

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 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					int       =	self.config_class(**self.inputs_dict    )
						self.parent.assertTrue(hasattr(A__					, """hidden_sizes"""    )    )
						self.parent.assertTrue(hasattr(A__					, """num_attention_heads"""    )    )



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=64					, A__=3					, A__=3					, A__=2					, A__=1					, A__=16					, A__=[128, 256, 384]					, A__=[4, 6, 8]					, A__=[2, 3, 4]					, A__=[16, 16, 16]					, A__=0					, A__=[2, 2, 2]					, A__=[2, 2, 2]					, A__=0.0_2					, A__=True					, A__=True					, A__=2					, ):
						A__							:					Dict       =	parent
						A__							:					str       =	batch_size
						A__							:					Dict       =	image_size
						A__							:					Optional[int]       =	num_channels
						A__							:					List[str]       =	kernel_size
						A__							:					List[str]       =	stride
						A__							:					Union[str, Any]       =	padding
						A__							:					int       =	hidden_sizes
						A__							:					Union[str, Any]       =	num_attention_heads
						A__							:					Union[str, Any]       =	depths
						A__							:					List[str]       =	key_dim
						A__							:					Any       =	drop_path_rate
						A__							:					List[Any]       =	patch_size
						A__							:					Optional[int]       =	attention_ratio
						A__							:					Optional[Any]       =	mlp_ratio
						A__							:					List[Any]       =	initializer_range
						A__							:					List[Any]       =	[
						    ["""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],
						]
						A__							:					int       =	is_training
						A__							:					List[str]       =	use_labels
						A__							:					Tuple       =	num_labels
						A__							:					int       =	initializer_range






					def 						__A    (				self    ):
						A__							:					str       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

						A__							:					Any       =	None
						if self.use_labels:
							A__							:					Dict       =	ids_tensor([self.batch_size]					, self.num_labels    )

						A__							:					str       =	self.get_config()
						return config, pixel_values, labels






					def 						__A    (				self    ):
						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 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Optional[int]       =	LevitModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Tuple       =	model(A__    )
						A__							:					str       =	(self.image_size, self.image_size)
						A__     ,     A__							:					Any       =	image_size[0], image_size[1]
						for _ in range(4    ):
							A__							:					List[Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1    )
							A__							:					Union[str, Any]       =	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 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Optional[Any]       =	self.num_labels
						A__							:					Any       =	LevitForImageClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[Any]       =	model(A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.prepare_config_and_inputs()
						A__     ,     A__     ,     A__							:					List[Any]       =	config_and_inputs
						A__							:					List[Any]       =	{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_torch
class 					_a		(__magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= (
					    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__:     Union[str, Any]													= (
					    {
					        '''feature-extraction''': LevitModel,
					        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__:     List[str]													= False
					UpperCAmelCase__:     List[Any]													= False
					UpperCAmelCase__:     int													= False
					UpperCAmelCase__:     Optional[int]													= False
					UpperCAmelCase__:     Dict													= False






					def 						__A    (				self    ):
						A__							:					Dict       =	LevitModelTester(self    )
						A__							:					Optional[int]       =	ConfigTester(self					, config_class=A__					, has_text_modality=A__					, hidden_size=37    )






					def 						__A    (				self    ):
						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    ):
						return






					@unittest.skip(reason="""Levit does not use inputs_embeds"""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""Levit does not support input and output embeddings"""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""Levit does not output attentions"""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						A__     ,     A__							:					Any       =	self.model_tester.prepare_config_and_inputs_for_common()

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

							A__							:					Optional[Any]       =	["""pixel_values"""]
							self.assertListEqual(arg_names[:1]					, A__    )






					def 						__A    (				self    ):
						def check_hidden_states_output(A__					, A__					, A__    ):
							A__							:					str       =	model_class(A__    )
							model.to(A__    )
							model.eval()

							with torch.no_grad():
								A__							:					List[Any]       =	model(**self._prepare_for_class(A__					, A__    )    )

							A__							:					List[Any]       =	outputs.hidden_states

							A__							:					int       =	len(self.model_tester.depths    ) + 1
							self.assertEqual(len(A__    )					, A__    )

							A__							:					List[Any]       =	(self.model_tester.image_size, self.model_tester.image_size)
							A__     ,     A__							:					List[str]       =	image_size[0], image_size[1]
							for _ in range(4    ):
								A__							:					str       =	floor(
								    (
								        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
								        / self.model_tester.stride
								    )
								    + 1    )
								A__							:					List[str]       =	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],
							    ]					, )

						A__     ,     A__							:					str       =	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:					Union[str, Any]       =	True
							check_hidden_states_output(A__					, A__					, A__    )

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

							check_hidden_states_output(A__					, A__					, A__    )






					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self					, A__					, A__					, A__=False    ):
						A__							:					Tuple       =	super()._prepare_for_class(A__					, A__					, return_labels=A__    )

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

						return inputs_dict






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__    )






					def 						__A    (				self    ):
						if not self.model_tester.is_training:
							return

						A__     ,     A__							:					Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
						A__							:					Dict       =	True

						for model_class in self.all_model_classes:
							# LevitForImageClassificationWithTeacher supports inference-only
							if (
							    model_class in get_values(A__    )
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
								continue
							A__							:					Dict       =	model_class(A__    )
							model.to(A__    )
							model.train()
							A__							:					Union[str, Any]       =	self._prepare_for_class(A__					, A__					, return_labels=A__    )
							A__							:					List[Any]       =	model(**A__    ).loss
							loss.backward()






					def 						__A    (				self    ):
						A__     ,     A__							:					Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
							return

						A__							:					Dict       =	False
						A__							:					Any       =	True

						for model_class in self.all_model_classes:
							if model_class in get_values(A__    ) or not model_class.supports_gradient_checkpointing:
								continue
							# LevitForImageClassificationWithTeacher supports inference-only
							if model_class.__name__ == "LevitForImageClassificationWithTeacher":
								continue
							A__							:					List[Any]       =	model_class(A__    )
							model.gradient_checkpointing_enable()
							model.to(A__    )
							model.train()
							A__							:					Optional[Any]       =	self._prepare_for_class(A__					, A__					, return_labels=A__    )
							A__							:					Any       =	model(**A__    ).loss
							loss.backward()






					def 						__A    (				self    ):
						A__     ,     A__							:					Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

						A__							:					Union[str, Any]       =	[
						    {"""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(A__    ),
							    ]
							    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
							):
								continue

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

									A__							:					List[Any]       =	model_class(A__    )
									model.to(A__    )
									model.train()

									A__							:					Optional[Any]       =	self._prepare_for_class(A__					, A__					, return_labels=A__    )

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

									A__							:					Dict       =	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=A__    ) as warning_list:
										A__							:					Dict       =	model(**A__    ).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 						__A    (				self    ):
						for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:					Union[str, Any]       =	LevitModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )



def 		UpperCamelCase      ()  ->   Dict:
	A__							:					Union[str, Any]       =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
	return image



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


					'''simple docstring'''






					@cached_property
					def 						__A    (				self    ):
						return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






					@slow
					def 						__A    (				self    ):
						A__							:					Tuple       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
						    A__    )

						A__							:					Optional[int]       =	self.default_image_processor
						A__							:					List[Any]       =	prepare_img()
						A__							:					Dict       =	image_processor(images=A__					, return_tensors="""pt"""    ).to(A__    )

						# forward pass
						with torch.no_grad():
							A__							:					Optional[int]       =	model(**A__    )

						# verify the logits
						A__							:					List[Any]       =	torch.Size((1, 1000)    )
						self.assertEqual(outputs.logits.shape					, A__    )

						A__							:					Any       =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]    ).to(A__    )

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






import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[Any]						 =    [file for file in filepaths if file != file.lower()]
if upper_files:
	print(f'''{len(upper_files)} files contain uppercase characters:''')
	print('\n'.join(upper_files) + '\n')

A_ :				Tuple						 =    [file for file in filepaths if ' ' in file]
if space_files:
	print(f'''{len(space_files)} files contain space characters:''')
	print('\n'.join(space_files) + '\n')

A_ :				Any						 =    [file for file in filepaths if '-' in file]
if hyphen_files:
	print(f'''{len(hyphen_files)} files contain hyphen characters:''')
	print('\n'.join(hyphen_files) + '\n')

A_ :				List[str]						 =    [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(f'''{len(nodir_files)} files are not in a directory:''')
	print('\n'.join(nodir_files) + '\n')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



