




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

       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 :Optional[Any]   )  ->      Optional[int]:
       UpperCAmelCase__							=    self.prepare_config_and_inputs()
       (
           UpperCAmelCase__							
       )							=    config_and_inputs
       UpperCAmelCase__							=    {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_torch
class       _UpperCamelCase   (					UpperCamelCase__						,							UpperCamelCase__						,							unittest.TestCase		):
      UpperCAmelCase_          =		(DonutSwinModel,) if is_torch_available() else ()
      UpperCAmelCase_          =		{"feature-extraction": DonutSwinModel} if is_torch_available() else {}
      UpperCAmelCase_          =		True

      UpperCAmelCase_          =		False
      UpperCAmelCase_          =		False
      UpperCAmelCase_          =		False
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Any:
       UpperCAmelCase__							=    DonutSwinModelTester(self   )
       UpperCAmelCase__							=    ConfigTester(self			,   config_class=_UpperCAmelCase			,   embed_dim=37   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Tuple:
       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]   )  ->      int:
       return
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :Any   )  ->      List[Any]:
       # DonutSwin does not use inputs_embeds
       pass
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							=    model_class(_UpperCAmelCase   )
        self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module)   )
        UpperCAmelCase__							=    model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase			,   nn.Linear   )   )
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							=    model_class(_UpperCAmelCase   )
        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]			,   _UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      List[Any]:
       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(_UpperCAmelCase   )
        model.to(_UpperCAmelCase   )
        model.eval()
        with torch.no_grad():
         UpperCAmelCase__							=    model(**self._prepare_for_class(_UpperCAmelCase			,   _UpperCAmelCase   )   )
        UpperCAmelCase__							=    outputs.attentions
        UpperCAmelCase__							=    len(self.model_tester.depths   )
        self.assertEqual(len(_UpperCAmelCase   )			,   _UpperCAmelCase   )

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

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

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

        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(_UpperCAmelCase   )   )

        UpperCAmelCase__							=    outputs.attentions

        self.assertEqual(len(_UpperCAmelCase   )			,   _UpperCAmelCase   )

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]   )			,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,   )
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :str			,   lowerCamelCase :str			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :int   )  ->      Dict:
       UpperCAmelCase__							=    model_class(_UpperCAmelCase   )
       model.to(_UpperCAmelCase   )
       model.eval()

       with torch.no_grad():
        UpperCAmelCase__							=    model(**self._prepare_for_class(_UpperCAmelCase			,   _UpperCAmelCase   )   )

       UpperCAmelCase__							=    outputs.hidden_states

       UpperCAmelCase__							=    getattr(
           self.model_tester			,   "expected_num_hidden_layers"			,   len(self.model_tester.depths   ) + 1   )
       self.assertEqual(len(_UpperCAmelCase   )			,   _UpperCAmelCase   )

       # 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(_UpperCAmelCase   )			,   _UpperCAmelCase   )

       UpperCAmelCase__							=    reshaped_hidden_states[0].shape
       UpperCAmelCase__							=    (
           reshaped_hidden_states[0].view(_UpperCAmelCase			,   _UpperCAmelCase			,   height * width   ).permute(0			,   2			,   1   )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]   )			,   [num_patches, self.model_tester.embed_dim]			,   )
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      Tuple:
       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(_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase   )

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

        self.check_hidden_states_output(_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Optional[Any]:
       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(_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   (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(_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   (padded_height, padded_width)   )
      @slow
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Union[str, Any]:
       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							=    DonutSwinModel.from_pretrained(_UpperCAmelCase   )
        self.assertIsNotNone(_UpperCAmelCase   )







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

       UpperCAmelCase__							=    _config_zero_init(_UpperCAmelCase   )
       for model_class in self.all_model_classes:
        UpperCAmelCase__							=    model_class(config=_UpperCAmelCase   )
        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'''			,   )













'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					(DDIMScheduler,)
      SCREAMING_SNAKE_CASE				:    str				       =					(("eta", 0.0), ("num_inference_steps", 5_0))




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   List[str]												=	{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.00_01,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**_UpperCAmelCase						)
             return config




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_scheduler_config(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	scheduler_class(**_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	10, 0.0

             UpperCAmelCase__	:   Optional[Any]												=	self.dummy_model()
             UpperCAmelCase__	:   List[Any]												=	self.dummy_sample_deter

             scheduler.set_timesteps(_UpperCAmelCase						)

             for t in scheduler.timesteps:
                    UpperCAmelCase__	:   Dict												=	model(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   int												=	scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return sample




      def 							lowerCamelCase     (					self						):
             for timesteps in [100, 500, 1000]:
                    self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Any												=	self.get_scheduler_config(steps_offset=1						)
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(5						)
             assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]						)						)




      def 							lowerCamelCase     (					self						):
             for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,		[0.0_02, 0.02, 0.2, 2]						):
                    self.check_over_configs(beta_start=_UpperCAmelCase  ,		beta_end=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             self.check_over_configs(thresholding=_UpperCAmelCase						)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=_UpperCAmelCase  ,		prediction_type=_UpperCAmelCase  ,		sample_max_value=_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             for t in [1, 10, 49]:
                    self.check_over_forward(time_step=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		num_inference_steps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		eta=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   int												=	self.get_scheduler_config()
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400						) - 0.1_47_71						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960						) - 0.3_24_60						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487  ,		486						) - 0.0_09_79						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998						) - 0.02						)						) < 1E-5




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.full_loop()

             UpperCAmelCase__	:   Dict												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   str												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67						) < 1E-3




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Any												=	self.full_loop(prediction_type='''v_prediction'''						)

             UpperCAmelCase__	:   Union[str, Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Tuple												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 52.53_02						) < 1E-2
             assert abs(result_mean.item() - 0.06_84						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   List[str]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
             assert abs(result_mean.item() - 0.19_51						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   Optional[Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   int												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
             assert abs(result_mean.item() - 0.19_41						) < 1E-3