


"""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			,   __A			,   __A=13			,   __A=32			,   __A=2			,   __A=3			,   __A=16			,   __A=[32, 64, 128]			,   __A=[1, 2, 1]			,   __A=[2, 2, 4]			,   __A=2			,   __A=2.0			,   __A=True			,   __A=0.0			,   __A=0.0			,   __A=0.1			,   __A="gelu"			,   __A=False			,   __A=True			,   __A=0.02			,   __A=1E-5			,   __A=True			,   __A=None			,   __A=True			,   __A=10			,   __A=8			,   __A=["stage1", "stage2"]			,   __A=[1, 2]			,   ) ->					int:
					a					=parent
					a					=batch_size
					a					=image_size
					a					=patch_size
					a					=num_channels
					a					=embed_dim
					a					=hidden_sizes
					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
					a					=out_features
					a					=out_indices


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
					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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					int:
					a					=FocalNetModel(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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					int:
					a					=FocalNetBackbone(config=__A						)
					model.to(__A						)
					model.eval()
					a					=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
					a					=None
					a					=FocalNetBackbone(config=__A						)
					model.to(__A						)
					model.eval()
					a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Any:
					a					=FocalNetForMaskedImageModeling(config=__A						)
					model.to(__A						)
					model.eval()
					a					=model(__A						)
					self.parent.assertEqual(
					    result.reconstruction.shape			,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

					# test greyscale images
					a					=1
					a					=FocalNetForMaskedImageModeling(__A						)
					model.to(__A						)
					model.eval()

					a					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					a					=model(__A						)
					self.parent.assertEqual(result.reconstruction.shape			,   (self.batch_size, 1, self.image_size, self.image_size)						)


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					str:
					a					=self.type_sequence_label_size
					a					=FocalNetForImageClassification(__A						)
					model.to(__A						)
					model.eval()
					a					=model(__A			,   labels=__A						)
					self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					a					=1
					a					=FocalNetForImageClassification(__A						)
					model.to(__A						)
					model.eval()

					a					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					a					=model(__A						)
					self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)						)





	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
					a					=self.prepare_config_and_inputs()

					a		,				a		,				a					=config_and_inputs
					a					={'''pixel_values''': pixel_values}
					return config, inputs_dict






@require_torch
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






	"""simple docstring"""




	__lowerCAmelCase			 =  (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	__lowerCAmelCase			 =  (
	    {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__lowerCAmelCase			 =  False

	__lowerCAmelCase			 =  False
	__lowerCAmelCase			 =  False
	__lowerCAmelCase			 =  False
	__lowerCAmelCase			 =  False


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
					a					=FocalNetModelTester(self						)
					a					=ConfigTester(self			,   config_class=__A			,   embed_dim=37			,   has_text_modality=__A						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					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 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					return


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
					a					=self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__A						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
					a					=self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*__A						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
					a					=self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__A						)


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
					a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					pass


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


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
									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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
					a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
									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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A						) ->					List[Any]:
					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						)

					# FocalNet 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 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
					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[:-1]:
									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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					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[:-1]:
									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 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
					for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a					=FocalNetModel.from_pretrained(__A						)
									self.assertIsNotNone(__A						)





	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					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'''			,   )






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






	"""simple docstring"""




	@cached_property
	def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
					# TODO update organization
					return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''						) if is_vision_available() else None





	@slow
	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
					a					=FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''						).to(__A						)
					a					=self.default_image_processor

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

					# forward pass
					with torch.no_grad():
									a					=model(**__A						)

					# verify the logits
					a					=torch.Size((1, 1000)						)
					self.assertEqual(outputs.logits.shape			,   __A						)
					a					=torch.tensor([0.2_166, -0.4_368, 0.2_191]						).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    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






	"""simple docstring"""




	__lowerCAmelCase			 =  (FocalNetBackbone,) if is_torch_available() else ()
	__lowerCAmelCase			 =  FocalNetConfig

	__lowerCAmelCase			 =  False


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
					a					=FocalNetModelTester(self						)



"""simple docstring"""







from __future__ import annotations
def _A						(						lowercase	,   lowercase	,   lowercase	,   ):







    """simple docstring"""
    if (electron_conc, hole_conc, intrinsic_conc).count(0		) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''		)
    elif electron_conc < 0:
        raise ValueError('''Electron concentration cannot be negative in a semiconductor'''		)
    elif hole_conc < 0:
        raise ValueError('''Hole concentration cannot be negative in a semiconductor'''		)
    elif intrinsic_conc < 0:
        raise ValueError(
            '''Intrinsic concentration cannot be negative in a semiconductor'''		)
    elif electron_conc == 0:
        return (
            "electron_conc",
            intrinsic_conc**2 / hole_conc,
        )
    elif hole_conc == 0:
        return (
            "hole_conc",
            intrinsic_conc**2 / electron_conc,
        )
    elif intrinsic_conc == 0:
        return (
            "intrinsic_conc",
            (electron_conc * hole_conc) ** 0.5,
        )
    else:
        return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()