






'''simple docstring'''





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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.config_class(**self.inputs_dict )
         self.parent.assertTrue(hasattr(lowercase__    ,							'hidden_sizes' ) )
         self.parent.assertTrue(hasattr(lowercase__    ,							'num_attention_heads' ) )






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=64    ,							lowercase__=3    ,							lowercase__=3    ,							lowercase__=2    ,							lowercase__=1    ,							lowercase__=16    ,							lowercase__=[128, 256, 384]    ,							lowercase__=[4, 6, 8]    ,							lowercase__=[2, 3, 4]    ,							lowercase__=[16, 16, 16]    ,							lowercase__=0    ,							lowercase__=[2, 2, 2]    ,							lowercase__=[2, 2, 2]    ,							lowercase__=0.0_2    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=2    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       parent
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Dict    	=       kernel_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       stride
         SCREAMING_SNAKE_CASE					:			Dict    	=       padding
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       depths
         SCREAMING_SNAKE_CASE					:			Dict    	=       key_dim
         SCREAMING_SNAKE_CASE					:			Any    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			str    	=       patch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_ratio
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       mlp_ratio
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			int    	=       [
             ['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],
         ]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       num_labels
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       initializer_range






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       None
         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size]    ,							self.num_labels )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_config()
         return config, pixel_values, labels






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			str    	=       LevitModel(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       (self.image_size, self.image_size)
         SCREAMING_SNAKE_CASE					:			int    	=       image_size[0], image_size[1]
         for _ in range(4 ):
              SCREAMING_SNAKE_CASE					:			str    	=       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
              SCREAMING_SNAKE_CASE					:			int    	=       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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       LevitForImageClassification(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE					:			Any    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Any    	=       {'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						(
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    snake_case__							:   Optional[Any]							=						(
        {
            "feature-extraction": LevitModel,
            "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    snake_case__							:   int							=						False
    snake_case__							:   Tuple							=						False
    snake_case__							:   str							=						False
    snake_case__							:   Any							=						False
    snake_case__							:   List[Any]							=						False






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       LevitModelTester(self )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ConfigTester(self    ,							config_class=lowercase__    ,							has_text_modality=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> List[str]:
         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       _UpperCamelCase    (       self )							-> Optional[int]:
         return






    @unittest.skip(reason='Levit does not use inputs_embeds' )
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         pass






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






    @unittest.skip(reason='Levit does not output attentions' )
    def       _UpperCamelCase    (       self )							-> Dict:
         pass






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE					:			str    	=       [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE					:			Dict    	=       ['pixel_values']
              self.assertListEqual(arg_names[:1]    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         def check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			int    	=       model_class(lowercase__ )
              model.to(lowercase__ )
              model.eval()

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

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.hidden_states

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(self.model_tester.depths ) + 1
              self.assertEqual(len(lowercase__ )    ,							lowercase__ )

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       (self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE					:			str    	=       image_size[0], image_size[1]
              for _ in range(4 ):
                   SCREAMING_SNAKE_CASE					:			Dict    	=       floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1 )
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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],
                  ]    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True
              check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ )

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

              check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ )






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def       _UpperCamelCase    (       self )							-> Dict:
         pass






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=False )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       super()._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )

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

         return inputs_dict






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True

         for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase__ )
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class(lowercase__ )
              model.to(lowercase__ )
              model.train()
              SCREAMING_SNAKE_CASE					:			Dict    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(**lowercase__ ).loss
              loss.backward()






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       False
         SCREAMING_SNAKE_CASE					:			Any    	=       True

         for model_class in self.all_model_classes:
              if model_class in get_values(lowercase__ ) or not model_class.supports_gradient_checkpointing:
                   continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       model_class(lowercase__ )
              model.gradient_checkpointing_enable()
              model.to(lowercase__ )
              model.train()
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(**lowercase__ ).loss
              loss.backward()






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE					:			Union[str, 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(lowercase__ ),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue

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

                        SCREAMING_SNAKE_CASE					:			Any    	=       model_class(lowercase__ )
                        model.to(lowercase__ )
                        model.train()

                        SCREAMING_SNAKE_CASE					:			Tuple    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )

                        if problem_type["num_labels"] > 1:
                             SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       inputs['labels'].unsqueeze(1 ).repeat(1    ,							problem_type['num_labels'] )

                        SCREAMING_SNAKE_CASE					:			Any    	=       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:
                             SCREAMING_SNAKE_CASE					:			List[Any]    	=       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       _UpperCamelCase    (       self )							-> Dict:
         for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       LevitModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )






def     __lowerCAmelCase						(       )      ->	str:





     '''simple docstring'''





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






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




    '''simple docstring'''







    @cached_property
    def       _UpperCamelCase    (       self )							-> int:
         return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
             lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.default_image_processor
         SCREAMING_SNAKE_CASE					:			Dict    	=       prepare_img()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor(images=lowercase__    ,							return_tensors='pt' ).to(lowercase__ )

         # forward pass
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Dict    	=       model(**lowercase__ )

         # verify the logits
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.Size((1, 1_000) )
         self.assertEqual(outputs.logits.shape    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowercase__ )

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




'''simple docstring'''





import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



