
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
					from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=32    ,				A_=3    ,				A_=4    ,				A_=[10, 20, 30, 40]    ,				A_=[2, 2, 3, 2]    ,				A_=True    ,				A_=True    ,				A_=37    ,				A_="gelu"    ,				A_=10    ,				A_=0.02    ,				A_=["stage2", "stage3", "stage4"]    ,				A_=[2, 3, 4]    ,				A_=None    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					num_stages
				UpperCamelCase          =					hidden_sizes
				UpperCamelCase          =					depths
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					out_features
				UpperCamelCase          =					out_indices
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.num_labels	)

				UpperCamelCase          =					self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				return ConvNextVaConfig(
				    num_channels=self.num_channels    ,				hidden_sizes=self.hidden_sizes    ,				depths=self.depths    ,				num_stages=self.num_stages    ,				hidden_act=self.hidden_act    ,				is_decoder=A_    ,				initializer_range=self.initializer_range    ,				out_features=self.out_features    ,				out_indices=self.out_indices    ,				num_labels=self.num_labels    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					ConvNextVaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					ConvNextVaForImageClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					ConvNextVaBackbone(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)

				# verify hidden states
				self.parent.assertEqual(len(result.feature_maps	)    ,				len(config.out_features	)	)
				self.parent.assertListEqual(list(result.feature_maps[0].shape	)    ,				[self.batch_size, self.hidden_sizes[1], 4, 4]	)

				# verify channels
				self.parent.assertEqual(len(model.channels	)    ,				len(config.out_features	)	)
				self.parent.assertListEqual(model.channels    ,				config.hidden_sizes[1:]	)

				# verify backbone works with out_features=None
				UpperCamelCase          =					None
				UpperCamelCase          =					ConvNextVaBackbone(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps	)    ,				1	)
				self.parent.assertListEqual(list(result.feature_maps[0].shape	)    ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

				# verify channels
				self.parent.assertEqual(len(model.channels	)    ,				1	)
				self.parent.assertListEqual(model.channels    ,				[config.hidden_sizes[-1]]	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values, 'labels': labels}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[int]     					=    (
		    (
		        ConvNextVaModel,
		        ConvNextVaForImageClassification,
		        ConvNextVaBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase	:	int     					=    (
		    {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase	:	List[Any]     					=    False
		__lowercase	:	int     					=    False
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	List[str]     					=    False
		__lowercase	:	str     					=    False



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					ConvNextVaModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> str:
				"""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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				return



		@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'	)
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				pass



		@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

				for model_class in self.all_model_classes:
						UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_with_labels()
						UpperCamelCase          =					True

						if model_class.__name__ in [
						    *get_values(A_	),
						    *get_values(A_	),
						]:
								continue

						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.train()
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_    ,				return_labels=A_	)
						UpperCamelCase          =					model(**A_	).loss
						loss.backward()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

				for model_class in self.all_model_classes:
						UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_with_labels()
						UpperCamelCase          =					False
						UpperCamelCase          =					True

						if (
						    model_class.__name__
						    in [*get_values(A_	), *get_values(A_	)]
						    or not model_class.supports_gradient_checkpointing
						):
								continue

						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.gradient_checkpointing_enable()
						model.train()
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_    ,				return_labels=A_	)
						UpperCamelCase          =					model(**A_	).loss
						loss.backward()



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase          =					['pixel_values']
						self.assertListEqual(arg_names[:1]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				def check_hidden_states_output(A_    ,				A_    ,				A_	):
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()

						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)

						UpperCamelCase          =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						UpperCamelCase          =					self.model_tester.num_stages
						self.assertEqual(len(A_	)    ,				expected_num_stages + 1	)

						# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]	)    ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,				)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					True
						check_hidden_states_output(A_    ,				A_    ,				A_	)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase          =					True

						check_hidden_states_output(A_    ,				A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					ConvNextVaModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  Any:
		'''simple docstring'''

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


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



		@cached_property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'	) if is_vision_available() else None



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'	).to(A_	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					preprocessor(images=A_    ,				return_tensors='pt'	).to(A_	)

				# forward pass
				with torch.no_grad():
						UpperCamelCase          =					model(**A_	)

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 1_000)	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor([0.9996, 0.1966, -0.4386]	).to(A_	)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				A_    ,				atol=1e-4	)	)


from __future__ import annotations


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					text, pattern
				UpperCamelCase     ,  UpperCamelCase          =					len(A_	), len(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if char == self.pattern[i]:
								return i
				return -1



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
				return -1



		def __UpperCamelCase			(			self	)  -> list[int]:
				"""simple docstring"""
				# searches pattern in text and returns index positions
				UpperCamelCase          =					[]
				for i in range(self.textLen - self.patLen + 1	):
						UpperCamelCase          =					self.mismatch_in_text(A_	)
						if mismatch_index == -1:
								positions.append(A_	)
						else:
								UpperCamelCase          =					self.match_in_pattern(self.text[mismatch_index]	)
								UpperCamelCase          =					(
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
				return positions






_UpperCAmelCase :			Union[str, Any]              =	"ABAABA"
_UpperCAmelCase :			Any              =	"AB"
_UpperCAmelCase :			Dict              =	BoyerMooreSearch(text, pattern)
_UpperCAmelCase :			Optional[int]              =	bms.bad_character_heuristic()

if len(positions) == 0:
					print("No match found")
else:
					print("Pattern found in following positions: ")
					print(positions)
