




'''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      _a    :
       def __init__(      self		:      List[str]  ,							lowercase		:      Union[str, Any]  ,							lowercase		:      int = 13  ,							lowercase		:      int = 64  ,							lowercase		:      int = 2  ,							lowercase		:      int = 3  ,							lowercase		:      int = 3  ,							lowercase		:      bool = True  ,							lowercase		:      bool = True  ,							lowercase		:      int = 128  ,							lowercase		:      Optional[int]=[16, 32, 64, 128]  ,							lowercase		:      int = 7  ,							lowercase		:      int = 4  ,							lowercase		:      int = 37  ,							lowercase		:      str = "gelu"  ,							lowercase		:      float = 0.1  ,							lowercase		:      float = 0.1  ,							lowercase		:      int = 10  ,							lowercase		:      float = 0.02  ,							lowercase		:      int = 2  ,							lowercase		:      int = 1  ,							lowercase		:      int = 128  ,							lowercase		:      List[int] = [2, 2, 2, 2]  ,							lowercase		:      int = 2  ,							lowercase		:      int = 2  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						image_size
           UpperCAmelCase     			=						patch_size
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						hidden_size
           UpperCAmelCase     			=						num_hidden_layers
           UpperCAmelCase     			=						num_attention_heads
           UpperCAmelCase     			=						intermediate_size
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						attention_probs_dropout_prob
           UpperCAmelCase     			=						type_sequence_label_size
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						encoder_stride
           UpperCAmelCase     			=						num_attention_outputs
           UpperCAmelCase     			=						embed_dim
           UpperCAmelCase     			=						embed_dim + 1
           UpperCAmelCase     			=						resolution
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						dim
           UpperCAmelCase     			=						mlp_expansion_ratio
       def        A (      self		:      Optional[int]							):



           '''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.type_sequence_label_size							)

           UpperCAmelCase     			=						self.get_config()
           return config, pixel_values, labels
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           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=lowercase  ,							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        A (      self		:      str  ,							lowercase		:      Tuple  ,							lowercase		:      List[Any]  ,							lowercase		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						EfficientFormerModel(config=lowercase							)
           model.to(lowercase							)
           model.eval()
           UpperCAmelCase     			=						model(lowercase							)
           self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)							)
       def        A (      self		:      Optional[int]  ,							lowercase		:      Any  ,							lowercase		:      Dict  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.type_sequence_label_size
           UpperCAmelCase     			=						EfficientFormerForImageClassification(lowercase							)
           model.to(lowercase							)
           model.eval()
           UpperCAmelCase     			=						model(lowercase  ,							labels=lowercase							)
           self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.type_sequence_label_size)							)

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

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







       def        A (      self		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           )			,	(
               UpperCAmelCase
           )			,	(
               UpperCAmelCase
           )			,	
           )     			=						config_and_inputs
           UpperCAmelCase     			=						{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class      _a    (      __a				,				__a				,				unittest.TestCase					):
       __a							:	List[Any]  							=   (
           (
               EfficientFormerModel,
               EfficientFormerForImageClassificationWithTeacher,
               EfficientFormerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       __a							:	Union[str, Any]  							=   (
           {
               """feature-extraction""": EfficientFormerModel,
               """image-classification""": (
                   EfficientFormerForImageClassification,
                   EfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_torch_available()
           else {}
       )
       __a							:	Union[str, Any]  							=   False

       __a							:	str  							=   False
       __a							:	Tuple  							=   False
       __a							:	Union[str, Any]  							=   False
       def        A (      self		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						EfficientFormerModelTester(self							)
           UpperCAmelCase     			=						ConfigTester(
               self  ,							config_class=lowercase  ,							has_text_modality=lowercase  ,							hidden_size=37							)
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''							)
       def        A (      self		:      Any							):



           '''simple docstring'''

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



           '''simple docstring'''

           pass
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						model_class(lowercase							)
               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]  ,							lowercase							)
       def        A (      self		:      Any							):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:      str  ,							lowercase		:      Union[str, Any]  ,							lowercase		:      Tuple							):
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.eval()

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

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

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

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

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

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

           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(lowercase  ,							lowercase  ,							lowercase							)

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

               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)
       def        A (      self		:      Any  ,							lowercase		:      Optional[Any]  ,							lowercase		:      Optional[Any]  ,							lowercase		:      Dict=False							):



           '''simple docstring'''

           UpperCAmelCase     			=						super()._prepare_for_class(lowercase  ,							lowercase  ,							return_labels=lowercase							)

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

           return inputs_dict
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase							)
       @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowercase							)
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase							)
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

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

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



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

                       UpperCAmelCase     			=						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=lowercase							) as warning_list:
                           UpperCAmelCase     			=						model(**lowercase							).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        A (      self		:      Tuple							):



           '''simple docstring'''

           for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     			=						EfficientFormerModel.from_pretrained(lowercase							)
               self.assertIsNotNone(lowercase							)







       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

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

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

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

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

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







@require_torch
@require_vision
class      _a    (      unittest.TestCase					):
       @cached_property
       def        A (      self		:      str							):



           '''simple docstring'''

           return (
               EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''							)
               if is_vision_available()
               else None
           )
       @slow
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''							).to(
               lowercase							)

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

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

           # verify the logits
           UpperCAmelCase     			=						(1, 1_000)
           self.assertEqual(outputs.logits.shape  ,							lowercase							)

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







       @slow
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						EfficientFormerForImageClassificationWithTeacher.from_pretrained(
               '''snap-research/efficientformer-l1-300'''							).to(lowercase							)

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

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

           # verify the logits
           UpperCAmelCase     			=						(1, 1_000)
           self.assertEqual(outputs.logits.shape  ,							lowercase							)

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










'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




