




import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_MAPPING,
      DataaVecVisionForImageClassification,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
  )
  from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor
class 		UpperCAmelCase       :







      '''simple docstring'''





      def __init__(						self							:					Dict	,     __lowercase							:					int	,     __lowercase							:					List[Any]=1_00	,     __lowercase							:					Any=13	,     __lowercase							:					str=30	,     __lowercase							:					str=2	,     __lowercase							:					Optional[Any]=3	,     __lowercase							:					Optional[Any]=True	,     __lowercase							:					Optional[int]=True	,     __lowercase							:					Optional[Any]=32	,     __lowercase							:					str=4	,     __lowercase							:					Any=4	,     __lowercase							:					Dict=37	,     __lowercase							:					Union[str, Any]="gelu"	,     __lowercase							:					Union[str, Any]=0.1	,     __lowercase							:					List[Any]=0.1	,     __lowercase							:					Optional[Any]=10	,     __lowercase							:					Tuple=0.02	,     __lowercase							:					int=3	,     __lowercase							:					Union[str, Any]=None	,     __lowercase							:					Tuple=[0, 1, 2, 3]	,     ):






       """simple docstring"""






       snake_case_		   =					parent
       snake_case_		   =					1_00
       snake_case_		   =					batch_size
       snake_case_		   =					image_size
       snake_case_		   =					patch_size
       snake_case_		   =					num_channels
       snake_case_		   =					is_training
       snake_case_		   =					use_labels
       snake_case_		   =					hidden_size
       snake_case_		   =					num_hidden_layers
       snake_case_		   =					num_attention_heads
       snake_case_		   =					intermediate_size
       snake_case_		   =					hidden_act
       snake_case_		   =					hidden_dropout_prob
       snake_case_		   =					attention_probs_dropout_prob
       snake_case_		   =					type_sequence_label_size
       snake_case_		   =					initializer_range
       snake_case_		   =					scope
       snake_case_		   =					out_indices
       snake_case_		   =					num_labels

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       snake_case_		   =					(image_size // patch_size) ** 2
       snake_case_		   =					num_patches + 1






      def 	snake_case__						(						self							:					Optional[int]						):






       """simple docstring"""






       snake_case_		   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       snake_case_		   =					None
       snake_case_		   =					None
       if self.use_labels:
        snake_case_		   =					ids_tensor([self.batch_size]	,     self.type_sequence_label_size						)
        snake_case_		   =					ids_tensor([self.batch_size, self.image_size, self.image_size]	,     self.num_labels						)

       snake_case_		   =					self.get_config()

       return config, pixel_values, labels, pixel_labels






      def 	snake_case__						(						self							:					Any						):






       """simple docstring"""






       return DataaVecVisionConfig(
           vocab_size=self.vocab_size	,     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=__UpperCamelCase	,     initializer_range=self.initializer_range	,     out_indices=self.out_indices	,     )






      def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					Any	,     __lowercase							:					int	,     __lowercase							:					Any	,     __lowercase							:					Any						):






       """simple docstring"""






       snake_case_		   =					DataaVecVisionModel(config=__UpperCamelCase						)
       model.to(__UpperCamelCase						)
       model.eval()
       snake_case_		   =					model(__UpperCamelCase						)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       snake_case_		   =					(self.image_size // self.patch_size) ** 2
       self.parent.assertEqual(result.last_hidden_state.shape	,     (self.batch_size, num_patches + 1, self.hidden_size)						)






      def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Any	,     __lowercase							:					int	,     __lowercase							:					Dict	,     __lowercase							:					Dict						):






       """simple docstring"""






       snake_case_		   =					self.type_sequence_label_size
       snake_case_		   =					DataaVecVisionForImageClassification(__UpperCamelCase						)
       model.to(__UpperCamelCase						)
       model.eval()
       snake_case_		   =					model(__UpperCamelCase	,     labels=__UpperCamelCase						)
       self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.type_sequence_label_size)						)






      def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					Optional[Any]	,     __lowercase							:					Optional[int]	,     __lowercase							:					str						):






       """simple docstring"""






       snake_case_		   =					self.num_labels
       snake_case_		   =					DataaVecVisionForSemanticSegmentation(__UpperCamelCase						)
       model.to(__UpperCamelCase						)
       model.eval()
       snake_case_		   =					model(__UpperCamelCase						)
       self.parent.assertEqual(
           result.logits.shape	,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
       snake_case_		   =					model(__UpperCamelCase	,     labels=__UpperCamelCase						)
       self.parent.assertEqual(
           result.logits.shape	,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)






      def 	snake_case__						(						self							:					Dict						):






       """simple docstring"""






       snake_case_		   =					self.prepare_config_and_inputs()
       snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					config_and_inputs
       snake_case_		   =					{"pixel_values": pixel_values}
       return config, inputs_dict







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







      '''simple docstring'''





      lowerCAmelCase_							=       (
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      lowerCAmelCase_							=       (
          {
              '''feature-extraction''': DataaVecVisionModel,
              '''image-classification''': DataaVecVisionForImageClassification,
              '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase_							=       False
      lowerCAmelCase_							=       False
      lowerCAmelCase_							=       False






      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       snake_case_		   =					DataaVecVisionModelTester(self						)
       snake_case_		   =					ConfigTester(
           self	,     config_class=__UpperCamelCase	,     has_text_modality=__UpperCamelCase	,     hidden_size=37						)






      def 	snake_case__						(						self							:					List[Any]						):






       """simple docstring"""






       self.config_tester.run_common_tests()






      def 	snake_case__						(						self							:					List[Any]						):






       """simple docstring"""






       pass






      @require_torch_multi_gpu
      @unittest.skip(
          reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"						)
      def 	snake_case__						(						self							:					Optional[int]						):






       """simple docstring"""






       pass






      def 	snake_case__						(						self							:					int						):






       """simple docstring"""






       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(__UpperCamelCase						)
        self.assertIsInstance(model.get_input_embeddings()	,     (nn.Module)						)
        snake_case_		   =					model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__UpperCamelCase	,     nn.Linear						)						)






      def 	snake_case__						(						self							:					Any						):






       """simple docstring"""






       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(__UpperCamelCase						)
        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]	,     __UpperCamelCase						)






      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       snake_case_		   =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase						)






      def 	snake_case__						(						self							:					Any						):






       """simple docstring"""






       snake_case_		   =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*__UpperCamelCase						)






      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






       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:
        if model_class in [*get_values(__UpperCamelCase						)]:
         continue

        snake_case_		   =					model_class(__UpperCamelCase						)
        model.to(__UpperCamelCase						)
        model.train()
        snake_case_		   =					self._prepare_for_class(__UpperCamelCase	,     __UpperCamelCase	,     return_labels=__UpperCamelCase						)
        snake_case_		   =					model(**__UpperCamelCase						).loss
        loss.backward()






      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






       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(__UpperCamelCase						)] or not model_class.supports_gradient_checkpointing:
         continue
        # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
        # this can then be incorporated into _prepare_for_class in test_modeling_common.py
        elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
         snake_case_   ,				snake_case_   ,				snake_case_   ,				snake_case_		   =					inputs_dict["pixel_values"].shape
         snake_case_		   =					torch.zeros(
             [self.model_tester.batch_size, height, width]	,     device=__UpperCamelCase						).long()
        snake_case_		   =					model_class(__UpperCamelCase						)
        model.gradient_checkpointing_enable()
        model.to(__UpperCamelCase						)
        model.train()
        snake_case_		   =					self._prepare_for_class(__UpperCamelCase	,     __UpperCamelCase	,     return_labels=__UpperCamelCase						)
        snake_case_		   =					model(**__UpperCamelCase						).loss
        loss.backward()






      def 	snake_case__						(						self							:					Any						):






       """simple docstring"""






       snake_case_   ,				snake_case_		   =					self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_		   =					_config_zero_init(__UpperCamelCase						)
       for model_class in self.all_model_classes:
        snake_case_		   =					model_class(config=__UpperCamelCase						)
        for name, param in model.named_parameters():
         # we skip lambda parameters as these require special initial values
         # determined by config.layer_scale_init_value
         if "lambda" in name:
          continue
         if param.requires_grad:
          self.assertIn(
              ((param.data.mean() * 1E9).round() / 1E9).item()	,     [0.0, 1.0]	,     msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,     )






      def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					List[Any]	,     __lowercase							:					List[Any]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					int=2E-4	,     __lowercase							:					Dict="outputs"	,     __lowercase							:					Dict=None						):






       """simple docstring"""






       super().check_pt_tf_outputs(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase						)






      def 	snake_case__						(						self							:					Optional[int]						):






       """simple docstring"""






       snake_case_		   =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)






      @slow
      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






       for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case_		   =					DataaVecVisionModel.from_pretrained(__UpperCamelCase						)
        self.assertIsNotNone(__UpperCamelCase						)







def 						lowerCamelCase__   (		):







 '''simple docstring'''





 snake_case_		   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
 return image






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







      '''simple docstring'''





      @cached_property
      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       return (
           BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"						) if is_vision_available() else None
       )






      @slow
      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






       snake_case_		   =					DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"						).to(
           __UpperCamelCase						)

       snake_case_		   =					self.default_image_processor
       snake_case_		   =					prepare_img()
       snake_case_		   =					image_processor(images=__UpperCamelCase	,     return_tensors="pt"						).to(__UpperCamelCase						)

       # forward pass
       with torch.no_grad():
        snake_case_		   =					model(**__UpperCamelCase						)
       snake_case_		   =					outputs.logits

       # verify the logits
       snake_case_		   =					torch.Size((1, 10_00)						)
       self.assertEqual(logits.shape	,     __UpperCamelCase						)

       snake_case_		   =					torch.tensor([0.3277, -0.1395, 0.0911]						).to(__UpperCamelCase						)

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

       snake_case_		   =					[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
       self.assertEqual(logits[0].topk(2						).indices.cpu().tolist()	,     __UpperCamelCase						)






def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if right <= left:
		return
	snake_case_		   =					snake_case_		   =					left
	snake_case_		   =					right
	snake_case_		   =					sorting[left]
	while i <= b:
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[a]
			a += 1
			i += 1
		elif sorting[i] > pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[b]
			b -= 1
		else:
			i += 1
	quick_sort_apartition(_A					,     _A					,     a - 1							)
	quick_sort_apartition(_A					,     b + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if left < right:
		snake_case_		   =					lomuto_partition(_A					,     _A					,     _A							)
		quick_sort_lomuto_partition(_A					,     _A					,     pivot_index - 1							)
		quick_sort_lomuto_partition(_A					,     pivot_index + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					sorting[right]
	snake_case_		   =					left
	for i in range(_A					,     _A							):
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[store_index]
			store_index += 1
	snake_case_   ,				snake_case_		   =					sorting[store_index], sorting[right]
	return store_index


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	if len(_A							) <= 1:
		return sorting
	return (
	    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
	    + [i for i in sorting if i == sorting[0]]
	    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)

		lowercase__    :					Tuple					=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__    :					int					=						[int(item) for item in user_input.split(",")]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)
