



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  snake_case__						:






     """simple docstring"""


     def __init__(	self  :  Union[str, Any],			_snake_case  :  Optional[Any],			_snake_case  :  int = 1_3,			_snake_case  :  int = 6_4,			_snake_case  :  int = 2,			_snake_case  :  int = 3,			_snake_case  :  int = 3,			_snake_case  :  bool = True,			_snake_case  :  bool = True,			_snake_case  :  int = 1_2_8,			_snake_case  :  int=[1_6, 3_2, 6_4, 1_2_8],			_snake_case  :  int = 7,			_snake_case  :  int = 4,			_snake_case  :  int = 3_7,			_snake_case  :  str = "gelu",			_snake_case  :  float = 0.1,			_snake_case  :  float = 0.1,			_snake_case  :  int = 1_0,			_snake_case  :  float = 0.0_2,			_snake_case  :  int = 2,			_snake_case  :  int = 1,			_snake_case  :  int = 1_2_8,			_snake_case  :  List[int] = [2, 2, 2, 2],			_snake_case  :  int = 2,			_snake_case  :  int = 2,			)       ->str:
            snake_case__							:     int            =     parent
            snake_case__							:     List[str]            =     batch_size
            snake_case__							:     Any            =     image_size
            snake_case__							:     Optional[int]            =     patch_size
            snake_case__							:     List[str]            =     num_channels
            snake_case__							:     Optional[Any]            =     is_training
            snake_case__							:     Dict            =     use_labels
            snake_case__							:     Dict            =     hidden_size
            snake_case__							:     Optional[Any]            =     num_hidden_layers
            snake_case__							:     Optional[int]            =     num_attention_heads
            snake_case__							:     List[Any]            =     intermediate_size
            snake_case__							:     Dict            =     hidden_act
            snake_case__							:     List[str]            =     hidden_dropout_prob
            snake_case__							:     List[Any]            =     attention_probs_dropout_prob
            snake_case__							:     List[Any]            =     type_sequence_label_size
            snake_case__							:     Optional[Any]            =     initializer_range
            snake_case__							:     Union[str, Any]            =     encoder_stride
            snake_case__							:     Optional[int]            =     num_attention_outputs
            snake_case__							:     Union[str, Any]            =     embed_dim
            snake_case__							:     List[str]            =     embed_dim + 1
            snake_case__							:     List[Any]            =     resolution
            snake_case__							:     List[str]            =     depths
            snake_case__							:     Optional[Any]            =     hidden_sizes
            snake_case__							:     Optional[int]            =     dim
            snake_case__							:     List[str]            =     mlp_expansion_ratio






     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            snake_case__							:     List[str]            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

            snake_case__							:     Optional[int]            =     self.get_config()
            return config, pixel_values, labels






     def        lowercase_       (	self  :  int )       ->Tuple:
            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=_snake_case,			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        lowercase_       (	self  :  List[Any],			_snake_case  :  Any,			_snake_case  :  Any,			_snake_case  :  List[str] )       ->Optional[int]:
            snake_case__							:     Tuple            =     EfficientFormerModel(config=_snake_case )
            model.to(_snake_case )
            model.eval()
            snake_case__							:     Union[str, Any]            =     model(_snake_case )
            self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size) )






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  str,			_snake_case  :  int,			_snake_case  :  Optional[Any] )       ->Union[str, Any]:
            snake_case__							:     Dict            =     self.type_sequence_label_size
            snake_case__							:     Any            =     EfficientFormerForImageClassification(_snake_case )
            model.to(_snake_case )
            model.eval()
            snake_case__							:     int            =     model(_snake_case,			labels=_snake_case )
            self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size) )

            # test greyscale images
            snake_case__							:     int            =     1
            snake_case__							:     Optional[int]            =     EfficientFormerForImageClassification(_snake_case )
            model.to(_snake_case )
            model.eval()

            snake_case__							:     Union[str, Any]            =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
            snake_case__							:     List[Any]            =     model(_snake_case )
            self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size) )






     def        lowercase_       (	self  :  int )       ->List[str]:
            snake_case__							:     str            =     self.prepare_config_and_inputs()
            (
                (
                snake_case__
            )     ,    (
                snake_case__
            )     ,    (
                snake_case__
            )     ,    
            )							:     List[str]            =     config_and_inputs
            snake_case__							:     List[Any]            =     {'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class  snake_case__						(	lowerCAmelCase_    ,					lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     (
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE            =     (
         {
             """feature-extraction""": EfficientFormerModel,
             """image-classification""": (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     _SCREAMING_SNAKE_CASE            =     False

     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  Union[str, Any] )       ->str:
            snake_case__							:     Union[str, Any]            =     EfficientFormerModelTester(self )
            snake_case__							:     List[str]            =     ConfigTester(
                self,			config_class=_snake_case,			has_text_modality=_snake_case,			hidden_size=3_7 )






     def        lowercase_       (	self  :  int )       ->Optional[Any]:
            self.config_tester.run_common_tests()






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






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






     def        lowercase_       (	self  :  Union[str, Any] )       ->Tuple:
            snake_case__     ,    snake_case__							:     Dict            =     self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   snake_case__							:     Optional[int]            =     model_class(_snake_case )
                   snake_case__							:     Union[str, Any]            =     inspect.signature(model.forward )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   snake_case__							:     int            =     [*signature.parameters.keys()]

                   snake_case__							:     Any            =     ['pixel_values']
                   self.assertListEqual(arg_names[:1],			_snake_case )






     def        lowercase_       (	self  :  int )       ->List[Any]:
            def check_hidden_states_output(_snake_case  :  Tuple,			_snake_case  :  Optional[int],			_snake_case  :  Tuple ):
                   snake_case__							:     Dict            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.eval()

                   with torch.no_grad():
                          snake_case__							:     List[Any]            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )

                   snake_case__							:     int            =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   snake_case__							:     Tuple            =     getattr(
                       self.model_tester,			'expected_num_hidden_layers',			self.model_tester.num_hidden_layers + 1 )
                   self.assertEqual(len(_snake_case ),			_snake_case )

                   if hasattr(self.model_tester,			'encoder_seq_length' ):
                          snake_case__							:     int            =     self.model_tester.encoder_seq_length
                          if hasattr(self.model_tester,			'chunk_length' ) and self.model_tester.chunk_length > 1:
                                 snake_case__							:     List[Any]            =     seq_length * self.model_tester.chunk_length
                   else:
                          snake_case__							:     Tuple            =     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:
                          snake_case__							:     str            =     outputs.decoder_hidden_states

                          self.assertIsInstance(_snake_case,			(list, tuple) )
                          self.assertEqual(len(_snake_case ),			_snake_case )
                          snake_case__							:     Optional[int]            =     getattr(self.model_tester,			'seq_length',			_snake_case )
                          snake_case__							:     int            =     getattr(self.model_tester,			'decoder_seq_length',			_snake_case )

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

            snake_case__     ,    snake_case__							:     str            =     self.model_tester.prepare_config_and_inputs_for_common()

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

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   snake_case__							:     Dict            =     True

                   check_hidden_states_output(_snake_case,			_snake_case,			_snake_case )






     def        lowercase_       (	self  :  str,			_snake_case  :  List[Any],			_snake_case  :  Dict,			_snake_case  :  Optional[Any]=False )       ->Any:
            snake_case__							:     Any            =     super()._prepare_for_class(_snake_case,			_snake_case,			return_labels=_snake_case )

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

            return inputs_dict






     def        lowercase_       (	self  :  List[str] )       ->List[Any]:
            snake_case__							:     Union[str, Any]            =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_snake_case )






     @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet' )
     def        lowercase_       (	self  :  List[Any] )       ->Union[str, Any]:
            snake_case__							:     Optional[Any]            =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case )






     def        lowercase_       (	self  :  Any )       ->str:
            snake_case__							:     Optional[int]            =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*_snake_case )






     def        lowercase_       (	self  :  Tuple )       ->str:
            if not self.model_tester.is_training:
                   return

            snake_case__     ,    snake_case__							:     List[str]            =     self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:     str            =     True

            for model_class in self.all_model_classes:
                   # EfficientFormerForImageClassificationWithTeacher supports inference-only
                   if (
                       model_class in get_values(_snake_case )
                       or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
                   ):
                          continue
                   snake_case__							:     Any            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.train()
                   snake_case__							:     List[str]            =     self._prepare_for_class(_snake_case,			_snake_case,			return_labels=_snake_case )
                   snake_case__							:     List[Any]            =     model(**_snake_case ).loss
                   loss.backward()






     def        lowercase_       (	self  :  int )       ->str:
            snake_case__     ,    snake_case__							:     Dict            =     self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:     Optional[Any]            =     [
                {'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(_snake_case ),
                       ]
                       or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
                   ):
                          continue

                   for problem_type in problem_types:
                          with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}''' ):
                                 snake_case__							:     Optional[int]            =     problem_type['title']
                                 snake_case__							:     int            =     problem_type['num_labels']

                                 snake_case__							:     int            =     model_class(_snake_case )
                                 model.to(_snake_case )
                                 model.train()

                                 snake_case__							:     Dict            =     self._prepare_for_class(_snake_case,			_snake_case,			return_labels=_snake_case )

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

                                 snake_case__							:     str            =     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=_snake_case ) as warning_list:
                                        snake_case__							:     Union[str, Any]            =     model(**_snake_case ).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        lowercase_       (	self  :  List[str] )       ->Dict:
            for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   snake_case__							:     Optional[Any]            =     EfficientFormerModel.from_pretrained(_snake_case )
                   self.assertIsNotNone(_snake_case )






     def        lowercase_       (	self  :  Union[str, Any] )       ->str:
            snake_case__     ,    snake_case__							:     int            =     self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:     Optional[Any]            =     True

            snake_case__							:     Dict            =     getattr(self.model_tester,			'seq_length',			_snake_case )
            snake_case__							:     Union[str, Any]            =     getattr(self.model_tester,			'encoder_seq_length',			_snake_case )
            snake_case__							:     Any            =     getattr(self.model_tester,			'key_length',			_snake_case )
            snake_case__							:     List[str]            =     getattr(self.model_tester,			'chunk_length',			_snake_case )

            if chunk_length is not None and hasattr(self.model_tester,			'num_hashes' ):
                   snake_case__							:     Union[str, Any]            =     encoder_seq_length * self.model_tester.num_hashes

            for model_class in self.all_model_classes:
                   snake_case__							:     Optional[int]            =     True
                   snake_case__							:     List[str]            =     False
                   snake_case__							:     Union[str, Any]            =     True
                   snake_case__							:     Any            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:     List[str]            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )
                   snake_case__							:     Optional[Any]            =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(_snake_case ),			self.model_tester.num_attention_outputs )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   snake_case__							:     Dict            =     True
                   snake_case__							:     Union[str, Any]            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:     List[Any]            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )
                   snake_case__							:     int            =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(_snake_case ),			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 	lowercase_							():
       snake_case__							:     int            =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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






     """simple docstring"""


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






     @slow
     def        lowercase_       (	self  :  Tuple )       ->Optional[int]:
            snake_case__							:     List[str]            =     EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300' ).to(
                _snake_case )

            snake_case__							:     Tuple            =     self.default_image_processor
            snake_case__							:     List[Any]            =     prepare_img()
            snake_case__							:     Tuple            =     image_processor(images=_snake_case,			return_tensors='pt' ).to(_snake_case )

            # forward pass
            with torch.no_grad():
                   snake_case__							:     Tuple            =     model(**_snake_case )

            # verify the logits
            snake_case__							:     Union[str, Any]            =     (1, 1_0_0_0)
            self.assertEqual(outputs.logits.shape,			_snake_case )

            snake_case__							:     List[Any]            =     torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2] ).to(_snake_case )
            self.assertTrue(torch.allclose(outputs.logits[0][:3],			_snake_case,			atol=1e-4 ) )






     @slow
     def        lowercase_       (	self  :  Union[str, Any] )       ->Union[str, Any]:
            snake_case__							:     Optional[int]            =     EfficientFormerForImageClassificationWithTeacher.from_pretrained(
                'snap-research/efficientformer-l1-300' ).to(_snake_case )

            snake_case__							:     List[Any]            =     self.default_image_processor
            snake_case__							:     Tuple            =     prepare_img()
            snake_case__							:     Any            =     image_processor(images=_snake_case,			return_tensors='pt' ).to(_snake_case )

            # forward pass
            with torch.no_grad():
                   snake_case__							:     int            =     model(**_snake_case )

            # verify the logits
            snake_case__							:     Dict            =     (1, 1_0_0_0)
            self.assertEqual(outputs.logits.shape,			_snake_case )

            snake_case__							:     str            =     torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9] ).to(_snake_case )
            self.assertTrue(torch.allclose(outputs.logits[0][:3],			_snake_case,			atol=1e-4 ) )











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






