


"""simple docstring"""


from math import pow, sqrt




def   snake_case		(     *UpperCamelCase__       :						float    )   ->      bool:
      lowerCamelCase					:					Dict        =	len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
      return result




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      float | ValueError:
      return (
          round(sqrt(molar_mass_a / molar_mass_a    )			,				6    )
          if validate(UpperCamelCase__			,				UpperCamelCase__    )
          else ValueError("""Input Error: Molar mass values must greater than 0."""    )
      )




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      float | ValueError:
      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )			,				6    )
          if validate(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""    )
      )




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      float | ValueError:
      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )			,				6    )
          if validate(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""    )
      )




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      float | ValueError:
      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a			,				2    )			,				6    )
          if validate(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""    )
      )






def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						float			,				UpperCamelCase__       :						float    )   ->      float | ValueError:
      return (
          round(pow(effusion_rate_a / effusion_rate_a			,				2    ) / molar_mass			,				6    )
          if validate(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""    )
      )








"""simple docstring"""


import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _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 (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						Optional[int]=13     ,		__a:						List[str]=32     ,		__a:						int=2     ,		__a:						List[str]=3     ,		__a:						Union[str, Any]=16     ,		__a:						int=[32, 64, 128]     ,		__a:						Optional[Any]=[1, 2, 1]     ,		__a:						Optional[int]=[2, 2, 4]     ,		__a:						Tuple=2     ,		__a:						Dict=2.0     ,		__a:						List[str]=True     ,		__a:						Optional[Any]=0.0     ,		__a:						Any=0.0     ,		__a:						List[Any]=0.1     ,		__a:						List[str]="gelu"     ,		__a:						Tuple=False     ,		__a:						Union[str, Any]=True     ,		__a:						Optional[int]=0.02     ,		__a:						Tuple=1e-5     ,		__a:						int=True     ,		__a:						List[Any]=None     ,		__a:						Optional[int]=True     ,		__a:						Dict=10     ,		__a:						List[str]=8     ,		__a:						Any=["stage1", "stage2"]     ,		__a:						Union[str, Any]=[1, 2]     ,		)->    Dict:
           lowerCamelCase					:					Dict        =	parent
           lowerCamelCase					:					Optional[Any]        =	batch_size
           lowerCamelCase					:					Union[str, Any]        =	image_size
           lowerCamelCase					:					Optional[int]        =	patch_size
           lowerCamelCase					:					Any        =	num_channels
           lowerCamelCase					:					Any        =	embed_dim
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					List[Any]        =	depths
           lowerCamelCase					:					Tuple        =	num_heads
           lowerCamelCase					:					List[Any]        =	window_size
           lowerCamelCase					:					str        =	mlp_ratio
           lowerCamelCase					:					str        =	qkv_bias
           lowerCamelCase					:					str        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Tuple        =	drop_path_rate
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					Tuple        =	use_absolute_embeddings
           lowerCamelCase					:					List[str]        =	patch_norm
           lowerCamelCase					:					List[str]        =	layer_norm_eps
           lowerCamelCase					:					str        =	initializer_range
           lowerCamelCase					:					Tuple        =	is_training
           lowerCamelCase					:					int        =	scope
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	type_sequence_label_size
           lowerCamelCase					:					str        =	encoder_stride
           lowerCamelCase					:					List[str]        =	out_features
           lowerCamelCase					:					Optional[int]        =	out_indices

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					str        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[str]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					str        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           return FocalNetConfig(
               image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		hidden_sizes=self.hidden_sizes     ,		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 		a__						(	self:						Tuple     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Tuple        =	model(__a	)

           lowerCamelCase					:					Any        =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
           lowerCamelCase					:					List[Any]        =	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 		a__						(	self:						Optional[int]     ,		__a:						Dict     ,		__a:						Tuple     ,		__a:						List[Any]	)->    int:
           lowerCamelCase					:					List[Any]        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Optional[Any]        =	model(__a	)

           # 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.image_size, 8, 8]	)

           # 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
           lowerCamelCase					:					Dict        =	None
           lowerCamelCase					:					Dict        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size * 2, 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		1	)
           self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetForMaskedImageModeling(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a	)
           self.parent.assertEqual(
               result.reconstruction.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

           # test greyscale images
           lowerCamelCase					:					List[str]        =	1
           lowerCamelCase					:					Any        =	FocalNetForMaskedImageModeling(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					str        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Tuple        =	model(__a	)
           self.parent.assertEqual(result.reconstruction.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)	)

     def 		a__						(	self:						str     ,		__a:						Optional[Any]     ,		__a:						Optional[Any]     ,		__a:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	self.type_sequence_label_size
           lowerCamelCase					:					Optional[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)

           # test greyscale images
           lowerCamelCase					:					int        =	1
           lowerCamelCase					:					List[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)







     def 		a__						(	self:						int	)->    Optional[int]:
           lowerCamelCase					:					str        =	self.prepare_config_and_inputs()

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	config_and_inputs
           lowerCamelCase					:					List[Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Optional[int]													=(
         {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     snake_case__	:					Tuple													=False

     snake_case__	:					Dict													=False
     snake_case__	:					Dict													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Optional[int]													=False

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	FocalNetModelTester(self	)
           lowerCamelCase					:					Optional[Any]        =	ConfigTester(self     ,		config_class=__a     ,		embed_dim=37     ,		has_text_modality=__a	)

     def 		a__						(	self:						List[str]	)->    List[str]:
           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 		a__						(	self:						List[str]	)->    Union[str, Any]:
           return

     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[Any]	)->    Dict:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						List[Any]	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__a	)

     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a	)

     @unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
     def 		a__						(	self:						Optional[Any]	)->    str:
           pass

     @unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
     def 		a__						(	self:						Optional[Any]	)->    Dict:
           pass

     def 		a__						(	self:						Optional[Any]	)->    Dict:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)	)
                 lowerCamelCase					:					Dict        =	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__a     ,		nn.Linear	)	)

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					int        =	model_class(__a	)
                 lowerCamelCase					:					int        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					Any        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[Any]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[str]	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	model_class(__a	)
           model.to(__a	)
           model.eval()

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

           lowerCamelCase					:					List[str]        =	outputs.hidden_states

           lowerCamelCase					:					Tuple        =	getattr(
               self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths	) + 1	)
           self.assertEqual(len(__a	)     ,		__a	)

           # FocalNet has a different seq_length
           lowerCamelCase					:					Tuple        =	(
               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					:					Optional[Any]        =	outputs.reshaped_hidden_states
           self.assertEqual(len(__a	)     ,		__a	)

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	reshaped_hidden_states[0].shape
           lowerCamelCase					:					Tuple        =	(
               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 		a__						(	self:						Any	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					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[:-1]:
                 lowerCamelCase					:					List[str]        =	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"]
                 lowerCamelCase					:					List[Any]        =	True

                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

     def 		a__						(	self:						str	)->    Union[str, Any]:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase					:					List[str]        =	3

           lowerCamelCase					:					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)
           )
           lowerCamelCase					:					Optional[int]        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Optional[Any]        =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCamelCase					:					List[str]        =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					str        =	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"]
                 lowerCamelCase					:					Union[str, Any]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

     @slow
     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					List[str]        =	FocalNetModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)







     def 		a__						(	self:						str	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					int        =	_config_zero_init(__a	)
           for model_class in self.all_model_classes:
                 lowerCamelCase					:					int        =	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'     ,		)


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


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__a	)
           lowerCamelCase					:					Any        =	self.default_image_processor

           lowerCamelCase					:					Union[str, Any]        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
           lowerCamelCase					:					int        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Tuple        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)
           lowerCamelCase					:					List[str]        =	torch.tensor([0.21_66, -0.43_68, 0.21_91]	).to(__a	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__a     ,		atol=1e-4	)	)
           self.assertTrue(outputs.logits.argmax(dim=-1	).item()     ,		281	)


@require_torch
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=(FocalNetBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[int]													=FocalNetConfig

     snake_case__	:					str													=False

     def 		a__						(	self:						Union[str, Any]	)->    Tuple:
           lowerCamelCase					:					str        =	FocalNetModelTester(self	)




