
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 							A__		:
						def __init__(				self   ,  __magic_name__   ,  __magic_name__=1_3   ,  __magic_name__=3_2   ,  __magic_name__=2   ,  __magic_name__=3   ,  __magic_name__=1_6   ,  __magic_name__=[1, 2, 1]   ,  __magic_name__=[2, 2, 4]   ,  __magic_name__=2   ,  __magic_name__=2.0   ,  __magic_name__=True   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.1   ,  __magic_name__="gelu"   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=0.02   ,  __magic_name__=1e-5   ,  __magic_name__=True   ,  __magic_name__=None   ,  __magic_name__=True   ,  __magic_name__=1_0   ,  __magic_name__=8   ,  ):
							lowerCamelCase		:     str          =				parent
							lowerCamelCase		:     int          =				batch_size
							lowerCamelCase		:     str          =				image_size
							lowerCamelCase		:     Tuple          =				patch_size
							lowerCamelCase		:     List[Any]          =				num_channels
							lowerCamelCase		:     int          =				embed_dim
							lowerCamelCase		:     Optional[int]          =				depths
							lowerCamelCase		:     int          =				num_heads
							lowerCamelCase		:     int          =				window_size
							lowerCamelCase		:     Optional[int]          =				mlp_ratio
							lowerCamelCase		:     Tuple          =				qkv_bias
							lowerCamelCase		:     str          =				hidden_dropout_prob
							lowerCamelCase		:     str          =				attention_probs_dropout_prob
							lowerCamelCase		:     List[str]          =				drop_path_rate
							lowerCamelCase		:     Optional[int]          =				hidden_act
							lowerCamelCase		:     Dict          =				use_absolute_embeddings
							lowerCamelCase		:     List[Any]          =				patch_norm
							lowerCamelCase		:     Any          =				layer_norm_eps
							lowerCamelCase		:     Optional[int]          =				initializer_range
							lowerCamelCase		:     Any          =				is_training
							lowerCamelCase		:     str          =				scope
							lowerCamelCase		:     Optional[Any]          =				use_labels
							lowerCamelCase		:     Dict          =				type_sequence_label_size
							lowerCamelCase		:     Any          =				encoder_stride
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Union[str, Any]          =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCamelCase		:     Optional[int]          =				None
							if self.use_labels:
								lowerCamelCase		:     List[str]          =				ids_tensor([self.batch_size]   ,  self.type_sequence_label_size				)

							lowerCamelCase		:     str          =				self.get_config()

							return config, pixel_values, labels
						def 						UpperCamelCase__							(				self				):
							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 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
							lowerCamelCase		:     Tuple          =				DonutSwinModel(config=__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							lowerCamelCase		:     int          =				model(__magic_name__				)

							lowerCamelCase		:     Union[str, Any]          =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
							lowerCamelCase		:     Any          =				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 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     List[Any]          =				self.prepare_config_and_inputs()
							(
							    lowerCamelCase   
							)		:     List[str]          =				config_and_inputs
							lowerCamelCase		:     List[str]          =				{"""pixel_values""": pixel_values}
							return config, inputs_dict





@require_torch
class 							A__		(     __SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
						_UpperCAmelCase    :   Tuple   	=      (DonutSwinModel,) if is_torch_available() else ()
						_UpperCAmelCase    :   str   	=      {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
						_UpperCAmelCase    :   List[str]   	=      True

						_UpperCAmelCase    :   Union[str, Any]   	=      False
						_UpperCAmelCase    :   List[Any]   	=      False
						_UpperCAmelCase    :   Dict   	=      False
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Union[str, Any]          =				DonutSwinModelTester(self				)
							lowerCamelCase		:     int          =				ConfigTester(self   ,  config_class=__magic_name__   ,  embed_dim=3_7				)
						def 						UpperCamelCase__							(				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 						UpperCamelCase__							(				self				):
							return
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__				)
						def 						UpperCamelCase__							(				self				):
							# DonutSwin does not use inputs_embeds
							pass
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Dict          =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCamelCase		:     int          =				model_class(__magic_name__				)
								self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module)				)
								lowerCamelCase		:     Any          =				model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(__magic_name__   ,  nn.Linear				)				)
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Dict          =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCamelCase		:     Union[str, Any]          =				model_class(__magic_name__				)
								lowerCamelCase		:     Tuple          =				inspect.signature(model.forward				)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								lowerCamelCase		:     List[Any]          =				[*signature.parameters.keys()]

								lowerCamelCase		:     Any          =				["""pixel_values"""]
								self.assertListEqual(arg_names[:1]   ,  __magic_name__				)
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[int]          =				self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase		:     int          =				True

							for model_class in self.all_model_classes:
								lowerCamelCase		:     List[str]          =				True
								lowerCamelCase		:     Tuple          =				False
								lowerCamelCase		:     str          =				True
								lowerCamelCase		:     int          =				model_class(__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								with torch.no_grad():
									lowerCamelCase		:     List[str]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)
								lowerCamelCase		:     Union[str, Any]          =				outputs.attentions
								lowerCamelCase		:     Optional[int]          =				len(self.model_tester.depths				)
								self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

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

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

								# Check attention is always last and order is fine
								lowerCamelCase		:     Union[str, Any]          =				True
								lowerCamelCase		:     List[str]          =				True
								lowerCamelCase		:     str          =				model_class(__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								with torch.no_grad():
									lowerCamelCase		:     List[Any]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)

								if hasattr(self.model_tester   ,  """num_hidden_states_types"""				):
									lowerCamelCase		:     List[str]          =				self.model_tester.num_hidden_states_types
								else:
									# also another +1 for reshaped_hidden_states
									lowerCamelCase		:     Optional[int]          =				2
								self.assertEqual(out_len + added_hidden_states   ,  len(__magic_name__				)				)

								lowerCamelCase		:     List[Any]          =				outputs.attentions

								self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

								self.assertListEqual(
								    list(self_attentions[0].shape[-3:]				)   ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,  )
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
							lowerCamelCase		:     Dict          =				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()

							with torch.no_grad():
								lowerCamelCase		:     Any          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)

							lowerCamelCase		:     Dict          =				outputs.hidden_states

							lowerCamelCase		:     Optional[Any]          =				getattr(
							    self.model_tester   ,  """expected_num_hidden_layers"""   ,  len(self.model_tester.depths				) + 1				)
							self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

							# DonutSwin has a different seq_length
							lowerCamelCase		:     Union[str, Any]          =				(
							    config.patch_size
							    if isinstance(config.patch_size   ,  collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							lowerCamelCase		:     Any          =				(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]   ,  )

							lowerCamelCase		:     str          =				outputs.reshaped_hidden_states
							self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

							lowerCamelCase		:     List[Any]          =				reshaped_hidden_states[0].shape
							lowerCamelCase		:     Optional[int]          =				(
							    reshaped_hidden_states[0].view(__magic_name__   ,  __magic_name__   ,  height * width				).permute(0   ,  2   ,  1				)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]				)   ,  [num_patches, self.model_tester.embed_dim]   ,  )
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Optional[int]          =				self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase		:     Any          =				(
							    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:
								lowerCamelCase		:     str          =				True
								self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

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

								self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase		:     List[str]          =				3

							lowerCamelCase		:     Optional[Any]          =				(
							    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)
							)
							lowerCamelCase		:     Optional[int]          =				(
							    config.patch_size
							    if isinstance(config.patch_size   ,  collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							lowerCamelCase		:     Optional[Any]          =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							lowerCamelCase		:     Union[str, Any]          =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
								lowerCamelCase		:     Dict          =				True
								self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  (padded_height, padded_width)				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCamelCase		:     int          =				True
								self.check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  (padded_height, padded_width)				)
						@slow
						def 						UpperCamelCase__							(				self				):
							for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase		:     List[str]          =				DonutSwinModel.from_pretrained(__magic_name__				)
								self.assertIsNotNone(__magic_name__				)






						def 						UpperCamelCase__							(				self				):
							lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase		:     Tuple          =				_config_zero_init(__magic_name__				)
							for model_class in self.all_model_classes:
								lowerCamelCase		:     Union[str, Any]          =				model_class(config=__magic_name__				)
								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'''   ,  )






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # Creating a dictionary containing count of each word
 for word in sentence.split():
  occurrence[word] += 1
 return occurrence


if __name__ == "__main__":
      for word, count in word_occurrence("""INPUT STRING""").items():
            print(f'''{word}: {count}''')




