

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 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Union[str, Any]   =						self.config_class(**self.inputs_dict			)
												self.parent.assertTrue(hasattr(_a			,				'''hidden_sizes'''			)			)
												self.parent.assertTrue(hasattr(_a			,				'''num_attention_heads'''			)			)



class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=6_4			,				_a=3			,				_a=3			,				_a=2			,				_a=1			,				_a=1_6			,				_a=[1_2_8, 2_5_6, 3_8_4]			,				_a=[4, 6, 8]			,				_a=[2, 3, 4]			,				_a=[1_6, 1_6, 1_6]			,				_a=0			,				_a=[2, 2, 2]			,				_a=[2, 2, 2]			,				_a=0.02			,				_a=True			,				_a=True			,				_a=2			,				)       ->       Tuple:
												_a      :       Optional[int]   =						parent
												_a      :       Optional[Any]   =						batch_size
												_a      :       Any   =						image_size
												_a      :       int   =						num_channels
												_a      :       str   =						kernel_size
												_a      :       Dict   =						stride
												_a      :       List[Any]   =						padding
												_a      :       Union[str, Any]   =						hidden_sizes
												_a      :       str   =						num_attention_heads
												_a      :       Optional[int]   =						depths
												_a      :       Any   =						key_dim
												_a      :       Any   =						drop_path_rate
												_a      :       List[str]   =						patch_size
												_a      :       str   =						attention_ratio
												_a      :       Union[str, Any]   =						mlp_ratio
												_a      :       Any   =						initializer_range
												_a      :       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      :       Any   =						is_training
												_a      :       Tuple   =						use_labels
												_a      :       str   =						num_labels
												_a      :       List[Any]   =						initializer_range


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Optional[int]   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       int   =						None
												if self.use_labels:
																	_a      :       Tuple   =						ids_tensor([self.batch_size]			,				self.num_labels			)

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


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




							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       List[str]   =						self.prepare_config_and_inputs()
												_a ,   _a ,   _a      :       int   =						config_and_inputs
												_a      :       Tuple   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__			:		int             =       (
							    {
							        "feature-extraction": LevitModel,
							        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Any             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		List[str]             =       False
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       str   =						LevitModelTester(self			)
												_a      :       Optional[int]   =						ConfigTester(self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			)


							def 							__lowercase							(     self			)       ->       str:
												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 							__lowercase							(     self			)       ->       int:
												return


							@unittest.skip(reason='''Levit does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


							@unittest.skip(reason='''Levit does not support input and output embeddings'''			)
							def 							__lowercase							(     self			)       ->       int:
												pass


							@unittest.skip(reason='''Levit does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass


							def 							__lowercase							(     self			)       ->       Tuple:
												_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      :       Union[str, Any]   =						inspect.signature(model.forward			)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	_a      :       Optional[int]   =						[*signature.parameters.keys()]

																	_a      :       Union[str, Any]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

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

																	_a      :       str   =						outputs.hidden_states

																	_a      :       List[Any]   =						len(self.model_tester.depths			) + 1
																	self.assertEqual(len(_a			)			,				_a			)

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

												for model_class in self.all_model_classes:
																	_a      :       Tuple   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_a      :       Union[str, Any]   =						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 							__lowercase							(     self			)       ->       Tuple:
												pass


							def 							__lowercase							(     self			,				_a			,				_a			,				_a=False			)       ->       str:
												_a      :       List[Any]   =						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 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Dict   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												if not self.model_tester.is_training:
																	return

												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()
												_a      :       Union[str, Any]   =						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      :       int   =						model_class(_a			)
																	model.to(_a			)
																	model.train()
																	_a      :       List[str]   =						self._prepare_for_class(_a			,				_a			,				return_labels=_a			)
																	_a      :       Optional[int]   =						model(**_a			).loss
																	loss.backward()


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a ,   _a      :       Any   =						self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																	return

												_a      :       Any   =						False
												_a      :       Union[str, 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      :       Dict   =						model_class(_a			)
																	model.gradient_checkpointing_enable()
																	model.to(_a			)
																	model.train()
																	_a      :       Dict   =						self._prepare_for_class(_a			,				_a			,				return_labels=_a			)
																	_a      :       Optional[Any]   =						model(**_a			).loss
																	loss.backward()


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a ,   _a      :       Optional[Any]   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       int   =						[
												    {'''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      :       Optional[int]   =						problem_type['''title''']
																											_a      :       Optional[Any]   =						problem_type['''num_labels''']

																											_a      :       Optional[Any]   =						model_class(_a			)
																											model.to(_a			)
																											model.train()

																											_a      :       List[Any]   =						self._prepare_for_class(_a			,				_a			,				return_labels=_a			)

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

																											_a      :       Union[str, Any]   =						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      :       List[Any]   =						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 							__lowercase							(     self			)       ->       Any:
												for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       int   =						LevitModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""

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







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       List[str]   =						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(
												    _a			)

												_a      :       int   =						self.default_image_processor
												_a      :       int   =						prepare_img()
												_a      :       List[str]   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Union[str, Any]   =						model(**_a			)

												# verify the logits
												_a      :       int   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       List[Any]   =						torch.tensor([1.0448, -0.3745, -1.8317]			).to(_a			)

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







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

