





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__			     =    (DDIMScheduler,)
  lowerCamelCase__			     =    (("""eta""", 0.0), ("""num_inference_steps""", 50))

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				**UpperCAmelCase	)->					Optional[Any]:
     __A      					=   {
         '''num_train_timesteps''': 10_00,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**_lowercase	)
     return config

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				**UpperCAmelCase	)->					Optional[Any]:
     __A      					=   self.scheduler_classes[0]
     __A      					=   self.get_scheduler_config(**_lowercase	)
     __A      					=   scheduler_class(**_lowercase	)

     __A					,						__A      					=   10, 0.0

     __A      					=   self.dummy_model()
     __A      					=   self.dummy_sample_deter

     scheduler.set_timesteps(_lowercase	)

     for t in scheduler.timesteps:
        __A      					=   model(_lowercase  ,				_lowercase	)
        __A      					=   scheduler.step(_lowercase  ,				_lowercase  ,				_lowercase  ,				_lowercase	).prev_sample

     return sample

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					int:
     for timesteps in [1_00, 5_00, 10_00]:
        self.check_over_configs(num_train_timesteps=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					str:
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=_lowercase	)

     __A      					=   self.scheduler_classes[0]
     __A      					=   self.get_scheduler_config(steps_offset=1	)
     __A      					=   scheduler_class(**_lowercase	)
     scheduler.set_timesteps(5	)
     assert torch.equal(scheduler.timesteps  ,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]	)	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Dict:
     for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,				[0.002, 0.02, 0.2, 2]	):
        self.check_over_configs(beta_start=_lowercase  ,				beta_end=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					int:
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Dict:
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					int:
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					str:
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Any:
     self.check_over_configs(thresholding=_lowercase	)
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=_lowercase  ,				prediction_type=_lowercase  ,				sample_max_value=_lowercase  ,				)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Any:
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Tuple:
     for t, num_inference_steps in zip([1, 10, 50]  ,				[10, 50, 5_00]	):
        self.check_over_forward(time_step=_lowercase  ,				num_inference_steps=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					List[Any]:
     for t, eta in zip([1, 10, 49]  ,				[0.0, 0.5, 1.0]	):
        self.check_over_forward(time_step=_lowercase  ,				eta=_lowercase	)

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Optional[Any]:
     __A      					=   self.scheduler_classes[0]
     __A      					=   self.get_scheduler_config()
     __A      					=   scheduler_class(**_lowercase	)

     assert torch.sum(torch.abs(scheduler._get_variance(0  ,				0	) - 0.0	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_20  ,				4_00	) - 0.14771	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80  ,				9_60	) - 0.32460	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0  ,				0	) - 0.0	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87  ,				4_86	) - 0.00979	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99  ,				9_98	) - 0.02	)	) < 1e-5

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Tuple:
     __A      					=   self.full_loop()

     __A      					=   torch.sum(torch.abs(_lowercase	)	)
     __A      					=   torch.mean(torch.abs(_lowercase	)	)

     assert abs(result_sum.item() - 172.0067	) < 1e-2
     assert abs(result_mean.item() - 0.223967	) < 1e-3

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Any:
     __A      					=   self.full_loop(prediction_type='''v_prediction'''	)

     __A      					=   torch.sum(torch.abs(_lowercase	)	)
     __A      					=   torch.mean(torch.abs(_lowercase	)	)

     assert abs(result_sum.item() - 52.5302	) < 1e-2
     assert abs(result_mean.item() - 0.0684	) < 1e-3

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					str:
     # We specify different beta, so that the first alpha is 0.99
     __A      					=   self.full_loop(set_alpha_to_one=_lowercase  ,				beta_start=0.01	)
     __A      					=   torch.sum(torch.abs(_lowercase	)	)
     __A      					=   torch.mean(torch.abs(_lowercase	)	)

     assert abs(result_sum.item() - 149.8295	) < 1e-2
     assert abs(result_mean.item() - 0.1951	) < 1e-3





  def 		SCREAMING_SNAKE_CASE__       (					self	)->					List[str]:
     # We specify different beta, so that the first alpha is 0.99
     __A      					=   self.full_loop(set_alpha_to_one=_lowercase  ,				beta_start=0.01	)
     __A      					=   torch.sum(torch.abs(_lowercase	)	)
     __A      					=   torch.mean(torch.abs(_lowercase	)	)

     assert abs(result_sum.item() - 149.0784	) < 1e-2
     assert abs(result_mean.item() - 0.1941	) < 1e-3




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 		__magic_name__ :






     def __init__(							self						,		_lowercase						,		_lowercase=13						,		_lowercase=32						,		_lowercase=2						,		_lowercase=3						,		_lowercase=16						,		_lowercase=[1, 2, 1]						,		_lowercase=[2, 2, 4]						,		_lowercase=2						,		_lowercase=2.0						,		_lowercase=True						,		_lowercase=0.0						,		_lowercase=0.0						,		_lowercase=0.1						,		_lowercase="gelu"						,		_lowercase=False						,		_lowercase=True						,		_lowercase=0.02						,		_lowercase=1e-5						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=10						,		_lowercase=8						,		)->			Tuple:
         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      UpperCAmelCase_							(							self     )->			Dict:
         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      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         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						,		_lowercase						,		_lowercase						,		_lowercase     )->			Optional[Any]:
         UpperCamelCase_          =		DonutSwinModel(config=_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )

         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      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    
         )          =		config_and_inputs
         UpperCamelCase_          =		{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 		__magic_name__ (				snake_case       ,	snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :List[str]									=	(DonutSwinModel,) if is_torch_available() else ()
     UpperCamelCase_     :Optional[Any]									=	{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
     UpperCamelCase_     :Any									=	True

     UpperCamelCase_     :Tuple									=	False
     UpperCamelCase_     :List[str]									=	False
     UpperCamelCase_     :List[str]									=	False






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		DonutSwinModelTester(self     )
         UpperCamelCase_          =		ConfigTester(self						,		config_class=_lowercase						,		embed_dim=37     )






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         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






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			int:
         # DonutSwin does not use inputs_embeds
         pass






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             self.assertIsInstance(model.get_input_embeddings()						,		(nn.Module)     )
             UpperCamelCase_          =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowercase						,		nn.Linear     )     )






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             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]						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         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(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )
             UpperCamelCase_          =		outputs.attentions
             UpperCamelCase_          =		len(self.model_tester.depths     )
             self.assertEqual(len(_lowercase     )						,		_lowercase     )

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

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

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

             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(_lowercase     )     )

             UpperCamelCase_          =		outputs.attentions

             self.assertEqual(len(_lowercase     )						,		_lowercase     )

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		model_class(_lowercase     )
         model.to(_lowercase     )
         model.eval()

         with torch.no_grad():
             UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )

         UpperCamelCase_          =		outputs.hidden_states

         UpperCamelCase_          =		getattr(
             self.model_tester						,		"expected_num_hidden_layers"						,		len(self.model_tester.depths     ) + 1     )
         self.assertEqual(len(_lowercase     )						,		_lowercase     )

         # 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(_lowercase     )						,		_lowercase     )

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






     def      UpperCAmelCase_							(							self     )->			List[str]:
         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(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )

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

             self.check_hidden_states_output(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         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(_lowercase						,		_lowercase						,		_lowercase						,		(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(_lowercase						,		_lowercase						,		_lowercase						,		(padded_height, padded_width)     )






     @slow
     def      UpperCAmelCase_							(							self     )->			List[str]:
         for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_          =		DonutSwinModel.from_pretrained(_lowercase     )
             self.assertIsNotNone(_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_          =		_config_zero_init(_lowercase     )
         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(config=_lowercase     )
             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"						,		)
