




import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType







@require_torch
class       UpperCAmelCase:



	"""simple docstring"""






	a	:					str         =     ()
	a	:					Any         =     True

	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__  :       Tuple   					=							self.config_class

								# test default config
								lowercase__  :       List[Any]   					=							config_class()
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)
								lowercase__  :       str   					=							["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(config.depths				) + 1				)]
								self.assertEqual(config.stage_names       ,      __SCREAMING_SNAKE_CASE				)
								self.assertTrue(set(config.out_features				).issubset(set(config.stage_names				)				)				)

								# Test out_features and out_indices are correctly set
								# out_features and out_indices both None
								lowercase__  :       List[Any]   					=							config_class(out_features=__SCREAMING_SNAKE_CASE       ,      out_indices=__SCREAMING_SNAKE_CASE				)
								self.assertEqual(config.out_features       ,      [config.stage_names[-1]]				)
								self.assertEqual(config.out_indices       ,      [len(config.stage_names				) - 1]				)

								# out_features and out_indices both set
								lowercase__  :       int   					=							config_class(out_features=["stem", "stage1"]       ,      out_indices=[0, 1]				)
								self.assertEqual(config.out_features       ,      ["stem", "stage1"]				)
								self.assertEqual(config.out_indices       ,      [0, 1]				)

								# Only out_features set
								lowercase__  :       Optional[Any]   					=							config_class(out_features=["stage1", "stage3"]				)
								self.assertEqual(config.out_features       ,      ["stage1", "stage3"]				)
								self.assertEqual(config.out_indices       ,      [1, 3]				)

								# Only out_indices set
								lowercase__  :       int   					=							config_class(out_indices=[0, 2]				)
								self.assertEqual(config.out_features       ,      [config.stage_names[0], config.stage_names[2]]				)
								self.assertEqual(config.out_indices       ,      [0, 2]				)

								# Error raised when out_indices do not correspond to out_features
								with self.assertRaises(__SCREAMING_SNAKE_CASE				):
															lowercase__  :       int   					=							config_class(out_features=["stage1", "stage2"]       ,      out_indices=[0, 2]				)

	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								lowercase__					,   lowercase__  :       int   					=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowercase__  :       Optional[Any]   					=							model_class(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       List[str]   					=							inspect.signature(model.forward				)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															lowercase__  :       Optional[Any]   					=							[*signature.parameters.keys()]
															lowercase__  :       str   					=							["pixel_values"]
															self.assertListEqual(arg_names[:1]       ,      __SCREAMING_SNAKE_CASE				)

	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


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

								for model_class in self.all_model_classes:
															lowercase__  :       str   					=							model_class(__SCREAMING_SNAKE_CASE				)
															self.assertEqual(len(model.channels				)       ,      len(config.out_features				)				)
															lowercase__  :       int   					=							model.num_features
															lowercase__  :       Union[str, Any]   					=							[config.stage_names.index(__SCREAMING_SNAKE_CASE				) for feat in config.out_features]
															lowercase__  :       Optional[int]   					=							[num_features[idx] for idx in out_indices]
															self.assertListEqual(model.channels       ,      __SCREAMING_SNAKE_CASE				)

															lowercase__  :       Optional[Any]   					=							copy.deepcopy(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       Union[str, Any]   					=							None
															lowercase__  :       int   					=							model_class(__SCREAMING_SNAKE_CASE				)
															self.assertEqual(len(model.channels				)       ,      1				)
															self.assertListEqual(model.channels       ,      [num_features[-1]]				)

															lowercase__  :       int   					=							copy.deepcopy(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       Any   					=							None
															lowercase__  :       Optional[Any]   					=							model_class(__SCREAMING_SNAKE_CASE				)
															self.assertEqual(len(model.channels				)       ,      1				)
															self.assertListEqual(model.channels       ,      [num_features[-1]]				)

	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								lowercase__					,   lowercase__  :       int   					=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowercase__  :       Union[str, Any]   					=							model_class(__SCREAMING_SNAKE_CASE				)
															model.to(__SCREAMING_SNAKE_CASE				)
															model.eval()
															lowercase__  :       Any   					=							model(**__SCREAMING_SNAKE_CASE				)

															self.assertEqual(len(result.feature_maps				)       ,      len(config.out_features				)				)
															self.assertEqual(len(model.channels				)       ,      len(config.out_features				)				)
															self.assertEqual(len(result.feature_maps				)       ,      len(config.out_indices				)				)
															self.assertEqual(len(model.channels				)       ,      len(config.out_indices				)				)

															# Check output of last stage is taken if out_features=None, out_indices=None
															lowercase__  :       Tuple   					=							copy.deepcopy(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       int   					=							None
															lowercase__  :       Tuple   					=							model_class(__SCREAMING_SNAKE_CASE				)
															model.to(__SCREAMING_SNAKE_CASE				)
															model.eval()
															lowercase__  :       Union[str, Any]   					=							model(**__SCREAMING_SNAKE_CASE				)

															self.assertEqual(len(result.feature_maps				)       ,      1				)
															self.assertEqual(len(model.channels				)       ,      1				)

															lowercase__  :       Optional[Any]   					=							copy.deepcopy(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       Union[str, Any]   					=							None
															lowercase__  :       Dict   					=							model_class(__SCREAMING_SNAKE_CASE				)
															model.to(__SCREAMING_SNAKE_CASE				)
															model.eval()
															lowercase__  :       int   					=							model(**__SCREAMING_SNAKE_CASE				)

															self.assertEqual(len(result.feature_maps				)       ,      1				)
															self.assertEqual(len(model.channels				)       ,      1				)

															# Check backbone can be initialized with fresh weights
															lowercase__  :       Optional[int]   					=							copy.deepcopy(__SCREAMING_SNAKE_CASE				)
															lowercase__  :       str   					=							False
															lowercase__  :       Any   					=							model_class(__SCREAMING_SNAKE_CASE				)
															model.to(__SCREAMING_SNAKE_CASE				)
															model.eval()
															lowercase__  :       str   					=							model(**__SCREAMING_SNAKE_CASE				)

	def     __a	(     self				)					->			Any:





								"""simple docstring"""


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

								for backbone_class in self.all_model_classes:
															lowercase__  :       Dict   					=							backbone_class(__SCREAMING_SNAKE_CASE				)

															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "backbone_type"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "stage_names"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "num_features"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "out_indices"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "out_features"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "out_feature_channels"				)				)
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      "channels"				)				)

															self.assertIsInstance(backbone.backbone_type       ,      __SCREAMING_SNAKE_CASE				)
															# Verify num_features has been initialized in the backbone init
															self.assertIsNotNone(backbone.num_features				)
															self.assertTrue(len(backbone.channels				) == len(backbone.out_indices				)				)
															self.assertTrue(len(backbone.stage_names				) == len(backbone.num_features				)				)
															self.assertTrue(len(backbone.channels				) <= len(backbone.num_features				)				)
															self.assertTrue(len(backbone.out_feature_channels				) == len(backbone.stage_names				)				)







	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								lowercase__					,   lowercase__  :       str   					=							self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__  :       Optional[int]   					=							inputs_dict["pixel_values"].shape[0]

								for backbone_class in self.all_model_classes:
															lowercase__  :       Optional[int]   					=							backbone_class(__SCREAMING_SNAKE_CASE				)
															backbone.to(__SCREAMING_SNAKE_CASE				)
															backbone.eval()

															lowercase__  :       Union[str, Any]   					=							backbone(**__SCREAMING_SNAKE_CASE				)

															# Test default outputs and verify feature maps
															self.assertIsInstance(outputs.feature_maps       ,      __SCREAMING_SNAKE_CASE				)
															self.assertTrue(len(outputs.feature_maps				) == len(backbone.channels				)				)
															for feature_map, n_channels in zip(outputs.feature_maps       ,      backbone.channels				):
																						self.assertTrue(feature_map.shape[:2]       ,      (batch_size, n_channels)				)
															self.assertIsNone(outputs.hidden_states				)
															self.assertIsNone(outputs.attentions				)

															# Test output_hidden_states=True
															lowercase__  :       List[Any]   					=							backbone(**__SCREAMING_SNAKE_CASE       ,      output_hidden_states=__SCREAMING_SNAKE_CASE				)
															self.assertIsNotNone(outputs.hidden_states				)
															self.assertTrue(len(outputs.hidden_states				)       ,      len(backbone.stage_names				)				)
															for hidden_state, n_channels in zip(outputs.hidden_states       ,      backbone.channels				):
																						self.assertTrue(hidden_state.shape[:2]       ,      (batch_size, n_channels)				)

															# Test output_attentions=True
															if self.has_attentions:
																						lowercase__  :       Tuple   					=							backbone(**__SCREAMING_SNAKE_CASE       ,      output_attentions=__SCREAMING_SNAKE_CASE				)
																						self.assertIsNotNone(outputs.attentions				)






"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

