






'''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 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=3	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=32	,    __SCREAMING_SNAKE_CASE   :      Any=3	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=10	,    __SCREAMING_SNAKE_CASE   :      Any=[8, 16, 32, 64]	,    __SCREAMING_SNAKE_CASE   :      str=[1, 1, 2, 1]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=True	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=True	,    __SCREAMING_SNAKE_CASE   :      List[str]="relu"	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=3	,    __SCREAMING_SNAKE_CASE   :      List[Any]=None	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=["stage2", "stage3", "stage4"]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=[2, 3, 4]	,    __SCREAMING_SNAKE_CASE   :      str=1	,    )      ->      int:
							a_		:	List[str]									=   parent
							a_		:	Dict									=   batch_size
							a_		:	Dict									=   image_size
							a_		:	Tuple									=   num_channels
							a_		:	Any									=   embeddings_size
							a_		:	Optional[int]									=   hidden_sizes
							a_		:	int									=   depths
							a_		:	str									=   is_training
							a_		:	Union[str, Any]									=   use_labels
							a_		:	Optional[Any]									=   hidden_act
							a_		:	Union[str, Any]									=   num_labels
							a_		:	int									=   scope
							a_		:	List[Any]									=   len(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   out_features
							a_		:	Tuple									=   out_indices
							a_		:	List[str]									=   num_groups






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Tuple:
							a_		:	Dict									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							a_		:	List[Any]									=   None
							if self.use_labels:
											a_		:	List[str]									=   ids_tensor([self.batch_size]	,    self.num_labels       )

							a_		:	str									=   self.get_config()

							return config, pixel_values, labels






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[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       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      List[str]:
							a_		:	Optional[Any]									=   BitModel(config=__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	Optional[int]									=   model(__SCREAMING_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       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      List[Any]:
							a_		:	Dict									=   self.num_labels
							a_		:	int									=   BitForImageClassification(__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	int									=   model(__SCREAMING_SNAKE_CASE	,    labels=__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      int:
							a_		:	int									=   BitBackbone(config=__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	Dict									=   model(__SCREAMING_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_		:	Tuple									=   None
							a_		:	Dict									=   BitBackbone(config=__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	int									=   model(__SCREAMING_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       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Dict:
							a_		:	Union[str, Any]									=   self.prepare_config_and_inputs()
							a_					,      a_					,      a_		:	List[str]									=   config_and_inputs
							a_		:	List[str]									=   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Dict									=   BitModelTester(self       )
							a_		:	int									=   ConfigTester(self	,    config_class=__SCREAMING_SNAKE_CASE	,    has_text_modality=__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      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       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Tuple:
							return






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






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






			@unittest.skip(reason='''Bit does not support input and output embeddings'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[int]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      List[str]:
							a_					,      a_		:	Optional[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:	Union[str, Any]									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	Dict									=   inspect.signature(model.forward       )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_		:	Tuple									=   [*signature.parameters.keys()]

											a_		:	int									=   ['''pixel_values''']
											self.assertListEqual(arg_names[:1]	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Tuple:
							a_		:	Tuple									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Optional[int]:
							a_		:	int									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							a_					,      a_		:	Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:	int									=   model_class(config=__SCREAMING_SNAKE_CASE       )
											for name, module in model.named_modules():
															if isinstance(__SCREAMING_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       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							def check_hidden_states_output(__SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Any       ):
											a_		:	Optional[int]									=   model_class(__SCREAMING_SNAKE_CASE       )
											model.to(__SCREAMING_SNAKE_CASE       )
											model.eval()

											with torch.no_grad():
															a_		:	Optional[Any]									=   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )

											a_		:	Optional[int]									=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											a_		:	List[Any]									=   self.model_tester.num_stages
											self.assertEqual(len(__SCREAMING_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_		:	int									=   self.model_tester.prepare_config_and_inputs_for_common()
							a_		:	Tuple									=   ['''preactivation''', '''bottleneck''']
							for model_class in self.all_model_classes:
											for layer_type in layers_type:
															a_		:	Optional[int]									=   layer_type
															a_		:	Optional[int]									=   True
															check_hidden_states_output(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															a_		:	Optional[int]									=   True

															check_hidden_states_output(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			@unittest.skip(reason='''Bit does not use feedforward chunking'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Any:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							a_		:	str									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )







			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_		:	Tuple									=   BitModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image





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






			@cached_property
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Any:
							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ) if is_vision_available() else None
							)







			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							a_		:	List[str]									=   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(__SCREAMING_SNAKE_CASE       )

							a_		:	Any									=   self.default_image_processor
							a_		:	List[str]									=   prepare_img()
							a_		:	Dict									=   image_processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).to(__SCREAMING_SNAKE_CASE       )

							# forward pass
							with torch.no_grad():
											a_		:	Any									=   model(**__SCREAMING_SNAKE_CASE       )

							# verify the logits
							a_		:	Union[str, Any]									=   torch.Size((1, 1000)       )
							self.assertEqual(outputs.logits.shape	,    __SCREAMING_SNAKE_CASE       )

							a_		:	List[Any]									=   torch.tensor([[-0.6526, -0.5263, -1.4398]]       ).to(__SCREAMING_SNAKE_CASE       )

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





@require_torch
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					(BitBackbone,) if is_torch_available() else ()
			snake_case__	=					BitConfig

			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Dict:
							a_		:	Any									=   BitModelTester(self       )












'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

				return [s[i:] + s[:i] for i in range(len(__A   )   )]





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				a_		:	BWTTransformDict									=   {
				    "bwt_string": "".join([word[-1] for word in rotations]   ),
				    "idx_original_string": rotations.index(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter bwt_string type must be str.'''   )
				if not bwt_string:
								raise ValueError('''The parameter bwt_string must not be empty.'''   )
				try:
								a_		:	Union[str, Any]									=   int(__A   )
				except ValueError:
								raise TypeError(
								    '''The parameter idx_original_string type must be int or passive'''
								    ''' of cast to int.'''   )
				if idx_original_string < 0:
								raise ValueError('''The parameter idx_original_string must not be lower than 0.'''   )
				if idx_original_string >= len(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		__lowerCAmelCase									=   'Provide a string that I will generate its BWT transform: '
		__lowerCAmelCase									=   input(entry_msg).strip()
		__lowerCAmelCase									=   bwt_transform(s)
		print(
		    F"""Burrows Wheeler transform for string '{s}' results """
		    F"""in '{result['bwt_string']}'"""
		)
		__lowerCAmelCase									=   reverse_bwt(result['bwt_string'], result['idx_original_string'])
		print(
		    F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
		    F"""we get original string '{original_string}'"""
		)




