



import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
   from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[Any]=13			,						__lowerCAmelCase					:					Tuple=32			,						__lowerCAmelCase					:					Optional[Any]=2			,						__lowerCAmelCase					:					Optional[Any]=3			,						__lowerCAmelCase					:					int=16			,						__lowerCAmelCase					:					int=[1, 2, 1]			,						__lowerCAmelCase					:					int=[2, 2, 4]			,						__lowerCAmelCase					:					Optional[int]=2			,						__lowerCAmelCase					:					int=2.0			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Optional[int]=0.0			,						__lowerCAmelCase					:					Dict=0.0			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					Tuple="gelu"			,						__lowerCAmelCase					:					Tuple=False			,						__lowerCAmelCase					:					Any=True			,						__lowerCAmelCase					:					int=0.02			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Union[str, Any]=True			,						__lowerCAmelCase					:					List[str]=10			,						__lowerCAmelCase					:					Dict=8			,						)   ->	Optional[int]:
    _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     snake_case_      (    self					:					List[str]      )   ->	str:
    _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     snake_case_      (    self					:					int      )   ->	List[Any]:
    return SwinvaConfig(
        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     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Dict      )   ->	Union[str, Any]:
    _A      =       SwinvaModel(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )

    _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     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[int]:
    _A      =       SwinvaForMaskedImageModeling(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )
    self.parent.assertEqual(
        result.logits.shape			,						(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

    # test greyscale images
    _A      =       1
    _A      =       SwinvaForMaskedImageModeling(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()

    _A      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    _A      =       model(__lowerCAmelCase      )
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, 1, self.image_size, self.image_size)      )

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Any:
    _A      =       self.type_sequence_label_size
    _A      =       SwinvaForImageClassification(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase			,						labels=__lowerCAmelCase      )
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.type_sequence_label_size)      )

 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       self.prepare_config_and_inputs()
    _A  ,    _A  ,    _A      =       config_and_inputs
    _A      =       {'''pixel_values''': pixel_values}
    return config, inputs_dict


@require_torch
class 	lowerCamelCase__	(       _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: int =  (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 a__					: Any =  (
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 a__					: Union[str, Any] =  False
 a__					: Optional[int] =  False
 a__					: Any =  False
 a__					: Optional[int] =  False

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Union[str, Any]:
    _A      =       SwinvaModelTester(self      )
    _A      =       ConfigTester(self			,						config_class=__lowerCAmelCase			,						embed_dim=37      )

 def     snake_case_      (    self					:					Tuple      )   ->	Any:
    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     snake_case_      (    self					:					Dict      )   ->	Tuple:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase      )

 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''      )
 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    pass

 @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''      )
 def     snake_case_      (    self					:					str      )   ->	str:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       _A      =       model_class(__lowerCAmelCase      )
       self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)      )
       _A      =       model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__lowerCAmelCase			,						nn.Linear      )      )

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Tuple:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       _A      =       model_class(__lowerCAmelCase      )
       _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]			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _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(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.eval()
       with torch.no_grad():
          _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )
       _A      =       outputs.attentions
       _A      =       len(self.model_tester.depths      )
       self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

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

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

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

       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(__lowerCAmelCase      )      )

       _A      =       outputs.attentions

       self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[Any]:
    _A      =       model_class(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()

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

    _A      =       outputs.hidden_states

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

    # Swinv2 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(__lowerCAmelCase      )			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					int      )   ->	Tuple:
    _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(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

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

       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Optional[int]:
    _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(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(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(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(padded_height, padded_width)      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	List[str]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase      )

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Tuple:
    for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _A      =       SwinvaModel.from_pretrained(__lowerCAmelCase      )
       self.assertIsNotNone(__lowerCAmelCase      )

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

    _A      =       _config_zero_init(__lowerCAmelCase      )
    for model_class in self.all_model_classes:
       _A      =       model_class(config=__lowerCAmelCase      )
       for name, param in model.named_parameters():
          if "embeddings" not in name and "logit_scale" 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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @cached_property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return (
        AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      )
        if is_vision_available()
        else None
    )

 @slow
 def     snake_case_      (    self					:					str      )   ->	Dict:
    _A      =       SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      ).to(
        __lowerCAmelCase      )
    _A      =       self.default_image_processor

    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_processor(images=__lowerCAmelCase			,						return_tensors='''pt'''      ).to(__lowerCAmelCase      )

    # forward pass
    with torch.no_grad():
       _A      =       model(**__lowerCAmelCase      )

    # verify the logits
    _A      =       torch.Size((1, 10_00)      )
    self.assertEqual(outputs.logits.shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([-0.3947, -0.4306, 0.0026]      ).to(__lowerCAmelCase      )
    self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )







from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

