import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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 (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    EfficientFormerForImageClassification,
	    EfficientFormerForImageClassificationWithTeacher,
	    EfficientFormerModel,
	)
	from transformers.models.efficientformer.modeling_efficientformer import (
	    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)


if is_vision_available():
	from PIL import Image

	from transformers import EfficientFormerImageProcessor


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = 1_3      ,						UpperCAmelCase = 6_4      ,						UpperCAmelCase = 2      ,						UpperCAmelCase = 3      ,						UpperCAmelCase = 3      ,						UpperCAmelCase = True      ,						UpperCAmelCase = True      ,						UpperCAmelCase = 1_2_8      ,						UpperCAmelCase=[1_6, 3_2, 6_4, 1_2_8]      ,						UpperCAmelCase = 7      ,						UpperCAmelCase = 4      ,						UpperCAmelCase = 3_7      ,						UpperCAmelCase = "gelu"      ,						UpperCAmelCase = 0.1      ,						UpperCAmelCase = 0.1      ,						UpperCAmelCase = 1_0      ,						UpperCAmelCase = 0.02      ,						UpperCAmelCase = 2      ,						UpperCAmelCase = 1      ,						UpperCAmelCase = 1_2_8      ,						UpperCAmelCase = [2, 2, 2, 2]      ,						UpperCAmelCase = 2      ,						UpperCAmelCase = 2      ,						) ->    Dict:
								__a							=   parent
								__a							=   batch_size
								__a							=   image_size
								__a							=   patch_size
								__a							=   num_channels
								__a							=   is_training
								__a							=   use_labels
								__a							=   hidden_size
								__a							=   num_hidden_layers
								__a							=   num_attention_heads
								__a							=   intermediate_size
								__a							=   hidden_act
								__a							=   hidden_dropout_prob
								__a							=   attention_probs_dropout_prob
								__a							=   type_sequence_label_size
								__a							=   initializer_range
								__a							=   encoder_stride
								__a							=   num_attention_outputs
								__a							=   embed_dim
								__a							=   embed_dim + 1
								__a							=   resolution
								__a							=   depths
								__a							=   hidden_sizes
								__a							=   dim
								__a							=   mlp_expansion_ratio



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								__a							=   None
								if self.use_labels:
													__a							=   ids_tensor([self.batch_size]      ,						self.type_sequence_label_size    )

								__a							=   self.get_config()
								return config, pixel_values, labels



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								return EfficientFormerConfig(
								    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						is_decoder=UpperCAmelCase      ,						initializer_range=self.initializer_range      ,						encoder_stride=self.encoder_stride      ,						resolution=self.resolution      ,						depths=self.depths      ,						hidden_sizes=self.hidden_sizes      ,						dim=self.dim      ,						mlp_expansion_ratio=self.mlp_expansion_ratio      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    int:
								__a							=   EfficientFormerModel(config=UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								__a							=   model(UpperCAmelCase    )
								self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[int]:
								__a							=   self.type_sequence_label_size
								__a							=   EfficientFormerForImageClassification(UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()
								__a							=   model(UpperCAmelCase      ,						labels=UpperCAmelCase    )
								self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)    )

								# test greyscale images
								__a							=   1
								__a							=   EfficientFormerForImageClassification(UpperCAmelCase    )
								model.to(UpperCAmelCase    )
								model.eval()

								__a							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
								__a							=   model(UpperCAmelCase    )
								self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								__a							=   self.prepare_config_and_inputs()
								(
								    (
								    __a
								)					,      (
								    __a
								)					,      (
								    __a
								)					,      
								)							=   config_and_inputs
								__a							=   {'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 						a__							(   __snake_case      ,						__snake_case      ,						unittest.TestCase		):
			A__				:   List[Any]								=			(
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A__				:   Optional[int]								=			(
			    {
			        'feature-extraction': EfficientFormerModel,
			        'image-classification': (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			A__				:   Any								=			False

			A__				:   Optional[int]								=			False
			A__				:   str								=			False
			A__				:   Optional[Any]								=			False



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								__a							=   EfficientFormerModelTester(self    )
								__a							=   ConfigTester(
								    self      ,						config_class=UpperCAmelCase      ,						has_text_modality=UpperCAmelCase      ,						hidden_size=3_7    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								self.config_tester.run_common_tests()



			@unittest.skip(reason='EfficientFormer does not use inputs_embeds'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								pass



			@unittest.skip(reason='EfficientFormer does not support input and output embeddings'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								pass



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

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

													__a							=   ['pixel_values']
													self.assertListEqual(arg_names[:1]      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								def check_hidden_states_output(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()

													with torch.no_grad():
																		__a							=   model(**self._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase    )    )

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

													__a							=   getattr(
													    self.model_tester      ,						'expected_num_hidden_layers'      ,						self.model_tester.num_hidden_layers + 1    )
													self.assertEqual(len(UpperCAmelCase    )      ,						UpperCAmelCase    )

													if hasattr(self.model_tester      ,						'encoder_seq_length'    ):
																		__a							=   self.model_tester.encoder_seq_length
																		if hasattr(self.model_tester      ,						'chunk_length'    ) and self.model_tester.chunk_length > 1:
																							__a							=   seq_length * self.model_tester.chunk_length
													else:
																		__a							=   self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]    )      ,						[seq_length, self.model_tester.hidden_size]      ,						)

													if config.is_encoder_decoder:
																		__a							=   outputs.decoder_hidden_states

																		self.assertIsInstance(UpperCAmelCase      ,						(list, tuple)    )
																		self.assertEqual(len(UpperCAmelCase    )      ,						UpperCAmelCase    )
																		__a							=   getattr(self.model_tester      ,						'seq_length'      ,						UpperCAmelCase    )
																		__a							=   getattr(self.model_tester      ,						'decoder_seq_length'      ,						UpperCAmelCase    )

																		self.assertListEqual(
																		    list(hidden_states[-1].shape[-2:]    )      ,						[decoder_seq_length, self.model_tester.hidden_size]      ,						)

								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__a							=   True
													check_hidden_states_output(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )

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

													check_hidden_states_output(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=False    ) ->    Any:
								__a							=   super()._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase      ,						return_labels=UpperCAmelCase    )

								if return_labels:
													if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																		del inputs_dict["labels"]

								return inputs_dict



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase    )



			@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'    )
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								__a							=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								if not self.model_tester.is_training:
													return

								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()
								__a							=   True

								for model_class in self.all_model_classes:
													# EfficientFormerForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(UpperCAmelCase    )
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																		continue
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.train()
													__a							=   self._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase      ,						return_labels=UpperCAmelCase    )
													__a							=   model(**UpperCAmelCase    ).loss
													loss.backward()



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()

								__a							=   [
								    {'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(UpperCAmelCase    ),
													    ]
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																		continue

													for problem_type in problem_types:
																		with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''    ):
																							__a							=   problem_type['title']
																							__a							=   problem_type['num_labels']

																							__a							=   model_class(UpperCAmelCase    )
																							model.to(UpperCAmelCase    )
																							model.train()

																							__a							=   self._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase      ,						return_labels=UpperCAmelCase    )

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

																							__a							=   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=UpperCAmelCase    ) as warning_list:
																												__a							=   model(**UpperCAmelCase    ).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 __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a							=   EfficientFormerModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a					,      __a							=   self.model_tester.prepare_config_and_inputs_for_common()
								__a							=   True

								__a							=   getattr(self.model_tester      ,						'seq_length'      ,						UpperCAmelCase    )
								__a							=   getattr(self.model_tester      ,						'encoder_seq_length'      ,						UpperCAmelCase    )
								__a							=   getattr(self.model_tester      ,						'key_length'      ,						UpperCAmelCase    )
								__a							=   getattr(self.model_tester      ,						'chunk_length'      ,						UpperCAmelCase    )

								if chunk_length is not None and hasattr(self.model_tester      ,						'num_hashes'    ):
													__a							=   encoder_seq_length * self.model_tester.num_hashes

								for model_class in self.all_model_classes:
													__a							=   True
													__a							=   False
													__a							=   True
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																		__a							=   model(**self._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase    )    )
													__a							=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(UpperCAmelCase    )      ,						self.model_tester.num_attention_outputs    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__a							=   True
													__a							=   model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																		__a							=   model(**self._prepare_for_class(UpperCAmelCase      ,						UpperCAmelCase    )    )
													__a							=   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(UpperCAmelCase    )      ,						self.model_tester.num_attention_outputs    )

													if chunk_length is not None:
																		self.assertListEqual(
																		    list(attentions[0].shape[-4:]    )      ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,						)
													else:
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:]    )      ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,						)






def 							lowerCAmelCase(	):
					__a							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
					return image



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



			@cached_property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								return (
								    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'    )
								    if is_vision_available()
								    else None
								)



			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'    ).to(
								    UpperCAmelCase    )

								__a							=   self.default_image_processor
								__a							=   prepare_img()
								__a							=   image_processor(images=UpperCAmelCase      ,						return_tensors='pt'    ).to(UpperCAmelCase    )

								# forward pass
								with torch.no_grad():
													__a							=   model(**UpperCAmelCase    )

								# verify the logits
								__a							=   (1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape      ,						UpperCAmelCase    )

								__a							=   torch.tensor([-0.0_555, 0.4_825, -0.0_852]    ).to(UpperCAmelCase    )
								self.assertTrue(torch.allclose(outputs.logits[0][:3]      ,						UpperCAmelCase      ,						atol=1e-4    )    )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   EfficientFormerForImageClassificationWithTeacher.from_pretrained(
								    'snap-research/efficientformer-l1-300'    ).to(UpperCAmelCase    )

								__a							=   self.default_image_processor
								__a							=   prepare_img()
								__a							=   image_processor(images=UpperCAmelCase      ,						return_tensors='pt'    ).to(UpperCAmelCase    )

								# forward pass
								with torch.no_grad():
													__a							=   model(**UpperCAmelCase    )

								# verify the logits
								__a							=   (1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape      ,						UpperCAmelCase    )

								__a							=   torch.tensor([-0.1_312, 0.4_353, -1.0_499]    ).to(UpperCAmelCase    )
								self.assertTrue(torch.allclose(outputs.logits[0][:3]      ,						UpperCAmelCase      ,						atol=1e-4    )    )





def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




