




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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(_lowerCAmelCase    ,    'hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(_lowerCAmelCase    ,    'num_attention_heads'						)						)





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Optional[int]=13    ,    _lowerCAmelCase  :		List[Any]=64    ,    _lowerCAmelCase  :		List[Any]=3    ,    _lowerCAmelCase  :		int=3    ,    _lowerCAmelCase  :		Union[str, Any]=2    ,    _lowerCAmelCase  :		int=1    ,    _lowerCAmelCase  :		Dict=16    ,    _lowerCAmelCase  :		str=[128, 256, 384]    ,    _lowerCAmelCase  :		Dict=[4, 6, 8]    ,    _lowerCAmelCase  :		Optional[Any]=[2, 3, 4]    ,    _lowerCAmelCase  :		Optional[Any]=[16, 16, 16]    ,    _lowerCAmelCase  :		List[str]=0    ,    _lowerCAmelCase  :		Optional[Any]=[2, 2, 2]    ,    _lowerCAmelCase  :		Optional[int]=[2, 2, 2]    ,    _lowerCAmelCase  :		Union[str, Any]=0.02    ,    _lowerCAmelCase  :		List[str]=True    ,    _lowerCAmelCase  :		Any=True    ,    _lowerCAmelCase  :		int=2    ,    ):
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      kernel_size
       SCREAMING_SNAKE_CASE_     						=      stride
       SCREAMING_SNAKE_CASE_     						=      padding
       SCREAMING_SNAKE_CASE_     						=      hidden_sizes
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      depths
       SCREAMING_SNAKE_CASE_     						=      key_dim
       SCREAMING_SNAKE_CASE_     						=      drop_path_rate
       SCREAMING_SNAKE_CASE_     						=      patch_size
       SCREAMING_SNAKE_CASE_     						=      attention_ratio
       SCREAMING_SNAKE_CASE_     						=      mlp_ratio
       SCREAMING_SNAKE_CASE_     						=      initializer_range
       SCREAMING_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],
       ]
       SCREAMING_SNAKE_CASE_     						=      is_training
       SCREAMING_SNAKE_CASE_     						=      use_labels
       SCREAMING_SNAKE_CASE_     						=      num_labels
       SCREAMING_SNAKE_CASE_     						=      initializer_range



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       SCREAMING_SNAKE_CASE_     						=      self.get_config()
       return config, pixel_values, labels



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




 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      config_and_inputs
       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': pixel_values}
       return config, inputs_dict





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


 '''simple docstring'''







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

 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      LevitModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase    ,    hidden_size=37						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       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 				lowerCAmelCase_  (							self  :		List[str]						):
       return



 @unittest.skip(reason='Levit does not use inputs_embeds'						)
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       pass



 @unittest.skip(reason='Levit does not support input and output embeddings'						)
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       pass



 @unittest.skip(reason='Levit does not output attentions'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     						=      ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       def check_hidden_states_output(_lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Optional[int]						):
             SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE_     						=      model(**self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase						)						)

             SCREAMING_SNAKE_CASE_     						=      outputs.hidden_states

             SCREAMING_SNAKE_CASE_     						=      len(self.model_tester.depths						) + 1
             self.assertEqual(len(_lowerCAmelCase						)    ,    _lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      (self.model_tester.image_size, self.model_tester.image_size)
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      image_size[0], image_size[1]
             for _ in range(4						):
                   SCREAMING_SNAKE_CASE_     						=      floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1						)
                   SCREAMING_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],
                 ]    ,    )

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     						=      True
             check_hidden_states_output(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)

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

             check_hidden_states_output(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[int]=False						):
       SCREAMING_SNAKE_CASE_     						=      super()._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase    ,    return_labels=_lowerCAmelCase						)

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

       return inputs_dict



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       if not self.model_tester.is_training:
             return

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

       for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(_lowerCAmelCase						)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue
             SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.train()
             SCREAMING_SNAKE_CASE_     						=      self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase    ,    return_labels=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						).loss
             loss.backward()



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_     						=      False
       SCREAMING_SNAKE_CASE_     						=      True

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



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_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(_lowerCAmelCase						),
                 ]
                 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_     						=      problem_type['title']
                         SCREAMING_SNAKE_CASE_     						=      problem_type['num_labels']

                         SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
                         model.to(_lowerCAmelCase						)
                         model.train()

                         SCREAMING_SNAKE_CASE_     						=      self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase    ,    return_labels=_lowerCAmelCase						)

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

                         SCREAMING_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=_lowerCAmelCase						) as warning_list:
                               SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message						):
                                     raise ValueError(
                                         F"Something is going wrong in the regression problem: intercepted {w.message}"						)

                         loss.backward()




 @slow
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_     						=      LevitModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)


def  UpperCAmelCase_							(				) ->			Optional[Any]:
      SCREAMING_SNAKE_CASE_     						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





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


 '''simple docstring'''







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




 @slow
 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
           _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      self.default_image_processor
       SCREAMING_SNAKE_CASE_     						=      prepare_img()
       SCREAMING_SNAKE_CASE_     						=      image_processor(images=_lowerCAmelCase    ,    return_tensors='pt'						).to(_lowerCAmelCase						)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

       # verify the logits
       SCREAMING_SNAKE_CASE_     						=      torch.Size((1, 1_000)						)
       self.assertEqual(outputs.logits.shape    ,    _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor([1.0448, -0.3745, -1.8317]						).to(_lowerCAmelCase						)

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





import gc
import threading
import time

import psutil
import torch





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      psutil.Process()
       SCREAMING_SNAKE_CASE_     						=      False



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      -1

       while True:
             SCREAMING_SNAKE_CASE_     						=      max(self.process.memory_info().rss    ,    self.cpu_memory_peak						)

             # can't sleep or will not catch the peak right (this comment is here on purpose)
             if not self.peak_monitoring:
                   break



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      threading.Thread(target=self.peak_monitor						)
       SCREAMING_SNAKE_CASE_     						=      True
       self.thread.start()




 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      False
       self.thread.join()
       return self.cpu_memory_peak


lowerCamelCase__			:       List[str]         =							PeakCPUMemory()

def  UpperCAmelCase_							(				) ->			Tuple:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      torch.cuda.memory_allocated(__UpperCAmelCase							)
      torch.cuda.reset_peak_memory_stats()

      return measures

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time() - start_measures['time']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
      SCREAMING_SNAKE_CASE_     						=      (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.max_memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20

      return measures





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Any							) ->			Optional[Any]:
      print(f"{description}:"							)
      print(f"- Time: {measures['time']:.2f}s"							)
      for i in range(torch.cuda.device_count()							):
            print(f"- GPU {i} allocated: {measures[str(__UpperCAmelCase							)]:.2f}MiB"							)
            SCREAMING_SNAKE_CASE_     						=      measures[f"{i}-peak"]
            print(f"- GPU {i} peak: {peak:.2f}MiB"							)
      print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
      print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)