"""simple docstring"""





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_			(					_a):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	int     					=			self.config_class(**self.inputs_dict							)
             self.parent.assertTrue(hasattr(a	,    'hidden_sizes'							)							)
             self.parent.assertTrue(hasattr(a	,    'num_attention_heads'							)							)





class   UpperCAmelCase_			:


      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	,    ) ->      List[str]:
             lowercase__    :	List[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	Optional[int]     					=			image_size
             lowercase__    :	int     					=			num_channels
             lowercase__    :	Optional[Any]     					=			kernel_size
             lowercase__    :	List[str]     					=			stride
             lowercase__    :	Tuple     					=			padding
             lowercase__    :	int     					=			hidden_sizes
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	List[str]     					=			depths
             lowercase__    :	Optional[int]     					=			key_dim
             lowercase__    :	List[str]     					=			drop_path_rate
             lowercase__    :	int     					=			patch_size
             lowercase__    :	Union[str, Any]     					=			attention_ratio
             lowercase__    :	Any     					=			mlp_ratio
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	str     					=			[
                 ['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],
             ]
             lowercase__    :	int     					=			is_training
             lowercase__    :	Union[str, Any]     					=			use_labels
             lowercase__    :	Dict     					=			num_labels
             lowercase__    :	Tuple     					=			initializer_range


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	List[Any]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__    :	str     					=			None
             if self.use_labels:
                    lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size]	,    self.num_labels							)

             lowercase__    :	Optional[Any]     					=			self.get_config()
             return config, pixel_values, labels


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


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





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Dict										=						(
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      lowerCamelCase__	:					str										=						(
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      lowerCamelCase__	:					Union[str, Any]										=						False
      lowerCamelCase__	:					Tuple										=						False
      lowerCamelCase__	:					Optional[Any]										=						False
      lowerCamelCase__	:					Optional[int]										=						False
      lowerCamelCase__	:					List[str]										=						False


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	List[str]     					=			LevitModelTester(self							)
             lowercase__    :	List[str]     					=			ConfigTester(self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self							) ->      List[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 	_UpperCAmelCase (     self							) ->      Tuple:
             return


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


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


      @unittest.skip(reason='Levit does not output attentions'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__    :	List[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      def 	_UpperCAmelCase (     self							) ->      str:
             def check_hidden_states_output(a	,    a	,    a							):
                    lowercase__    :	Tuple     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

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

                    lowercase__    :	List[Any]     					=			outputs.hidden_states

                    lowercase__    :	Union[str, Any]     					=			len(self.model_tester.depths							) + 1
                    self.assertEqual(len(a							)	,    a							)

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

             lowercase__					,   lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	Optional[int]     					=			True
                    check_hidden_states_output(a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	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 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=False							) ->      Tuple:
             lowercase__    :	str     					=			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 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             if not self.model_tester.is_training:
                    return

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


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             lowercase__    :	Tuple     					=			False
             lowercase__    :	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
                    lowercase__    :	int     					=			model_class(a							)
                    model.gradient_checkpointing_enable()
                    model.to(a							)
                    model.train()
                    lowercase__    :	int     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    lowercase__    :	Optional[int]     					=			model(**a							).loss
                    loss.backward()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__					,   lowercase__    :	Tuple     					=			self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__    :	Tuple     					=			[
                 {'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"]}"""							):
                                  lowercase__    :	Any     					=			problem_type['title']
                                  lowercase__    :	List[str]     					=			problem_type['num_labels']

                                  lowercase__    :	Any     					=			model_class(a							)
                                  model.to(a							)
                                  model.train()

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

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

                                  lowercase__    :	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:
                                         lowercase__    :	str     					=			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 	_UpperCAmelCase (     self							) ->      str:
             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	Union[str, Any]     					=			LevitModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       return image






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


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


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
                 a							)

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

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[Any]     					=			model(**a							)

             # verify the logits
             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape	,    a							)

             lowercase__    :	Dict     					=			torch.tensor([1.0_448, -0.3_745, -1.8_317]							).to(a							)

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


"""simple docstring"""





import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
       from transformers.trainer import DEFAULT_CALLBACKS

       from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel


class   UpperCAmelCase_			(					_a):


      def __init__(     self							) ->      Any:
             lowercase__    :	Tuple     					=			[]


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_init_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[int]:
             self.events.append('on_train_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_train_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_epoch_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[Any]:
             self.events.append('on_epoch_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_step_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      str:
             self.events.append('on_step_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_evaluate'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Tuple:
             self.events.append('on_predict'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Union[str, Any]:
             self.events.append('on_save'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_log'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_prediction_step'							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	str     					=			tempfile.mkdtemp()


      def 	_UpperCAmelCase (     self							) ->      Dict:
             shutil.rmtree(self.output_dir							)


      def 	_UpperCAmelCase (     self	,    a=0	,    a=0	,    a=6_4	,    a=6_4	,    a=None	,    a=False	,    **a							) ->      int:
             # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
             # its set to False since the tests later on depend on its value.
             lowercase__    :	str     					=			RegressionDataset(length=a							)
             lowercase__    :	Any     					=			RegressionDataset(length=a							)
             lowercase__    :	Optional[Any]     					=			RegressionModelConfig(a=a	,    b=a							)
             lowercase__    :	Union[str, Any]     					=			RegressionPreTrainedModel(a							)

             lowercase__    :	Tuple     					=			TrainingArguments(self.output_dir	,    disable_tqdm=a	,    report_to=[]	,    **a							)
             return Trainer(
                 a	,    a	,    train_dataset=a	,    eval_dataset=a	,    callbacks=a	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Union[str, Any]:
             self.assertEqual(len(a							)	,    len(a							)							)

             # Order doesn't matter
             lowercase__    :	Optional[int]     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)
             lowercase__    :	Tuple     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)

             for cba, cba in zip(a	,    a							):
                    if isinstance(a	,    a							) and isinstance(a	,    a							):
                           self.assertEqual(a	,    a							)
                    elif isinstance(a	,    a							) and not isinstance(a	,    a							):
                           self.assertEqual(a	,    cba.__class__							)
                    elif not isinstance(a	,    a							) and isinstance(a	,    a							):
                           self.assertEqual(cba.__class__	,    a							)
                    else:
                           self.assertEqual(a	,    a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	Dict     					=			['on_init_end', 'on_train_begin']
             lowercase__    :	List[Any]     					=			0
             lowercase__    :	Optional[int]     					=			len(trainer.get_eval_dataloader()							)
             lowercase__    :	Tuple     					=			['on_prediction_step'] * len(trainer.get_eval_dataloader()							) + ['on_log', 'on_evaluate']
             for _ in range(trainer.state.num_train_epochs							):
                    expected_events.append('on_epoch_begin'							)
                    for _ in range(a							):
                           step += 1
                           expected_events += ["on_step_begin", "on_step_end"]
                           if step % trainer.args.logging_steps == 0:
                                  expected_events.append('on_log'							)
                           if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                                  expected_events += evaluation_events.copy()
                           if step % trainer.args.save_steps == 0:
                                  expected_events.append('on_save'							)
                    expected_events.append('on_epoch_end'							)
                    if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                           expected_events += evaluation_events.copy()
             expected_events += ["on_log", "on_train_end"]
             return expected_events


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	str     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # Callbacks passed at init are added to the default callbacks
             lowercase__    :	str     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             expected_callbacks.append(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
             lowercase__    :	List[Any]     					=			self.get_trainer(disable_tqdm=a							)
             lowercase__    :	Optional[Any]     					=			DEFAULT_CALLBACKS.copy() + [PrinterCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             lowercase__    :	List[str]     					=			self.get_trainer()

             # We can add, pop, or remove by class name
             trainer.remove_callback(a							)
             expected_callbacks.remove(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             lowercase__    :	Optional[Any]     					=			self.get_trainer()
             lowercase__    :	List[Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(cb.__class__	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             trainer.add_callback(a							)
             expected_callbacks.insert(0	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # We can also add, pop, or remove by instance
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	List[str]     					=			trainer.callback_handler.callbacks[0]
             trainer.remove_callback(a							)
             expected_callbacks.remove(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             lowercase__    :	Tuple     					=			self.get_trainer()
             lowercase__    :	Dict     					=			trainer.callback_handler.callbacks[0]
             lowercase__    :	Union[str, Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(a	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             trainer.add_callback(a							)
             expected_callbacks.insert(0	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             import warnings

             # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
             warnings.simplefilter(action='ignore'	,    category=a							)

             lowercase__    :	Optional[Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # Independent log/save/eval
             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    logging_steps=5							)
             trainer.train()
             lowercase__    :	int     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	Union[str, Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    save_steps=5							)
             trainer.train()
             lowercase__    :	Union[str, Any]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    eval_steps=5	,    evaluation_strategy='steps'							)
             trainer.train()
             lowercase__    :	Optional[int]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	int     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    evaluation_strategy='epoch'							)
             trainer.train()
             lowercase__    :	str     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # A bit of everything
             lowercase__    :	Any     					=			self.get_trainer(
                 callbacks=[MyTestTrainerCallback]	,    logging_steps=3	,    save_steps=1_0	,    eval_steps=5	,    evaluation_strategy='steps'	,    )
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # warning should be emitted for duplicated callbacks
             with patch('transformers.trainer_callback.logger.warning'							) as warn_mock:
                    lowercase__    :	str     					=			self.get_trainer(
                        callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]	,    )
                    assert str(a							) in warn_mock.call_args[0][0]

