






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor


class   __SCREAMING_SNAKE_CASE(	a_	):
							def lowerCAmelCase_ (     self:      Dict						)		->		List[str]:
											snake_case__									=     self.config_class(**self.inputs_dict						)
											self.parent.assertTrue(hasattr(UpperCamelCase       ,	'hidden_sizes'						)						)
											self.parent.assertTrue(hasattr(UpperCamelCase       ,	'num_attention_heads'						)						)


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Any       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Union[str, Any]=13       ,	UpperCamelCase:      Optional[int]=64       ,	UpperCamelCase:      Tuple=3       ,	UpperCamelCase:      Dict=3       ,	UpperCamelCase:      Union[str, Any]=2       ,	UpperCamelCase:      List[str]=1       ,	UpperCamelCase:      Union[str, Any]=16       ,	UpperCamelCase:      int=[1_28, 2_56, 3_84]       ,	UpperCamelCase:      Union[str, Any]=[4, 6, 8]       ,	UpperCamelCase:      Optional[int]=[2, 3, 4]       ,	UpperCamelCase:      Optional[Any]=[16, 16, 16]       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      int=[2, 2, 2]       ,	UpperCamelCase:      int=[2, 2, 2]       ,	UpperCamelCase:      Optional[int]=0.02       ,	UpperCamelCase:      Optional[int]=True       ,	UpperCamelCase:      Dict=True       ,	UpperCamelCase:      Optional[int]=2       ,	)		->		int:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     image_size
											snake_case__									=     num_channels
											snake_case__									=     kernel_size
											snake_case__									=     stride
											snake_case__									=     padding
											snake_case__									=     hidden_sizes
											snake_case__									=     num_attention_heads
											snake_case__									=     depths
											snake_case__									=     key_dim
											snake_case__									=     drop_path_rate
											snake_case__									=     patch_size
											snake_case__									=     attention_ratio
											snake_case__									=     mlp_ratio
											snake_case__									=     initializer_range
											snake_case__									=     [
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     num_labels
											snake_case__									=     initializer_range
							def lowerCAmelCase_ (     self:      int						)		->		List[str]:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case__									=     None
											if self.use_labels:
															snake_case__									=     ids_tensor([self.batch_size]       ,	self.num_labels						)

											snake_case__									=     self.get_config()
											return config, pixel_values, labels
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[str]:
											return LevitConfig(
											    image_size=self.image_size       ,	num_channels=self.num_channels       ,	kernel_size=self.kernel_size       ,	stride=self.stride       ,	padding=self.padding       ,	patch_size=self.patch_size       ,	hidden_sizes=self.hidden_sizes       ,	num_attention_heads=self.num_attention_heads       ,	depths=self.depths       ,	key_dim=self.key_dim       ,	drop_path_rate=self.drop_path_rate       ,	mlp_ratio=self.mlp_ratio       ,	attention_ratio=self.attention_ratio       ,	initializer_range=self.initializer_range       ,	down_ops=self.down_ops       ,	)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Union[str, Any]						)		->		Any:
											snake_case__									=     LevitModel(config=UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()
											snake_case__									=     model(UpperCamelCase						)
											snake_case__									=     (self.image_size, self.image_size)
											snake_case__							,						snake_case__									=     image_size[0], image_size[1]
											for _ in range(4						):
															snake_case__									=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
															snake_case__									=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
											self.parent.assertEqual(
											    result.last_hidden_state.shape       ,	(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])       ,	)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[Any]						)		->		Tuple:
											snake_case__									=     self.num_labels
											snake_case__									=     LevitForImageClassification(UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()
											snake_case__									=     model(UpperCamelCase       ,	labels=UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											snake_case__									=     self.prepare_config_and_inputs()
											snake_case__							,						snake_case__							,						snake_case__									=     config_and_inputs
											snake_case__									=     {'pixel_values': pixel_values}
											return config, inputs_dict


@require_torch
class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase     =       (
							    {
							        "feature-extraction": LevitModel,
							        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											snake_case__									=     LevitModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=UpperCamelCase       ,	has_text_modality=UpperCamelCase       ,	hidden_size=37						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Tuple:
											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 lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											return
							@unittest.skip(reason='Levit does not use inputs_embeds'						)
							def lowerCAmelCase_ (     self:      str						)		->		Dict:
											pass
							@unittest.skip(reason='Levit does not support input and output embeddings'						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		str:
											pass
							@unittest.skip(reason='Levit does not output attentions'						)
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											pass
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     model_class(UpperCamelCase						)
															snake_case__									=     inspect.signature(model.forward						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case__									=     [*signature.parameters.keys()]

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		int:
											def check_hidden_states_output(UpperCamelCase:      List[str]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[Any]						):
															snake_case__									=     model_class(UpperCamelCase						)
															model.to(UpperCamelCase						)
															model.eval()

															with torch.no_grad():
																			snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)

															snake_case__									=     outputs.hidden_states

															snake_case__									=     len(self.model_tester.depths						) + 1
															self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

															snake_case__									=     (self.model_tester.image_size, self.model_tester.image_size)
															snake_case__							,						snake_case__									=     image_size[0], image_size[1]
															for _ in range(4						):
																			snake_case__									=     floor(
																			    (
																			        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																			        / self.model_tester.stride
																			    )
																			    + 1						)
																			snake_case__									=     floor(
																			    (
																			        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																			        / self.model_tester.stride
																			    )
																			    + 1						)
															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)       ,	[
															        height * width,
															        self.model_tester.hidden_sizes[0],
															    ]       ,	)

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     True
															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

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

															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[str]:
											pass
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[int]=False						)		->		Any:
											snake_case__									=     super()._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)

											if return_labels:
															if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																			del inputs_dict["labels"]

											return inputs_dict
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Dict:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int						)		->		Dict:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Tuple:
											if not self.model_tester.is_training:
															return

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__									=     True

											for model_class in self.all_model_classes:
															# LevitForImageClassificationWithTeacher supports inference-only
															if (
															    model_class in get_values(UpperCamelCase						)
															    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
															):
																			continue
															snake_case__									=     model_class(UpperCamelCase						)
															model.to(UpperCamelCase						)
															model.train()
															snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)
															snake_case__									=     model(**UpperCamelCase						).loss
															loss.backward()
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[int]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
															return

											snake_case__									=     False
											snake_case__									=     True

											for model_class in self.all_model_classes:
															if model_class in get_values(UpperCamelCase						) or not model_class.supports_gradient_checkpointing:
																			continue
															# LevitForImageClassificationWithTeacher supports inference-only
															if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																			continue
															snake_case__									=     model_class(UpperCamelCase						)
															model.gradient_checkpointing_enable()
															model.to(UpperCamelCase						)
															model.train()
															snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)
															snake_case__									=     model(**UpperCamelCase						).loss
															loss.backward()
							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case__									=     [
											    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
											    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
											    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
											]

											for model_class in self.all_model_classes:
															if (
															    model_class
															    not in [
															        *get_values(UpperCamelCase						),
															    ]
															    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
															):
																			continue

															for problem_type in problem_types:
																			with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''						):
																							snake_case__									=     problem_type['title']
																							snake_case__									=     problem_type['num_labels']

																							snake_case__									=     model_class(UpperCamelCase						)
																							model.to(UpperCamelCase						)
																							model.train()

																							snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase       ,	return_labels=UpperCamelCase						)

																							if problem_type["num_labels"] > 1:
																											snake_case__									=     inputs['labels'].unsqueeze(1						).repeat(1       ,	problem_type['num_labels']						)

																							snake_case__									=     inputs['labels'].to(problem_type['dtype']						)

																							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																							# they have the same size." which is a symptom something in wrong for the regression problem.
																							# See https://github.com/huggingface/transformers/issues/11780
																							with warnings.catch_warnings(record=UpperCamelCase						) as warning_list:
																											snake_case__									=     model(**UpperCamelCase						).loss
																							for w in warning_list:
																											if "Using a target size that is different to the input size" in str(w.message						):
																															raise ValueError(
																															    F'''Something is going wrong in the regression problem: intercepted {w.message}'''						)

																							loss.backward()


							@slow
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     LevitModel.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)


def      a_ (      )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
				return image


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      List[str]						)		->		int:
											return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)


							@slow
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[Any]:
											snake_case__									=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
											    UpperCamelCase						)

											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='pt'						).to(UpperCamelCase						)

											# forward pass
											with torch.no_grad():
															snake_case__									=     model(**UpperCamelCase						)

											# verify the logits
											snake_case__									=     torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)

											snake_case__									=     torch.tensor([1.0_448, -0.3_745, -1.8_317]						).to(UpperCamelCase						)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




