





"""simple docstring"""




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						:       Dict,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       str=1_3,					lowerCAmelCase__						:       str=3_2,					lowerCAmelCase__						:       List[str]=2,					lowerCAmelCase__						:       Dict=3,					lowerCAmelCase__						:       Dict=1_6,					lowerCAmelCase__						:       Tuple=[1, 2, 1],					lowerCAmelCase__						:       Optional[int]=[2, 2, 4],					lowerCAmelCase__						:       Any=2,					lowerCAmelCase__						:       Optional[Any]=2.0,					lowerCAmelCase__						:       List[str]=True,					lowerCAmelCase__						:       int=0.0,					lowerCAmelCase__						:       Any=0.0,					lowerCAmelCase__						:       Optional[Any]=0.1,					lowerCAmelCase__						:       Any="gelu",					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       Optional[int]=True,					lowerCAmelCase__						:       Optional[int]=0.02,					lowerCAmelCase__						:       Dict=1e-5,					lowerCAmelCase__						:       Any=True,					lowerCAmelCase__						:       Union[str, Any]=None,					lowerCAmelCase__						:       List[Any]=True,					lowerCAmelCase__						:       Optional[Any]=1_0,					lowerCAmelCase__						:       Any=8,					)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       parent
										_UpperCamelCase     :   List[Any]      =       batch_size
										_UpperCamelCase     :   Optional[int]      =       image_size
										_UpperCamelCase     :   Tuple      =       patch_size
										_UpperCamelCase     :   Union[str, Any]      =       num_channels
										_UpperCamelCase     :   List[str]      =       embed_dim
										_UpperCamelCase     :   List[Any]      =       depths
										_UpperCamelCase     :   Tuple      =       num_heads
										_UpperCamelCase     :   Union[str, Any]      =       window_size
										_UpperCamelCase     :   Union[str, Any]      =       mlp_ratio
										_UpperCamelCase     :   List[Any]      =       qkv_bias
										_UpperCamelCase     :   Dict      =       hidden_dropout_prob
										_UpperCamelCase     :   List[str]      =       attention_probs_dropout_prob
										_UpperCamelCase     :   Dict      =       drop_path_rate
										_UpperCamelCase     :   Any      =       hidden_act
										_UpperCamelCase     :   Union[str, Any]      =       use_absolute_embeddings
										_UpperCamelCase     :   Dict      =       patch_norm
										_UpperCamelCase     :   int      =       layer_norm_eps
										_UpperCamelCase     :   List[Any]      =       initializer_range
										_UpperCamelCase     :   str      =       is_training
										_UpperCamelCase     :   Tuple      =       scope
										_UpperCamelCase     :   Optional[int]      =       use_labels
										_UpperCamelCase     :   Union[str, Any]      =       type_sequence_label_size
										_UpperCamelCase     :   List[str]      =       encoder_stride






				def 							snake_case      (					self						:       int				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

										_UpperCamelCase     :   str      =       self.get_config()

										return config, pixel_values, labels






				def 							snake_case      (					self						:       List[str]				)      -> List[Any]:




										'''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 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Optional[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       DonutSwinModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[int]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
										_UpperCamelCase     :   Union[str, 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 							snake_case      (					self						:       Union[str, Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				
										)     :   List[str]      =       config_and_inputs
										_UpperCamelCase     :   Tuple      =       {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							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 							snake_case      (					self						:       List[str]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       DonutSwinModelTester(self				)
										_UpperCamelCase     :   Union[str, Any]      =       ConfigTester(self,					config_class=lowerCAmelCase__,					embed_dim=3_7				)






				def 							snake_case      (					self						:       int				)      -> List[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 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										return






				def 							snake_case      (					self						:       Tuple				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> Dict:




										'''simple docstring'''



										pass






				def 							snake_case      (					self						:       Dict				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Any      =       model_class(lowerCAmelCase__				)
																self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)				)
																_UpperCamelCase     :   Union[str, Any]      =       model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase__,					nn.Linear				)				)






				def 							snake_case      (					self						:       List[str]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase     :   str      =       ['''pixel_values''']
																self.assertListEqual(arg_names[:1],					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCamelCase     :   Optional[Any]      =       True

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

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

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

																if hasattr(self.model_tester,					'''num_hidden_states_types'''				):
																						_UpperCamelCase     :   int      =       self.model_tester.num_hidden_states_types
																else:
																						# also another +1 for reshaped_hidden_states
																						_UpperCamelCase     :   Any      =       2
																self.assertEqual(out_len + added_hidden_states,					len(lowerCAmelCase__				)				)

																_UpperCamelCase     :   List[str]      =       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 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       model_class(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										with torch.no_grad():
																_UpperCamelCase     :   Optional[int]      =       model(**self._prepare_for_class(lowerCAmelCase__,					lowerCAmelCase__				)				)

										_UpperCamelCase     :   Any      =       outputs.hidden_states

										_UpperCamelCase     :   Tuple      =       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     :   List[Any]      =       (
										    config.patch_size
										    if isinstance(config.patch_size,					collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCamelCase     :   List[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],					)

										_UpperCamelCase     :   Union[str, Any]      =       outputs.reshaped_hidden_states
										self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

										_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       reshaped_hidden_states[0].shape
										_UpperCamelCase     :   Optional[Any]      =       (
										    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 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   int      =       (
										    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     :   int      =       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     :   Any      =       True

																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCamelCase     :   Any      =       3

										_UpperCamelCase     :   Tuple      =       (
										    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     :   Any      =       (
										    config.patch_size
										    if isinstance(config.patch_size,					collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCamelCase     :   Optional[int]      =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										_UpperCamelCase     :   int      =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Union[str, Any]      =       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     :   Dict      =       True
																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					(padded_height, padded_width)				)






				@slow
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase     :   Union[str, Any]      =       DonutSwinModel.from_pretrained(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   str      =       _config_zero_init(lowerCAmelCase__				)
										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Optional[Any]      =       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""",					)





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_           =    {
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SamModel",
      "SamPreTrainedModel",
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    [
      "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSamModel",
      "TFSamPreTrainedModel",
  ]
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_           =    ["SamImageProcessor"]


if TYPE_CHECKING:
  from .configuration_sam import (
      SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SamConfig,
      SamMaskDecoderConfig,
      SamPromptEncoderConfig,
      SamVisionConfig,
  )
  from .processing_sam import SamProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_sam import SamImageProcessor

else:
  import sys

  a_           =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




