


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor

class   A__ (   snake_case__     ):







    """simple docstring"""

    def 		a_      (	self   ):
         snake_case					=    self.config_class(**self.inputs_dict   )
         self.parent.assertTrue(hasattr(__snake_case      , '''hidden_sizes'''   )   )
         self.parent.assertTrue(hasattr(__snake_case      , '''num_attention_heads'''   )   )






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=6_4      , __snake_case=3      , __snake_case=3      , __snake_case=2      , __snake_case=1      , __snake_case=1_6      , __snake_case=[1_2_8, 2_5_6, 3_8_4]      , __snake_case=[4, 6, 8]      , __snake_case=[2, 3, 4]      , __snake_case=[1_6, 1_6, 1_6]      , __snake_case=0      , __snake_case=[2, 2, 2]      , __snake_case=[2, 2, 2]      , __snake_case=0.02      , __snake_case=True      , __snake_case=True      , __snake_case=2      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    image_size
         snake_case					=    num_channels
         snake_case					=    kernel_size
         snake_case					=    stride
         snake_case					=    padding
         snake_case					=    hidden_sizes
         snake_case					=    num_attention_heads
         snake_case					=    depths
         snake_case					=    key_dim
         snake_case					=    drop_path_rate
         snake_case					=    patch_size
         snake_case					=    attention_ratio
         snake_case					=    mlp_ratio
         snake_case					=    initializer_range
         snake_case					=    [
             ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]
         snake_case					=    is_training
         snake_case					=    use_labels
         snake_case					=    num_labels
         snake_case					=    initializer_range
    def 		a_      (	self   ):
         snake_case					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

         snake_case					=    self.get_config()
         return config, pixel_values, labels
    def 		a_      (	self   ):
         return LevitConfig(
             image_size=self.image_size      , num_channels=self.num_channels      , kernel_size=self.kernel_size      , stride=self.stride      , padding=self.padding      , patch_size=self.patch_size      , hidden_sizes=self.hidden_sizes      , num_attention_heads=self.num_attention_heads      , depths=self.depths      , key_dim=self.key_dim      , drop_path_rate=self.drop_path_rate      , mlp_ratio=self.mlp_ratio      , attention_ratio=self.attention_ratio      , initializer_range=self.initializer_range      , down_ops=self.down_ops      , )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    LevitModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case   )
         snake_case					=    (self.image_size, self.image_size)
         snake_case			,  snake_case					=    image_size[0], image_size[1]
         for _ in range(4   ):
              snake_case					=    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
              snake_case					=    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
         self.parent.assertEqual(
             result.last_hidden_state.shape      , (self.batch_size, ceil(height / 4   ) * ceil(width / 4   ), self.hidden_sizes[-1])      , )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    self.num_labels
         snake_case					=    LevitForImageClassification(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )




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






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







    """simple docstring"""

    __magic_name__        =		(
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    __magic_name__        =		(
        {
            'feature-extraction': LevitModel,
            'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    LevitModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , has_text_modality=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
    def 		a_      (	self   ):
         return
    @unittest.skip(reason='''Levit does not use inputs_embeds'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Levit does not support input and output embeddings'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Levit does not output attentions'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case					=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]      , __snake_case   )
    def 		a_      (	self   ):
         def check_hidden_states_output(__snake_case      , __snake_case      , __snake_case   ):
              snake_case					=    model_class(__snake_case   )
              model.to(__snake_case   )
              model.eval()

              with torch.no_grad():
                   snake_case					=    model(**self._prepare_for_class(__snake_case      , __snake_case   )   )

              snake_case					=    outputs.hidden_states

              snake_case					=    len(self.model_tester.depths   ) + 1
              self.assertEqual(len(__snake_case   )      , __snake_case   )

              snake_case					=    (self.model_tester.image_size, self.model_tester.image_size)
              snake_case			,  snake_case					=    image_size[0], image_size[1]
              for _ in range(4   ):
                   snake_case					=    floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1   )
                   snake_case					=    floor(
                       (
                           (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1   )
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]   )      , [
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]      , )

         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case					=    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					=    True

              check_hidden_states_output(__snake_case      , __snake_case      , __snake_case   )
    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case=False   ):
         snake_case					=    super()._prepare_for_class(__snake_case      , __snake_case      , return_labels=__snake_case   )

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

         return inputs_dict
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case   )
    def 		a_      (	self   ):
         if not self.model_tester.is_training:
              return

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

         for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(__snake_case   )
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue
              snake_case					=    model_class(__snake_case   )
              model.to(__snake_case   )
              model.train()
              snake_case					=    self._prepare_for_class(__snake_case      , __snake_case      , return_labels=__snake_case   )
              snake_case					=    model(**__snake_case   ).loss
              loss.backward()
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         snake_case					=    False
         snake_case					=    True

         for model_class in self.all_model_classes:
              if model_class in get_values(__snake_case   ) or not model_class.supports_gradient_checkpointing:
                   continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
              snake_case					=    model_class(__snake_case   )
              model.gradient_checkpointing_enable()
              model.to(__snake_case   )
              model.train()
              snake_case					=    self._prepare_for_class(__snake_case      , __snake_case      , return_labels=__snake_case   )
              snake_case					=    model(**__snake_case   ).loss
              loss.backward()
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         snake_case					=    [
             {'''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__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue

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

                        snake_case					=    model_class(__snake_case   )
                        model.to(__snake_case   )
                        model.train()

                        snake_case					=    self._prepare_for_class(__snake_case      , __snake_case      , return_labels=__snake_case   )

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

                        snake_case					=    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					=    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 		a_      (	self   ):
         for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case					=    LevitModel.from_pretrained(__snake_case   )
              self.assertIsNotNone(__snake_case   )






def       UpperCAmelCase__						():



     """simple docstring"""






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

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







    """simple docstring"""

    @cached_property
    def 		a_      (	self   ):
         return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )




    @slow
    def 		a_      (	self   ):
         snake_case					=    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
             __snake_case   )

         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(images=__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )

         # forward pass
         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         # verify the logits
         snake_case					=    torch.Size((1, 1_0_0_0)   )
         self.assertEqual(outputs.logits.shape      , __snake_case   )

         snake_case					=    torch.tensor([1.0448, -0.3745, -1.8317]   ).to(__snake_case   )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]      , __snake_case      , atol=1E-4   )   )








import math

class   A__ :







    """simple docstring"""

    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    0.0
         snake_case					=    0.0
         for i in range(len(__snake_case   )   ):
              da += math.pow((sample[i] - weights[0][i])      , 2   )
              da += math.pow((sample[i] - weights[1][i])      , 2   )
              return 0 if da > da else 1
         return 0




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         for i in range(len(__snake_case   )   ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     snake_case					=    [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     snake_case					=    SelfOrganizingMap()
     snake_case					=    3
     snake_case					=    0.5

     for _ in range(UpperCamelCase_    ):
          for j in range(len(UpperCamelCase_    )    ):
               # training sample
               snake_case					=    training_samples[j]

               # Compute the winning vector
               snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

               # Update the winning vector
               snake_case					=    self_organizing_map.update(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

    # classify test sample
     snake_case					=    [0, 0, 0, 1]
     snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''    )
     print(F'''Weights that have been trained : {weights}'''    )


# running the main() function
if __name__ == "__main__":
 main()




