
'''simple docstring'''





import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class 	UpperCamelCase__					(      nn.Linear):






 def __init__(     self					:Any		,			_A					:int		,			_A					:int		,			_A					:bool = True		,			_A					:str = "constant"		,			_A					:float = 0.0		,			_A					:str = "topK"		,			)			->     Optional[int]:







       '''simple docstring'''



       super(_A		,			self     ).__init__(in_features=_A		,			out_features=_A		,			bias=_A     )
       assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
       __A    		=     pruning_method

       if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
             __A    		=     mask_scale
             __A    		=     mask_init
             __A    		=     nn.Parameter(torch.empty(self.weight.size()     )     )
             self.init_mask()






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







       '''simple docstring'''



       if self.mask_init == "constant":
             init.constant_(self.mask_scores		,			val=self.mask_scale     )
       elif self.mask_init == "uniform":
             init.uniform_(self.mask_scores		,			a=-self.mask_scale		,			b=self.mask_scale     )
       elif self.mask_init == "kaiming":
             init.kaiming_uniform_(self.mask_scores		,			a=math.sqrt(5     )     )






 def 		lowercase_  (     self					:str		,			_A					:torch.tensor		,			_A					:float     )			->     Tuple:







       '''simple docstring'''



       if self.pruning_method == "topK":
             __A    		=     TopKBinarizer.apply(self.mask_scores		,			_A     )
       elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
             __A    		=     'sigmoied' in self.pruning_method
             __A    		=     ThresholdBinarizer.apply(self.mask_scores		,			_A		,			_A     )
       elif self.pruning_method == "magnitude":
             __A    		=     MagnitudeBinarizer.apply(self.weight		,			_A     )
       elif self.pruning_method == "l0":
             __A		, __A		, __A    		=     -0.1, 1.1, 2 / 3
             if self.training:
                   __A    		=     torch.zeros_like(self.mask_scores     ).uniform_().clamp(0.0_001		,			0.9_999     )
                   __A    		=     torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b     )
             else:
                   __A    		=     torch.sigmoid(self.mask_scores     )
             __A    		=     s * (r - l) + l
             __A    		=     s_bar.clamp(min=0.0		,			max=1.0     )
       # Mask weights with computed mask
       __A    		=     mask * self.weight
       # Compute output (linear layer) with masked weights
       return nn.functional.linear(_A		,			_A		,			self.bias     )








'''simple docstring'''





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


class 	UpperCamelCase__					:






 def __init__(     self					:int		,			_A					:int		,			_A					:List[Any]=13		,			_A					:Any=32		,			_A					:List[str]=2		,			_A					:str=3		,			_A					:Optional[Any]=16		,			_A					:List[Any]=[1, 2, 1]		,			_A					:int=[2, 2, 4]		,			_A					:Optional[Any]=2		,			_A					:Optional[Any]=2.0		,			_A					:int=True		,			_A					:Any=0.0		,			_A					:Tuple=0.0		,			_A					:Union[str, Any]=0.1		,			_A					:str="gelu"		,			_A					:Tuple=False		,			_A					:List[str]=True		,			_A					:Union[str, Any]=0.02		,			_A					:List[str]=1E-5		,			_A					:List[Any]=True		,			_A					:int=None		,			_A					:Union[str, Any]=True		,			_A					:int=10		,			_A					:Optional[int]=8		,			)			->     Optional[int]:







       '''simple docstring'''



       __A    		=     parent
       __A    		=     batch_size
       __A    		=     image_size
       __A    		=     patch_size
       __A    		=     num_channels
       __A    		=     embed_dim
       __A    		=     depths
       __A    		=     num_heads
       __A    		=     window_size
       __A    		=     mlp_ratio
       __A    		=     qkv_bias
       __A    		=     hidden_dropout_prob
       __A    		=     attention_probs_dropout_prob
       __A    		=     drop_path_rate
       __A    		=     hidden_act
       __A    		=     use_absolute_embeddings
       __A    		=     patch_norm
       __A    		=     layer_norm_eps
       __A    		=     initializer_range
       __A    		=     is_training
       __A    		=     scope
       __A    		=     use_labels
       __A    		=     type_sequence_label_size
       __A    		=     encoder_stride






 def 		lowercase_  (     self					:Any     )			->     Optional[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.type_sequence_label_size     )

       __A    		=     self.get_config()

       return config, pixel_values, labels






 def 		lowercase_  (     self					:List[str]     )			->     str:







       '''simple docstring'''



       return DonutSwinConfig(
           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		,			)






 def 		lowercase_  (     self					:List[str]		,			_A					:Any		,			_A					:List[str]		,			_A					:List[str]     )			->     int:







       '''simple docstring'''



       __A    		=     DonutSwinModel(config=_A     )
       model.to(_A     )
       model.eval()
       __A    		=     model(_A     )

       __A    		=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
       __A    		=     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 		lowercase_  (     self					:List[str]     )			->     List[str]:







       '''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 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      List[str]  			=   (DonutSwinModel,) if is_torch_available() else ()
 UpperCAmelCase__      :      Optional[Any]  			=   {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
 UpperCAmelCase__      :      Dict  			=   True

 UpperCAmelCase__      :      int  			=   False
 UpperCAmelCase__      :      List[Any]  			=   False
 UpperCAmelCase__      :      Tuple  			=   False






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







       '''simple docstring'''



       __A    		=     DonutSwinModelTester(self     )
       __A    		=     ConfigTester(self		,			config_class=_A		,			embed_dim=37     )






 def 		lowercase_  (     self					:Dict     )			->     Union[str, 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 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       return






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







       '''simple docstring'''



       __A    		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A     )






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					: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(_A     )
             self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)     )
             __A    		=     model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A		,			nn.Linear     )     )






 def 		lowercase_  (     self					:Any     )			->     Dict:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A    		=     model_class(_A     )
             __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]		,			_A     )






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







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()
       __A    		=     True

       for model_class in self.all_model_classes:
             __A    		=     True
             __A    		=     False
             __A    		=     True
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )
             __A    		=     outputs.attentions
             __A    		=     len(self.model_tester.depths     )
             self.assertEqual(len(_A     )		,			_A     )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __A    		=     True
             __A    		=     config.window_size**2
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )
             __A    		=     outputs.attentions
             self.assertEqual(len(_A     )		,			_A     )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]     )		,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,			)
             __A    		=     len(_A     )

             # Check attention is always last and order is fine
             __A    		=     True
             __A    		=     True
             __A    		=     model_class(_A     )
             model.to(_A     )
             model.eval()
             with torch.no_grad():
                   __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )

             if hasattr(self.model_tester		,			'num_hidden_states_types'     ):
                   __A    		=     self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   __A    		=     2
             self.assertEqual(out_len + added_hidden_states		,			len(_A     )     )

             __A    		=     outputs.attentions

             self.assertEqual(len(_A     )		,			_A     )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]     )		,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,			)






 def 		lowercase_  (     self					:Dict		,			_A					:Optional[int]		,			_A					:Tuple		,			_A					:List[Any]		,			_A					:List[Any]     )			->     Union[str, Any]:







       '''simple docstring'''



       __A    		=     model_class(_A     )
       model.to(_A     )
       model.eval()

       with torch.no_grad():
             __A    		=     model(**self._prepare_for_class(_A		,			_A     )     )

       __A    		=     outputs.hidden_states

       __A    		=     getattr(
           self.model_tester		,			'expected_num_hidden_layers'		,			len(self.model_tester.depths     ) + 1     )
       self.assertEqual(len(_A     )		,			_A     )

       # DonutSwin has a different seq_length
       __A    		=     (
           config.patch_size
           if isinstance(config.patch_size		,			collections.abc.Iterable     )
           else (config.patch_size, config.patch_size)
       )

       __A    		=     (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]		,			)

       __A    		=     outputs.reshaped_hidden_states
       self.assertEqual(len(_A     )		,			_A     )

       __A		, __A		, __A		, __A    		=     reshaped_hidden_states[0].shape
       __A    		=     (
           reshaped_hidden_states[0].view(_A		,			_A		,			height * width     ).permute(0		,			2		,			1     )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]     )		,			[num_patches, self.model_tester.embed_dim]		,			)






 def 		lowercase_  (     self					:List[Any]     )			->     Any:







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       __A    		=     (
           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:
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			_A     )

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

             self.check_hidden_states_output(_A		,			_A		,			_A		,			_A     )






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







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()
       __A    		=     3

       __A    		=     (
           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)
       )
       __A    		=     (
           config.patch_size
           if isinstance(config.patch_size		,			collections.abc.Iterable     )
           else (config.patch_size, config.patch_size)
       )

       __A    		=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __A    		=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			(padded_height, padded_width)     )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __A    		=     True
             self.check_hidden_states_output(_A		,			_A		,			_A		,			(padded_height, padded_width)     )






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







       '''simple docstring'''



       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A    		=     DonutSwinModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )






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







       '''simple docstring'''



       __A		, __A    		=     self.model_tester.prepare_config_and_inputs_for_common()

       __A    		=     _config_zero_init(_A     )
       for model_class in self.all_model_classes:
             __A    		=     model_class(config=_A     )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()		,			[0.0, 1.0]		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)






