



"""simple docstring"""







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    UpperCamelCase     :




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_ = 13							,UpperCAmelCase_ = 64							,UpperCAmelCase_ = 2							,UpperCAmelCase_ = 3							,UpperCAmelCase_ = 3							,UpperCAmelCase_ = True							,UpperCAmelCase_ = True							,UpperCAmelCase_ = 1_28							,UpperCAmelCase_=[16, 32, 64, 1_28]							,UpperCAmelCase_ = 7							,UpperCAmelCase_ = 4							,UpperCAmelCase_ = 37							,UpperCAmelCase_ = "gelu"							,UpperCAmelCase_ = 0.1							,UpperCAmelCase_ = 0.1							,UpperCAmelCase_ = 10							,UpperCAmelCase_ = 0.02							,UpperCAmelCase_ = 2							,UpperCAmelCase_ = 1							,UpperCAmelCase_ = 1_28							,UpperCAmelCase_ = [2, 2, 2, 2]							,UpperCAmelCase_ = 2							,UpperCAmelCase_ = 2							,):
			_lowercase							:      Optional[int]			=					parent
			_lowercase							:      List[str]			=					batch_size
			_lowercase							:      Optional[int]			=					image_size
			_lowercase							:      Optional[Any]			=					patch_size
			_lowercase							:      List[Any]			=					num_channels
			_lowercase							:      Union[str, Any]			=					is_training
			_lowercase							:      int			=					use_labels
			_lowercase							:      Optional[int]			=					hidden_size
			_lowercase							:      str			=					num_hidden_layers
			_lowercase							:      List[str]			=					num_attention_heads
			_lowercase							:      Tuple			=					intermediate_size
			_lowercase							:      str			=					hidden_act
			_lowercase							:      str			=					hidden_dropout_prob
			_lowercase							:      Any			=					attention_probs_dropout_prob
			_lowercase							:      Dict			=					type_sequence_label_size
			_lowercase							:      Optional[Any]			=					initializer_range
			_lowercase							:      Optional[int]			=					encoder_stride
			_lowercase							:      Tuple			=					num_attention_outputs
			_lowercase							:      str			=					embed_dim
			_lowercase							:      Any			=					embed_dim + 1
			_lowercase							:      Union[str, Any]			=					resolution
			_lowercase							:      Tuple			=					depths
			_lowercase							:      Dict			=					hidden_sizes
			_lowercase							:      str			=					dim
			_lowercase							:      Union[str, Any]			=					mlp_expansion_ratio






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      int			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			_lowercase							:      Any			=					None
			if self.use_labels:
					_lowercase							:      Tuple			=					ids_tensor([self.batch_size]							,self.type_sequence_label_size					)

			_lowercase							:      str			=					self.get_config()
			return config, pixel_values, labels






	def 					lowerCamelCase__ (			self					):
			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 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      List[Any]			=					EfficientFormerModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			_lowercase							:      Any			=					model(UpperCAmelCase_					)
			self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      Tuple			=					self.type_sequence_label_size
			_lowercase							:      Any			=					EfficientFormerForImageClassification(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			_lowercase							:      Dict			=					model(UpperCAmelCase_							,labels=UpperCAmelCase_					)
			self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size)					)

			# test greyscale images
			_lowercase							:      List[str]			=					1
			_lowercase							:      Any			=					EfficientFormerForImageClassification(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()

			_lowercase							:      Optional[Any]			=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
			_lowercase							:      Optional[Any]			=					model(UpperCAmelCase_					)
			self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size)					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Dict			=					self.prepare_config_and_inputs()
			(
			    (
			    _lowercase
			)			,   (
			    _lowercase
			)			,   (
			    _lowercase
			)			,   
			)							:      Tuple			=					config_and_inputs
			_lowercase							:      Dict			=					{"""pixel_values""": pixel_values}
			return config, inputs_dict







@require_torch
class    UpperCamelCase     (			snake_case ,					snake_case ,					unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   (
	    (
	        EfficientFormerModel,
	        EfficientFormerForImageClassificationWithTeacher,
	        EfficientFormerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_				:    str		    =   (
	    {
	        "feature-extraction": EfficientFormerModel,
	        "image-classification": (
	            EfficientFormerForImageClassification,
	            EfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   False

	SCREAMING_SNAKE_CASE_				:    Dict		    =   False
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   False
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   False






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[int]			=					EfficientFormerModelTester(self					)
			_lowercase							:      Union[str, Any]			=					ConfigTester(
			    self							,config_class=UpperCAmelCase_							,has_text_modality=UpperCAmelCase_							,hidden_size=37					)






	def 					lowerCamelCase__ (			self					):
			self.config_tester.run_common_tests()






	@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""					)
	def 					lowerCamelCase__ (			self					):
			pass






	@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""					)
	def 					lowerCamelCase__ (			self					):
			pass






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      int			=					self.model_tester.prepare_config_and_inputs_for_common()

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

					_lowercase							:      List[Any]			=					["""pixel_values"""]
					self.assertListEqual(arg_names[:1]							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			def check_hidden_states_output(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
					_lowercase							:      Union[str, Any]			=					model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.eval()

					with torch.no_grad():
							_lowercase							:      List[str]			=					model(**self._prepare_for_class(UpperCAmelCase_							,UpperCAmelCase_					)					)

					_lowercase							:      Union[str, Any]			=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					_lowercase							:      Any			=					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"""					):
							_lowercase							:      List[Any]			=					self.model_tester.encoder_seq_length
							if hasattr(self.model_tester							,"""chunk_length"""					) and self.model_tester.chunk_length > 1:
									_lowercase							:      Optional[Any]			=					seq_length * self.model_tester.chunk_length
					else:
							_lowercase							:      Dict			=					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:
							_lowercase							:      Tuple			=					outputs.decoder_hidden_states

							self.assertIsInstance(UpperCAmelCase_							,(list, tuple)					)
							self.assertEqual(len(UpperCAmelCase_					)							,UpperCAmelCase_					)
							_lowercase							:      List[Any]			=					getattr(self.model_tester							,"""seq_length"""							,UpperCAmelCase_					)
							_lowercase							:      Optional[int]			=					getattr(self.model_tester							,"""decoder_seq_length"""							,UpperCAmelCase_					)

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

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

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

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					_lowercase							:      Optional[int]			=					True

					check_hidden_states_output(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_=False					):
			_lowercase							:      Optional[Any]			=					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 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					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 					lowerCamelCase__ (			self					):
			_lowercase							:      Tuple			=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Dict			=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			if not self.model_tester.is_training:
					return

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






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Any			=					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(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"]}"""					):
									_lowercase							:      Union[str, Any]			=					problem_type["""title"""]
									_lowercase							:      List[Any]			=					problem_type["""num_labels"""]

									_lowercase							:      List[str]			=					model_class(UpperCAmelCase_					)
									model.to(UpperCAmelCase_					)
									model.train()

									_lowercase							:      Tuple			=					self._prepare_for_class(UpperCAmelCase_							,UpperCAmelCase_							,return_labels=UpperCAmelCase_					)

									if problem_type["num_labels"] > 1:
											_lowercase							:      List[str]			=					inputs["""labels"""].unsqueeze(1					).repeat(1							,problem_type["""num_labels"""]					)

									_lowercase							:      int			=					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:
											_lowercase							:      List[Any]			=					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 					lowerCamelCase__ (			self					):
			for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_lowercase							:      Union[str, Any]			=					EfficientFormerModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Optional[int]			=					self.model_tester.prepare_config_and_inputs_for_common()
			_lowercase							:      Union[str, Any]			=					True

			_lowercase							:      List[str]			=					getattr(self.model_tester							,"""seq_length"""							,UpperCAmelCase_					)
			_lowercase							:      str			=					getattr(self.model_tester							,"""encoder_seq_length"""							,UpperCAmelCase_					)
			_lowercase							:      List[str]			=					getattr(self.model_tester							,"""key_length"""							,UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					getattr(self.model_tester							,"""chunk_length"""							,UpperCAmelCase_					)

			if chunk_length is not None and hasattr(self.model_tester							,"""num_hashes"""					):
					_lowercase							:      List[str]			=					encoder_seq_length * self.model_tester.num_hashes

			for model_class in self.all_model_classes:
					_lowercase							:      Any			=					True
					_lowercase							:      List[Any]			=					False
					_lowercase							:      Optional[int]			=					True
					_lowercase							:      str			=					model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.eval()
					with torch.no_grad():
							_lowercase							:      List[str]			=					model(**self._prepare_for_class(UpperCAmelCase_							,UpperCAmelCase_					)					)
					_lowercase							:      Tuple			=					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"]
					_lowercase							:      Dict			=					True
					_lowercase							:      Optional[int]			=					model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.eval()
					with torch.no_grad():
							_lowercase							:      Any			=					model(**self._prepare_for_class(UpperCAmelCase_							,UpperCAmelCase_					)					)
					_lowercase							:      str			=					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       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      Optional[int]			=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
		return image





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




	"""simple docstring"""






	@cached_property
	def 					lowerCamelCase__ (			self					):
			return (
			    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""					)
			    if is_vision_available()
			    else None
			)






	@slow
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""					).to(
			    UpperCAmelCase_					)

			_lowercase							:      Union[str, Any]			=					self.default_image_processor
			_lowercase							:      List[str]			=					prepare_img()
			_lowercase							:      str			=					image_processor(images=UpperCAmelCase_							,return_tensors="""pt"""					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					_lowercase							:      int			=					model(**UpperCAmelCase_					)

			# verify the logits
			_lowercase							:      List[str]			=					(1, 10_00)
			self.assertEqual(outputs.logits.shape							,UpperCAmelCase_					)

			_lowercase							:      Tuple			=					torch.tensor([-0.0555, 0.4825, -0.0852]					).to(UpperCAmelCase_					)
			self.assertTrue(torch.allclose(outputs.logits[0][:3]							,UpperCAmelCase_							,atol=1E-4					)					)






	@slow
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					EfficientFormerForImageClassificationWithTeacher.from_pretrained(
			    """snap-research/efficientformer-l1-300"""					).to(UpperCAmelCase_					)

			_lowercase							:      List[str]			=					self.default_image_processor
			_lowercase							:      Optional[int]			=					prepare_img()
			_lowercase							:      Dict			=					image_processor(images=UpperCAmelCase_							,return_tensors="""pt"""					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					_lowercase							:      List[str]			=					model(**UpperCAmelCase_					)

			# verify the logits
			_lowercase							:      str			=					(1, 10_00)
			self.assertEqual(outputs.logits.shape							,UpperCAmelCase_					)

			_lowercase							:      List[str]			=					torch.tensor([-0.1312, 0.4353, -1.0499]					).to(UpperCAmelCase_					)
			self.assertTrue(torch.allclose(outputs.logits[0][:3]							,UpperCAmelCase_							,atol=1E-4					)					)










def 			lowerCamelCase_		(					lowerCAmelCase:		list			,							lowerCAmelCase:		list )->						list:
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		(
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  _snake_case      : list														=		[]
  for char_count in range(lowerCAmelCase ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count] )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count] )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)




