import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 DonutSwinModel
     from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE_		:







   def __init__(    self	:	List[str]      ,      lowerCamelCase_	:	List[Any]      ,      lowerCamelCase_	:	List[Any]=13      ,      lowerCamelCase_	:	int=32      ,      lowerCamelCase_	:	Optional[Any]=2      ,      lowerCamelCase_	:	List[str]=3      ,      lowerCamelCase_	:	Any=16      ,      lowerCamelCase_	:	Dict=[1, 2, 1]      ,      lowerCamelCase_	:	int=[2, 2, 4]      ,      lowerCamelCase_	:	List[str]=2      ,      lowerCamelCase_	:	Optional[int]=2.0      ,      lowerCamelCase_	:	Optional[int]=True      ,      lowerCamelCase_	:	Dict=0.0      ,      lowerCamelCase_	:	Dict=0.0      ,      lowerCamelCase_	:	Dict=0.1      ,      lowerCamelCase_	:	List[Any]="gelu"      ,      lowerCamelCase_	:	Union[str, Any]=False      ,      lowerCamelCase_	:	Tuple=True      ,      lowerCamelCase_	:	int=0.0_2      ,      lowerCamelCase_	:	Dict=1E-5      ,      lowerCamelCase_	:	str=True      ,      lowerCamelCase_	:	Optional[Any]=None      ,      lowerCamelCase_	:	Dict=True      ,      lowerCamelCase_	:	str=10      ,      lowerCamelCase_	:	Tuple=8      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			image_size
    UpperCamelCase      =			patch_size
    UpperCamelCase      =			num_channels
    UpperCamelCase      =			embed_dim
    UpperCamelCase      =			depths
    UpperCamelCase      =			num_heads
    UpperCamelCase      =			window_size
    UpperCamelCase      =			mlp_ratio
    UpperCamelCase      =			qkv_bias
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			drop_path_rate
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			use_absolute_embeddings
    UpperCamelCase      =			patch_norm
    UpperCamelCase      =			layer_norm_eps
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			is_training
    UpperCamelCase      =			scope
    UpperCamelCase      =			use_labels
    UpperCamelCase      =			type_sequence_label_size
    UpperCamelCase      =			encoder_stride







   def 	lowerCamelCase_  (    self	:	str							):





    """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 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    return DonutSwinConfig(
        image_size=self.image_size      ,      patch_size=self.patch_size      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      depths=self.depths      ,      num_heads=self.num_heads      ,      window_size=self.window_size      ,      mlp_ratio=self.mlp_ratio      ,      qkv_bias=self.qkv_bias      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      drop_path_rate=self.drop_path_rate      ,      hidden_act=self.hidden_act      ,      use_absolute_embeddings=self.use_absolute_embeddings      ,      path_norm=self.patch_norm      ,      layer_norm_eps=self.layer_norm_eps      ,      initializer_range=self.initializer_range      ,      encoder_stride=self.encoder_stride      ,      )







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			DonutSwinModel(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()
    UpperCamelCase      =			model(lowerCamelCase_							)

    UpperCamelCase      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
    UpperCamelCase      =			int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

    self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, expected_seq_len, expected_dim)							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    __lowerCAmelCase     ,    unittest.TestCase		):
   __lowerCAmelCase											=							(DonutSwinModel,) if is_torch_available() else ()
   __lowerCAmelCase											=							{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
   __lowerCAmelCase											=							True

   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			DonutSwinModelTester(self							)
    UpperCamelCase      =			ConfigTester(self      ,      config_class=lowerCamelCase_      ,      embed_dim=37							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    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	:	Dict							):





    """simple docstring"""


    return







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    pass







   def 	lowerCamelCase_  (    self	:	List[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(lowerCamelCase_							)
     self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)							)
     UpperCamelCase      =			model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase_      ,      nn.Linear							)							)







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     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]      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


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

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     UpperCamelCase      =			False
     UpperCamelCase      =			True
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)
     UpperCamelCase      =			outputs.attentions
     UpperCamelCase      =			len(self.model_tester.depths							)
     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     UpperCamelCase      =			True
     UpperCamelCase      =			config.window_size**2
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)
     UpperCamelCase      =			outputs.attentions
     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     self.assertListEqual(
         list(attentions[0].shape[-3:]							)      ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,      )
     UpperCamelCase      =			len(lowerCamelCase_							)

     # Check attention is always last and order is fine
     UpperCamelCase      =			True
     UpperCamelCase      =			True
     UpperCamelCase      =			model_class(lowerCamelCase_							)
     model.to(lowerCamelCase_							)
     model.eval()
     with torch.no_grad():
      UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)

     if hasattr(self.model_tester      ,      """num_hidden_states_types"""							):
      UpperCamelCase      =			self.model_tester.num_hidden_states_types
     else:
      # also another +1 for reshaped_hidden_states
      UpperCamelCase      =			2
     self.assertEqual(out_len + added_hidden_states      ,      len(lowerCamelCase_							)							)

     UpperCamelCase      =			outputs.attentions

     self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

     self.assertListEqual(
         list(self_attentions[0].shape[-3:]							)      ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,      )







   def 	lowerCamelCase_  (    self	:	Tuple      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			model_class(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()

    with torch.no_grad():
     UpperCamelCase      =			model(**self._prepare_for_class(lowerCamelCase_      ,      lowerCamelCase_							)							)

    UpperCamelCase      =			outputs.hidden_states

    UpperCamelCase      =			getattr(
        self.model_tester      ,      """expected_num_hidden_layers"""      ,      len(self.model_tester.depths							) + 1							)
    self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

    # DonutSwin has a different seq_length
    UpperCamelCase      =			(
        config.patch_size
        if isinstance(config.patch_size      ,      collections.abc.Iterable							)
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]							)      ,      [num_patches, self.model_tester.embed_dim]      ,      )

    UpperCamelCase      =			outputs.reshaped_hidden_states
    self.assertEqual(len(lowerCamelCase_							)      ,      lowerCamelCase_							)

    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			reshaped_hidden_states[0].shape
    UpperCamelCase      =			(
        reshaped_hidden_states[0].view(lowerCamelCase_      ,      lowerCamelCase_      ,      height * width							).permute(0      ,      2      ,      1							)
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-2:]							)      ,      [num_patches, self.model_tester.embed_dim]      ,      )







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase      =			(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable							)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							)

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

     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase      =			3

    UpperCamelCase      =			(
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size      ,      collections.abc.Iterable							)
        else (self.model_tester.image_size, self.model_tester.image_size)
    )
    UpperCamelCase      =			(
        config.patch_size
        if isinstance(config.patch_size      ,      collections.abc.Iterable							)
        else (config.patch_size, config.patch_size)
    )

    UpperCamelCase      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    UpperCamelCase      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      (padded_height, padded_width)							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCamelCase      =			True
     self.check_hidden_states_output(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      (padded_height, padded_width)							)







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





    """simple docstring"""


    for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCamelCase      =			DonutSwinModel.from_pretrained(lowerCamelCase_							)
     self.assertIsNotNone(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    UpperCamelCase      =			_config_zero_init(lowerCamelCase_							)
    for model_class in self.all_model_classes:
     UpperCamelCase      =			model_class(config=lowerCamelCase_							)
     for name, param in model.named_parameters():
      if "embeddings" not in name and 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  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			len(UpperCamelCase_					)
 for i in range(length - 1					):
  UpperCamelCase      =			False
  for j in range(length - 1 - i					):
   if collection[j] > collection[j + 1]:
    UpperCamelCase      =			True
    UpperCamelCase     ,    UpperCamelCase      =			collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
     import doctest
     import time

     doctest.testmod()

     _SCREAMING_SNAKE_CASE   		=			input("""Enter numbers separated by a comma:""").strip()
     _SCREAMING_SNAKE_CASE   		=			[int(item) for item in user_input.split(""",""")]
     _SCREAMING_SNAKE_CASE   		=			time.process_time()
     print(*bubble_sort(unsorted), sep=""",""")
     print(F'''Processing time: {(time.process_time() - start)%1E9 + 7}''')
