower()

							# Chi squared statistic values
							__lowercase       	={}

							# cycle through all of the shifts
							for shift in range(len(_lowerCAmelCase					)					):
														__lowercase       	=''

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												__lowercase       	=(alphabet_letters.index(letter.lower()					) - shift) % len(
																												    _lowerCAmelCase					)
																												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

														__lowercase       	=0.0

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

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

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((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
																																			__lowercase       	=decrypted_with_shift.count(_lowerCAmelCase					)

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

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((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
														__lowercase       	=(
														    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(_lowerCAmelCase					) -> tuple[float, str]:
														return chi_squared_statistic_values[key]

							__lowercase       	=min(
							    _lowerCAmelCase  ,   key=_lowerCAmelCase  ,   )

							# Get all the data from the most likely cipher (key, decoded message)
							(
							    (
							    __lowercase
							)	,				(
							    __lowercase
							)	,				
							)       	=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,
							)



