
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
		from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor


class 						__magic_name__      (  _lowerCAmelCase			):





		def 					_A							(							self			:		int ):
							lowerCAmelCase						:    List[Any]     		=   self.config_class(**self.inputs_dict )
							self.parent.assertTrue(hasattr(A_  ,     '''hidden_sizes''' ) )
							self.parent.assertTrue(hasattr(A_  ,     '''num_encoder_blocks''' ) )



class 						__magic_name__      :





		def __init__(							self			:		Optional[Any]  ,     lowerCamelCase__			:		Union[str, Any]  ,     lowerCamelCase__			:		List[str]=1_3  ,     lowerCamelCase__			:		Optional[int]=6_4  ,     lowerCamelCase__			:		int=3  ,     lowerCamelCase__			:		str=4  ,     lowerCamelCase__			:		int=[2, 2, 2, 2]  ,     lowerCamelCase__			:		Dict=[8, 4, 2, 1]  ,     lowerCamelCase__			:		Optional[Any]=[1_6, 3_2, 6_4, 1_2_8]  ,     lowerCamelCase__			:		Optional[Any]=[1, 4, 8, 1_6]  ,     lowerCamelCase__			:		Dict=False  ,     lowerCamelCase__			:		Tuple=True  ,     lowerCamelCase__			:		List[Any]="gelu"  ,     lowerCamelCase__			:		Optional[Any]=0.1  ,     lowerCamelCase__			:		Optional[Any]=0.0_2  ,     lowerCamelCase__			:		str=3  ,     lowerCamelCase__			:		int=None  ,     ):
							lowerCAmelCase						:    Optional[Any]     		=   parent
							lowerCAmelCase						:    Union[str, Any]     		=   batch_size
							lowerCAmelCase						:    Union[str, Any]     		=   image_size
							lowerCAmelCase						:    List[str]     		=   num_channels
							lowerCAmelCase						:    Union[str, Any]     		=   num_encoder_blocks
							lowerCAmelCase						:    List[Any]     		=   sr_ratios
							lowerCAmelCase						:    List[str]     		=   depths
							lowerCAmelCase						:    List[Any]     		=   hidden_sizes
							lowerCAmelCase						:    Tuple     		=   downsampling_rates
							lowerCAmelCase						:    List[str]     		=   is_training
							lowerCAmelCase						:    Union[str, Any]     		=   use_labels
							lowerCAmelCase						:    Tuple     		=   hidden_act
							lowerCAmelCase						:    Dict     		=   hidden_dropout_prob
							lowerCAmelCase						:    int     		=   initializer_range
							lowerCAmelCase						:    Optional[int]     		=   num_labels
							lowerCAmelCase						:    Tuple     		=   scope





		def 					_A							(							self			:		Optional[Any] ):
							lowerCAmelCase						:    Optional[int]     		=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							lowerCAmelCase						:    List[Any]     		=   None
							if self.use_labels:
												lowerCAmelCase						:    Optional[Any]     		=   ids_tensor([self.batch_size, self.image_size, self.image_size]  ,     self.num_labels )

							lowerCAmelCase						:    Union[str, Any]     		=   PoolFormerConfig(
							    image_size=self.image_size  ,     num_channels=self.num_channels  ,     num_encoder_blocks=self.num_encoder_blocks  ,     depths=self.depths  ,     hidden_sizes=self.hidden_sizes  ,     hidden_act=self.hidden_act  ,     hidden_dropout_prob=self.hidden_dropout_prob  ,     initializer_range=self.initializer_range  ,     )

							return config, pixel_values, labels





		def 					_A							(							self			:		List[Any]  ,     lowerCamelCase__			:		Optional[Any]  ,     lowerCamelCase__			:		Any  ,     lowerCamelCase__			:		Optional[Any] ):
							lowerCAmelCase						:    Union[str, Any]     		=   PoolFormerModel(config=A_ )
							model.to(A_ )
							model.eval()
							lowerCAmelCase						:    Any     		=   model(A_ )
							lowerCAmelCase						:    Union[str, Any]     		=   self.image_size // 3_2.0
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )





		def 					_A							(							self			:		str ):
							lowerCAmelCase						:    Optional[int]     		=   self.prepare_config_and_inputs()
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:    Any     		=   config_and_inputs
							lowerCAmelCase						:    Tuple     		=   {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class 						__magic_name__      (  _lowerCAmelCase, _lowerCAmelCase, unittest.TestCase			):
		_lowerCAmelCase						=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
		_lowerCAmelCase						=		(
		    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False





		def 					_A							(							self			:		List[str] ):
							lowerCAmelCase						:    Union[str, Any]     		=   PoolFormerModelTester(self )
							lowerCAmelCase						:    str     		=   PoolFormerConfigTester(self  ,     config_class=A_ )





		def 					_A							(							self			:		str ):
							self.config_tester.run_common_tests()





		def 					_A							(							self			:		Any ):
							lowerCAmelCase						:    Optional[Any]     		=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_ )





		@unittest.skip('''PoolFormer does not use inputs_embeds''' )
		def 					_A							(							self			:		Any ):
							pass





		@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods''' )
		def 					_A							(							self			:		Any ):
							pass





		def 					_A							(							self			:		Any ):
							def check_hidden_states_output(lowerCamelCase__			:		str  ,     lowerCamelCase__			:		List[Any]  ,     lowerCamelCase__			:		Union[str, Any] ):
												lowerCAmelCase						:    int     		=   model_class(A_ )
												model.to(A_ )
												model.eval()

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

												lowerCAmelCase						:    Optional[Any]     		=   outputs.hidden_states

												lowerCAmelCase						:    int     		=   self.model_tester.num_encoder_blocks
												self.assertEqual(len(A_ )  ,     A_ )

												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-3:] )  ,     [
												        self.model_tester.hidden_sizes[0],
												        self.model_tester.image_size // 4,
												        self.model_tester.image_size // 4,
												    ]  ,     )

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

							for model_class in self.all_model_classes:
												lowerCAmelCase						:    Dict     		=   True
												check_hidden_states_output(A_  ,     A_  ,     A_ )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCAmelCase						:    int     		=   True

												check_hidden_states_output(A_  ,     A_  ,     A_ )





		def 					_A							(							self			:		Optional[Any] ):
							if not self.model_tester.is_training:
												return

							lowerCAmelCase		,							lowerCAmelCase						:    str     		=   self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase						:    Optional[int]     		=   True

							for model_class in self.all_model_classes:
												if model_class in get_values(A_ ):
																	continue
												lowerCAmelCase						:    List[str]     		=   model_class(A_ )
												model.to(A_ )
												model.train()
												lowerCAmelCase						:    Dict     		=   self._prepare_for_class(A_  ,     A_  ,     return_labels=A_ )
												lowerCAmelCase						:    List[str]     		=   model(**A_ ).loss
												loss.backward()





		def 					_A							(							self			:		Union[str, Any] ):
							lowerCAmelCase		,							lowerCAmelCase						:    Optional[Any]     		=   self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCAmelCase						:    List[str]     		=   ['''pixel_values''']
												self.assertListEqual(arg_names[:1]  ,     A_ )





		@slow
		def 					_A							(							self			:		Dict ):
							for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCAmelCase						:    List[Any]     		=   PoolFormerModel.from_pretrained(A_ )
												self.assertIsNotNone(A_ )



def 			UpperCAmelCase__							(				):


					'''simple docstring'''

					lowerCAmelCase						:    int     		=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
					return image


@require_torch
class 						__magic_name__      (  unittest.TestCase			):





		@slow
		def 					_A							(							self			:		int ):
							lowerCAmelCase						:    Union[str, Any]     		=   PoolFormerImageProcessor()
							lowerCAmelCase						:    Optional[int]     		=   PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12''' ).to(A_ )

							lowerCAmelCase						:    List[str]     		=   image_processor(images=prepare_img()  ,     return_tensors='''pt''' ).to(A_ )

							# forward pass
							with torch.no_grad():
												lowerCAmelCase						:    Optional[Any]     		=   model(**A_ )

							# verify the logits
							lowerCAmelCase						:    Optional[Any]     		=   torch.Size((1, 1_0_0_0) )
							self.assertEqual(outputs.logits.shape  ,     A_ )

							lowerCAmelCase						:    Tuple     		=   torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2] ).to(A_ )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,     A_  ,     atol=1E-4 ) )





'''simple docstring'''

from __future__ import annotations

from pathlib import Path






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						tuple[int, int]							,		_SCREAMING_SNAKE_CASE     :						tuple[int, int]							)			->						int:







	"""simple docstring"""
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							)			->						bool:







	"""simple docstring"""
	lowerCAmelCase    =	(xa, ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	-vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	+vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return a > 0 and b > 0 and a + b < 1






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str = "p102_triangles.txt"							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	Path(_SCREAMING_SNAKE_CASE							).parent.joinpath(_SCREAMING_SNAKE_CASE							).read_text(encoding="""utf-8"""							)

	lowerCAmelCase    =	[]
	for line in data.strip().split("""\n"""							):
		triangles.append([int(_SCREAMING_SNAKE_CASE							) for number in line.split(""","""							)]							)

	lowerCAmelCase    =	0
	lowerCAmelCase    =	42

	for triangle in triangles:
		ret += contains_origin(*_SCREAMING_SNAKE_CASE							)

	return ret


if __name__ == "__main__":
				print(F'''{solution() = }''')