
"""simple docstring"""






import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_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 MaskFormerSwinBackbone
					from transformers.models.maskformer import MaskFormerSwinModel




class 				lowercase__:





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int=13		, lowerCamelCase_    :int=32		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :str=3		, lowerCamelCase_    :Any=16		, lowerCamelCase_    :Tuple=[1, 2, 1]		, lowerCamelCase_    :Dict=[2, 2, 4]		, lowerCamelCase_    :Dict=2		, lowerCamelCase_    :List[str]=2.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Union[str, Any]=0.0		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :List[str]=0.1		, lowerCamelCase_    :Optional[Any]="gelu"		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Union[str, Any]=0.0_2		, lowerCamelCase_    :List[str]=1E-5		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Tuple=None		, lowerCamelCase_    :int=True		, lowerCamelCase_    :Dict=10		, lowerCamelCase_    :List[Any]=8		, lowerCamelCase_    :Any=["stage1", "stage2", "stage3"]		, lowerCamelCase_    :str=[1, 2, 3]		, )							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= parent
			SCREAMING_SNAKE_CASE       :     List[str]				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= patch_size
			SCREAMING_SNAKE_CASE       :     Any				= num_channels
			SCREAMING_SNAKE_CASE       :     Dict				= embed_dim
			SCREAMING_SNAKE_CASE       :     Any				= depths
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_heads
			SCREAMING_SNAKE_CASE       :     Tuple				= window_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= mlp_ratio
			SCREAMING_SNAKE_CASE       :     Tuple				= qkv_bias
			SCREAMING_SNAKE_CASE       :     List[Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     List[str]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Tuple				= drop_path_rate
			SCREAMING_SNAKE_CASE       :     int				= hidden_act
			SCREAMING_SNAKE_CASE       :     Tuple				= use_absolute_embeddings
			SCREAMING_SNAKE_CASE       :     Any				= patch_norm
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     Tuple				= initializer_range
			SCREAMING_SNAKE_CASE       :     Tuple				= is_training
			SCREAMING_SNAKE_CASE       :     List[str]				= scope
			SCREAMING_SNAKE_CASE       :     Dict				= use_labels
			SCREAMING_SNAKE_CASE       :     List[Any]				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     str				= encoder_stride
			SCREAMING_SNAKE_CASE       :     Any				= out_features
			SCREAMING_SNAKE_CASE       :     Optional[int]				= out_indices





	def   __lowerCAmelCase (						self    :Tuple					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			SCREAMING_SNAKE_CASE       :     int				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)

			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_config()

			return config, pixel_values, labels





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			return MaskFormerSwinConfig(
			    image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, embed_dim=self.embed_dim		, depths=self.depths		, num_heads=self.num_heads		, window_size=self.window_size		, mlp_ratio=self.mlp_ratio		, qkv_bias=self.qkv_bias		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, drop_path_rate=self.drop_path_rate		, hidden_act=self.hidden_act		, use_absolute_embeddings=self.use_absolute_embeddings		, path_norm=self.patch_norm		, layer_norm_eps=self.layer_norm_eps		, initializer_range=self.initializer_range		, encoder_stride=self.encoder_stride		, out_features=self.out_features		, out_indices=self.out_indices		, )





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= MaskFormerSwinModel(config=_lowerCamelCase					)
			model.to(_lowerCamelCase					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(_lowerCamelCase					)

			SCREAMING_SNAKE_CASE       :     int				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
			SCREAMING_SNAKE_CASE       :     int				= int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, expected_seq_len, expected_dim)					)





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Dict					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= MaskFormerSwinBackbone(config=_lowerCamelCase					)
			model.to(_lowerCamelCase					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     str				= 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					)		, [13, 16, 16, 16]					)

			# verify channels
			self.parent.assertEqual(len(model.channels					)		, len(config.out_features					)					)
			self.parent.assertListEqual(model.channels		, [16, 32, 64]					)

			# verify ValueError
			with self.parent.assertRaises(_lowerCamelCase					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ['''stem''']
					SCREAMING_SNAKE_CASE       :     Dict				= MaskFormerSwinBackbone(config=_lowerCamelCase					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.prepare_config_and_inputs()
			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     List[str]				= {'''pixel_values''': pixel_values}
			return config, inputs_dict





@require_torch
class 				lowercase__(				__UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase   ):
	UpperCamelCase				=  (
	    (
	        MaskFormerSwinModel,
	        MaskFormerSwinBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= MaskFormerSwinModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ConfigTester(self		, config_class=_lowerCamelCase		, embed_dim=37					)





	@require_torch_multi_gpu
	@unittest.skip(
	    reason=(
	        '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
	        ''' `nn.DataParallel`'''
	    )					)
	def   __lowerCAmelCase (						self    :Tuple					)							->			Tuple:







			'''simple docstring'''




			pass





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







			'''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]					)							->			Any:







			'''simple docstring'''




			return





	def   __lowerCAmelCase (						self    :str					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_lowerCamelCase					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*_lowerCamelCase					)





	@unittest.skip('''Swin does not use inputs_embeds'''					)
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			pass





	@unittest.skip('''Swin does not support feedforward chunking'''					)
	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




			pass





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     str				= model_class(_lowerCamelCase					)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(_lowerCamelCase		, nn.Linear					)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE       :     int				= ['''pixel_values''']
					self.assertListEqual(arg_names[:1]		, _lowerCamelCase					)





	@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''					)
	def   __lowerCAmelCase (						self    :Any					)							->			str:







			'''simple docstring'''




			pass





	@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''					)
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :str					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model_class(_lowerCamelCase					)
			model.to(_lowerCamelCase					)
			model.eval()

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

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= outputs.hidden_states

			SCREAMING_SNAKE_CASE       :     List[str]				= getattr(
			    self.model_tester		, '''expected_num_hidden_layers'''		, len(self.model_tester.depths					) + 1					)
			self.assertEqual(len(_lowerCamelCase					)		, _lowerCamelCase					)

			# Swin has a different seq_length
			SCREAMING_SNAKE_CASE       :     Optional[int]				= (
			    config.patch_size
			    if isinstance(config.patch_size		, collections.abc.Iterable					)
			    else (config.patch_size, config.patch_size)
			)

			SCREAMING_SNAKE_CASE       :     Tuple				= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]					)		, [num_patches, self.model_tester.embed_dim]		, )





	def   __lowerCAmelCase (						self    :Any					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
			    self.model_tester.image_size
			    if isinstance(self.model_tester.image_size		, collections.abc.Iterable					)
			    else (self.model_tester.image_size, self.model_tester.image_size)
			)

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     str				= True
					self.check_hidden_states_output(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase					)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= True

					self.check_hidden_states_output(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     List[str]				= 3

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= (
			    self.model_tester.image_size
			    if isinstance(self.model_tester.image_size		, collections.abc.Iterable					)
			    else (self.model_tester.image_size, self.model_tester.image_size)
			)
			SCREAMING_SNAKE_CASE       :     str				= (
			    config.patch_size
			    if isinstance(config.patch_size		, collections.abc.Iterable					)
			    else (config.patch_size, config.patch_size)
			)

			SCREAMING_SNAKE_CASE       :     Tuple				= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			SCREAMING_SNAKE_CASE       :     List[Any]				= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= True
					self.check_hidden_states_output(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, (padded_height, padded_width)					)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE       :     int				= True
					self.check_hidden_states_output(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, (padded_height, padded_width)					)





	@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''					)
	def   __lowerCAmelCase (						self    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			pass





	@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''					)
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			pass





	@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''					)
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			pass







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







			'''simple docstring'''




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

			def set_nan_tensor_to_zero(lowerCamelCase_    :Union[str, Any]					):
					SCREAMING_SNAKE_CASE       :     List[str]				= 0
					return t

			def check_equivalence(lowerCamelCase_    :Dict		, lowerCamelCase_    :Any		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Tuple={}					):
					with torch.no_grad():
							SCREAMING_SNAKE_CASE       :     int				= model(**_lowerCamelCase		, return_dict=_lowerCamelCase		, **_lowerCamelCase					)
							SCREAMING_SNAKE_CASE       :     int				= model(**_lowerCamelCase		, return_dict=_lowerCamelCase		, **_lowerCamelCase					).to_tuple()

							def recursive_check(lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :List[str]					):
									if isinstance(_lowerCamelCase		, (List, Tuple)					):
											for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase		, _lowerCamelCase					):
													recursive_check(_lowerCamelCase		, _lowerCamelCase					)
									elif isinstance(_lowerCamelCase		, _lowerCamelCase					):
											for tuple_iterable_value, dict_iterable_value in zip(
											    tuple_object.values()		, dict_object.values()					):
													recursive_check(_lowerCamelCase		, _lowerCamelCase					)
									elif tuple_object is None:
											return
									else:
											self.assertTrue(
											    torch.allclose(
											        set_nan_tensor_to_zero(_lowerCamelCase					)		, set_nan_tensor_to_zero(_lowerCamelCase					)		, atol=1E-5					)		, msg=(
											        '''Tuple and dict output are not equal. Difference:'''
											        f" {torch.max(torch.abs(tuple_object - dict_object					)					)}. Tuple has `nan`:"
											        f" {torch.isnan(_lowerCamelCase					).any()} and `inf`: {torch.isinf(_lowerCamelCase					)}. Dict has"
											        f" `nan`: {torch.isnan(_lowerCamelCase					).any()} and `inf`: {torch.isinf(_lowerCamelCase					)}."
											    )		, )

							recursive_check(_lowerCamelCase		, _lowerCamelCase					)

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Any				= model_class(_lowerCamelCase					)
					model.to(_lowerCamelCase					)
					model.eval()

					SCREAMING_SNAKE_CASE       :     Optional[int]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase					)
					SCREAMING_SNAKE_CASE       :     Tuple				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase					)
					check_equivalence(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase					)

					SCREAMING_SNAKE_CASE       :     List[str]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase		, return_labels=_lowerCamelCase					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase		, return_labels=_lowerCamelCase					)
					check_equivalence(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase					)

					SCREAMING_SNAKE_CASE       :     List[Any]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase					)
					SCREAMING_SNAKE_CASE       :     int				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase					)
					check_equivalence(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, {'''output_hidden_states''': True}					)

					SCREAMING_SNAKE_CASE       :     List[str]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase		, return_labels=_lowerCamelCase					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase		, return_labels=_lowerCamelCase					)
					check_equivalence(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, {'''output_hidden_states''': True}					)





@require_torch
class 				lowercase__(				unittest.TestCase		,						__UpperCAmelCase   ):
	UpperCamelCase				=  (MaskFormerSwinBackbone,) if is_torch_available() else ()
	UpperCamelCase				=  MaskFormerSwinConfig





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= MaskFormerSwinModelTester(self					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     Any				= inputs_dict['''pixel_values'''].shape[0]

			for backbone_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Tuple				= backbone_class(_lowerCamelCase					)
					backbone.to(_lowerCamelCase					)
					backbone.eval()

					SCREAMING_SNAKE_CASE       :     Optional[int]				= backbone(**_lowerCamelCase					)

					# Test default outputs and verify feature maps
					self.assertIsInstance(outputs.feature_maps		, _lowerCamelCase					)
					self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
					for feature_map, n_channels in zip(outputs.feature_maps		, backbone.channels					):
							self.assertTrue(feature_map.shape[:2]		, (batch_size, n_channels)					)
					self.assertIsNone(outputs.hidden_states					)
					self.assertIsNone(outputs.attentions					)

					# Test output_hidden_states=True
					SCREAMING_SNAKE_CASE       :     str				= backbone(**_lowerCamelCase		, output_hidden_states=_lowerCamelCase					)
					self.assertIsNotNone(outputs.hidden_states					)
					self.assertTrue(len(outputs.hidden_states					)		, len(backbone.stage_names					)					)
					# We skip the stem layer
					for hidden_states, n_channels in zip(outputs.hidden_states[1:]		, backbone.channels					):
							for hidden_state in hidden_states:
									# Hidden states are in the format (batch_size, (height * width), n_channels)
									SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= hidden_state.shape
									self.assertTrue((h_batch_size, h_n_channels)		, (batch_size, n_channels)					)

            # Test output_attentions=True
					if self.has_attentions:
							SCREAMING_SNAKE_CASE       :     int				= backbone(**_lowerCamelCase		, output_attentions=_lowerCamelCase					)
							self.assertIsNotNone(outputs.attentions					)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(a_     )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





