




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    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Optional[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					int=13 ,				__lowerCamelCase    :					List[str]=32 ,				__lowerCamelCase    :					int=2 ,				__lowerCamelCase    :					int=3 ,				__lowerCamelCase    :					Any=16 ,				__lowerCamelCase    :					Tuple=[1, 2, 1] ,				__lowerCamelCase    :					int=[2, 2, 4] ,				__lowerCamelCase    :					int=2 ,				__lowerCamelCase    :					List[str]=2.0 ,				__lowerCamelCase    :					Tuple=True ,				__lowerCamelCase    :					Union[str, Any]=0.0 ,				__lowerCamelCase    :					Union[str, Any]=0.0 ,				__lowerCamelCase    :					Tuple=0.1 ,				__lowerCamelCase    :					Optional[Any]="gelu" ,				__lowerCamelCase    :					List[Any]=False ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					List[str]=0.02 ,				__lowerCamelCase    :					str=1e-5 ,				__lowerCamelCase    :					Tuple=True ,				__lowerCamelCase    :					int=None ,				__lowerCamelCase    :					Optional[int]=True ,				__lowerCamelCase    :					str=10 ,				__lowerCamelCase    :					Optional[Any]=8 ,				) ->					Tuple:
							a   =      parent
							a   =      batch_size
							a   =      image_size
							a   =      patch_size
							a   =      num_channels
							a   =      embed_dim
							a   =      depths
							a   =      num_heads
							a   =      window_size
							a   =      mlp_ratio
							a   =      qkv_bias
							a   =      hidden_dropout_prob
							a   =      attention_probs_dropout_prob
							a   =      drop_path_rate
							a   =      hidden_act
							a   =      use_absolute_embeddings
							a   =      patch_norm
							a   =      layer_norm_eps
							a   =      initializer_range
							a   =      is_training
							a   =      scope
							a   =      use_labels
							a   =      type_sequence_label_size
							a   =      encoder_stride





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					str:
							a   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							a   =      None
							if self.use_labels:
										a   =      ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)

							a   =      self.get_config()

							return config, pixel_values, labels





				def 							__UpperCAmelCase							(				self    :					int							) ->					str:
							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    :					List[str] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      DonutSwinModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a   =      model(__lowerCamelCase							)

							a   =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							a   =      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    :					str							) ->					Dict:
							a   =      self.prepare_config_and_inputs()
							(
							    (
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		
							)   =      config_and_inputs
							a   =      {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Optional[int]										= (DonutSwinModel,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_	:							List[str]										= {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
				SCREAMING_SNAKE_CASE_	:							List[Any]										= True

				SCREAMING_SNAKE_CASE_	:							List[str]										= False
				SCREAMING_SNAKE_CASE_	:							List[Any]										= False
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= False





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Tuple:
							a   =      DonutSwinModelTester(self							)
							a   =      ConfigTester(self ,				config_class=__lowerCamelCase ,				embed_dim=37							)





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Any:
							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    :					int							) ->					List[Any]:
							return





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					int:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Optional[int]:
							# DonutSwin does not use inputs_embeds
							pass





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Union[str, Any]:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

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





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Dict:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

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

										a   =      ["pixel_values"]
										self.assertListEqual(arg_names[:1] ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					int:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
							a   =      True

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

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

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

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

										a   =      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 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Any							) ->					Tuple:
							a   =      model_class(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							with torch.no_grad():
										a   =      model(**self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)							)

							a   =      outputs.hidden_states

							a   =      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
							a   =      (
							    config.patch_size
							    if isinstance(config.patch_size ,				collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							a   =      (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] ,				)

							a   =      outputs.reshaped_hidden_states
							self.assertEqual(len(__lowerCamelCase							) ,				__lowerCamelCase							)

							a   ,		a   ,		a   ,		a   =      reshaped_hidden_states[0].shape
							a   =      (
							    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 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

							a   =      (
							    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:
										a   =      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"]
										a   =      True

										self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					int:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
							a   =      3

							a   =      (
							    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)
							)
							a   =      (
							    config.patch_size
							    if isinstance(config.patch_size ,				collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

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

							for model_class in self.all_model_classes:
										a   =      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"]
										a   =      True
										self.check_hidden_states_output(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				(padded_height, padded_width)							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a   =      DonutSwinModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[Any]:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

							a   =      _config_zero_init(__lowerCamelCase							)
							for model_class in self.all_model_classes:
										a   =      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""" ,				)












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






