
from collections import namedtuple

A__       :   List[str]   						=					namedtuple("""from_to""", """from_ to""")

A__       :   List[Any]   						=					{
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.000_0098, 1_0_1_3_2_5),
    """bar""": from_to(0.98_6923, 1.0_1325),
    """kilopascal""": from_to(0.0098_6923, 101.325),
    """megapascal""": from_to(9.8_6923, 0.10_1325),
    """psi""": from_to(0.06_8046, 14.6959),
    """inHg""": from_to(0.033_4211, 29.9213),
    """torr""": from_to(0.0013_1579, 7_6_0),
}



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  List[str]	,__UpperCamelCase       :  int    ):
			if from_type not in PRESSURE_CONVERSION:
						raise ValueError(
						    f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
						    + ''', '''.join(__UpperCamelCase    )    )
			if to_type not in PRESSURE_CONVERSION:
						raise ValueError(
						    f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
						    + ''', '''.join(__UpperCamelCase    )    )
			return (
			    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=32 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=3 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=[1, 2, 1] ,	SCREAMING_SNAKE_CASE__=[2, 2, 4] ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=2.0 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.02 ,	SCREAMING_SNAKE_CASE__=1E-5 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=10 ,	SCREAMING_SNAKE_CASE__=8 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			parent
								lowerCAmelCase__ :    str				      =			batch_size
								lowerCAmelCase__ :    Tuple				      =			image_size
								lowerCAmelCase__ :    Union[str, Any]				      =			patch_size
								lowerCAmelCase__ :    int				      =			num_channels
								lowerCAmelCase__ :    Tuple				      =			embed_dim
								lowerCAmelCase__ :    Optional[int]				      =			depths
								lowerCAmelCase__ :    Tuple				      =			num_heads
								lowerCAmelCase__ :    Tuple				      =			window_size
								lowerCAmelCase__ :    Dict				      =			mlp_ratio
								lowerCAmelCase__ :    int				      =			qkv_bias
								lowerCAmelCase__ :    Dict				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Dict				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    Tuple				      =			drop_path_rate
								lowerCAmelCase__ :    int				      =			hidden_act
								lowerCAmelCase__ :    Union[str, Any]				      =			use_absolute_embeddings
								lowerCAmelCase__ :    Dict				      =			patch_norm
								lowerCAmelCase__ :    Any				      =			layer_norm_eps
								lowerCAmelCase__ :    Tuple				      =			initializer_range
								lowerCAmelCase__ :    Optional[Any]				      =			is_training
								lowerCAmelCase__ :    str				      =			scope
								lowerCAmelCase__ :    List[str]				      =			use_labels
								lowerCAmelCase__ :    Optional[Any]				      =			type_sequence_label_size
								lowerCAmelCase__ :    int				      =			encoder_stride
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								lowerCAmelCase__ :    Tuple				      =			None
								if self.use_labels:
											lowerCAmelCase__ :    Tuple				      =			ids_tensor([self.batch_size] ,	self.type_sequence_label_size    )

								lowerCAmelCase__ :    Tuple				      =			self.get_config()

								return config, pixel_values, labels
					def lowercase_			(		self    ):
								"""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 ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel(config=SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()
								lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
								lowerCAmelCase__ :    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 lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				
								) :    Optional[Any]				      =			config_and_inputs
								lowerCAmelCase__ :    Union[str, Any]				      =			{'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class 						lowercase       (  __UpperCamelCase	,		__UpperCamelCase	,		unittest.TestCase					):
					__a					=    (DonutSwinModel,) if is_torch_available() else ()
					__a					=    {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
					__a					=    True

					__a					=    False
					__a					=    False
					__a					=    False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			DonutSwinModelTester(self    )
								lowerCAmelCase__ :    Union[str, Any]				      =			ConfigTester(self ,	config_class=SCREAMING_SNAKE_CASE__ ,	embed_dim=37    )
					def lowercase_			(		self    ):
								"""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    ):
								"""simple docstring"""







								return
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								pass
					def lowercase_			(		self    ):
								"""simple docstring"""







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

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    List[str]				      =			model_class(SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(model.get_input_embeddings() ,	(nn.Module)    )
											lowerCAmelCase__ :    Dict				      =			model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__ ,	nn.Linear    )    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Tuple				      =			self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCAmelCase__ :    List[str]				      =			['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    List[Any]				      =			True

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    int				      =			True
											lowerCAmelCase__ :    Union[str, Any]				      =			False
											lowerCAmelCase__ :    Tuple				      =			True
											lowerCAmelCase__ :    str				      =			model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
														lowerCAmelCase__ :    Dict				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )
											lowerCAmelCase__ :    List[Any]				      =			outputs.attentions
											lowerCAmelCase__ :    str				      =			len(self.model_tester.depths    )
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

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

											self.assertListEqual(
											    list(attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
											lowerCAmelCase__ :    Optional[int]				      =			len(SCREAMING_SNAKE_CASE__    )

											# Check attention is always last and order is fine
											lowerCAmelCase__ :    Any				      =			True
											lowerCAmelCase__ :    List[Any]				      =			True
											lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											with torch.no_grad():
														lowerCAmelCase__ :    Any				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

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

											lowerCAmelCase__ :    List[str]				      =			outputs.attentions

											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()

								with torch.no_grad():
											lowerCAmelCase__ :    List[str]				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

								lowerCAmelCase__ :    List[str]				      =			outputs.hidden_states

								lowerCAmelCase__ :    Optional[Any]				      =			getattr(
								    self.model_tester ,	'''expected_num_hidden_layers''' ,	len(self.model_tester.depths    ) + 1    )
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    Dict				      =			(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] ,	)

								lowerCAmelCase__ :    List[str]				      =			outputs.reshaped_hidden_states
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			reshaped_hidden_states[0].shape
								lowerCAmelCase__ :    Dict				      =			(
								    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	height * width    ).permute(0 ,	2 ,	1    )
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Optional[int]				      =			(
								    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:
											lowerCAmelCase__ :    Any				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

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

											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    Union[str, Any]				      =			3

								lowerCAmelCase__ :    int				      =			(
								    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)
								)
								lowerCAmelCase__ :    str				      =			(
								    config.patch_size
								    if isinstance(config.patch_size ,	collections.abc.Iterable    )
								    else (config.patch_size, config.patch_size)
								)

								lowerCAmelCase__ :    Optional[int]				      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCAmelCase__ :    Any				      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Dict				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Union[str, Any]				      =			_config_zero_init(SCREAMING_SNAKE_CASE__    )
								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Union[str, Any]				      =			model_class(config=SCREAMING_SNAKE_CASE__    )
											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''' ,	)



