





from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__ = None      ,							a__ = None      ,							a__ = True      ,							a__ = None      ,							a__ = False      ,							a__ = None      ,							a__ = True      ,							a__ = "arrow"      ,							**a__      ,							):



             """simple docstring"""





             super().__init__(
                 split=a__      ,							features=a__      ,							cache_dir=a__      ,							keep_in_memory=a__      ,							streaming=a__      ,							**a__      ,							)
             _lowerCamelCase							:     Optional[Any]	=		load_from_cache_file
             _lowerCamelCase							:     Any	=		file_format
             _lowerCamelCase							:     Any	=		Spark(
                 df=a__      ,							features=a__      ,							cache_dir=a__      ,							working_dir=a__      ,							**a__      ,							)



      def 				__snake_case			(		self):



             """simple docstring"""





             if self.streaming:
                    return self.builder.as_streaming_dataset(split=self.split)
             _lowerCamelCase							:     int	=		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
             self.builder.download_and_prepare(
                 download_mode=a__      ,							file_format=self._file_format      ,							)
             return self.builder.as_dataset(split=self.split)








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       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		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__=0.02      ,							a__=True      ,							a__=True      ,							a__=2      ,							):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

             _lowerCamelCase							:     List[Any]	=		self.get_config()
             return config, pixel_values, labels


      def 				__snake_case			(		self):



             """simple docstring"""





             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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		LevitModel(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[str]	=		model(a__)
             _lowerCamelCase							:     List[str]	=		(self.image_size, self.image_size)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		image_size[0], image_size[1]
             for _ in range(4):
                    _lowerCamelCase							:     int	=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
                    _lowerCamelCase							:     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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.num_labels
             _lowerCamelCase							:     Dict	=		LevitForImageClassification(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[Any]	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels))



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.prepare_config_and_inputs()
             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		config_and_inputs
             _lowerCamelCase							:     List[str]	=		{'''pixel_values''': pixel_values}
             return config, inputs_dict







@require_torch
class 			__A       (      lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		LevitModelTester(self)
             _lowerCamelCase							:     Tuple	=		ConfigTester(self      ,							config_class=a__      ,							has_text_modality=a__      ,							hidden_size=37)


      def 				__snake_case			(		self):



             """simple docstring"""





             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 				__snake_case			(		self):



             """simple docstring"""





             return


      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''Levit does not output attentions''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		self.model_tester.prepare_config_and_inputs_for_common()

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             def check_hidden_states_output(a__      ,							a__      ,							a__):
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.to(a__)
                    model.eval()

                    with torch.no_grad():
                           _lowerCamelCase							:     Any	=		model(**self._prepare_for_class(a__      ,							a__))

                    _lowerCamelCase							:     int	=		outputs.hidden_states

                    _lowerCamelCase							:     List[str]	=		len(self.model_tester.depths) + 1
                    self.assertEqual(len(a__)      ,							a__)

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

             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     List[Any]	=		True
                    check_hidden_states_output(a__      ,							a__      ,							a__)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _lowerCamelCase							:     List[str]	=		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 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__=False):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             if not self.model_tester.is_training:
                    return

             _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase							:     Optional[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
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     Any	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Tuple	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             _lowerCamelCase							:     Union[str, Any]	=		False
             _lowerCamelCase							:     List[str]	=		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
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.gradient_checkpointing_enable()
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Any	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs_for_common()

             _lowerCamelCase							:     Dict	=		[
                 {'''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']}"""):
                                  _lowerCamelCase							:     Tuple	=		problem_type['''title''']
                                  _lowerCamelCase							:     Tuple	=		problem_type['''num_labels''']

                                  _lowerCamelCase							:     int	=		model_class(a__)
                                  model.to(a__)
                                  model.train()

                                  _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)

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

                                  _lowerCamelCase							:     Tuple	=		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:
                                         _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     int	=		LevitModel.from_pretrained(a__)
                    self.assertIsNotNone(a__)




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







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




      """simple docstring"""







      @cached_property
      def 				__snake_case			(		self):



             """simple docstring"""





             return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
                 a__)

             _lowerCamelCase							:     int	=		self.default_image_processor
             _lowerCamelCase							:     int	=		prepare_img()
             _lowerCamelCase							:     str	=		image_processor(images=a__      ,							return_tensors='''pt''').to(a__)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase							:     int	=		model(**a__)

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

             _lowerCamelCase							:     Optional[Any]	=		torch.tensor([1.0448, -0.3745, -1.8317]).to(a__)

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

