



"""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     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Optional[Any]						,					snake_case:							Optional[int]						,					snake_case:							Optional[Any]=13						,					snake_case:							Dict=32						,					snake_case:							Tuple=2						,					snake_case:							Any=3						,					snake_case:							Tuple=16						,					snake_case:							Optional[Any]=[1, 2, 1]						,					snake_case:							List[str]=[2, 2, 4]						,					snake_case:							str=2						,					snake_case:							Union[str, Any]=2.0						,					snake_case:							int=True						,					snake_case:							Optional[int]=0.0						,					snake_case:							List[Any]=0.0						,					snake_case:							Tuple=0.1						,					snake_case:							Any="gelu"						,					snake_case:							Optional[Any]=False						,					snake_case:							int=True						,					snake_case:							Tuple=0.0_2						,					snake_case:							List[str]=1E-5						,					snake_case:							Optional[int]=True						,					snake_case:							Dict=None						,					snake_case:							List[str]=True						,					snake_case:							List[str]=10						,					snake_case:							Union[str, Any]=8						,					)       ->     Any:
											snake_case_			:Optional[int]						=      parent
											snake_case_			:int						=      batch_size
											snake_case_			:Tuple						=      image_size
											snake_case_			:str						=      patch_size
											snake_case_			:Union[str, Any]						=      num_channels
											snake_case_			:Any						=      embed_dim
											snake_case_			:List[str]						=      depths
											snake_case_			:Tuple						=      num_heads
											snake_case_			:Optional[int]						=      window_size
											snake_case_			:List[Any]						=      mlp_ratio
											snake_case_			:str						=      qkv_bias
											snake_case_			:Optional[int]						=      hidden_dropout_prob
											snake_case_			:Optional[int]						=      attention_probs_dropout_prob
											snake_case_			:Optional[Any]						=      drop_path_rate
											snake_case_			:List[Any]						=      hidden_act
											snake_case_			:Union[str, Any]						=      use_absolute_embeddings
											snake_case_			:Any						=      patch_norm
											snake_case_			:Optional[Any]						=      layer_norm_eps
											snake_case_			:List[str]						=      initializer_range
											snake_case_			:Dict						=      is_training
											snake_case_			:Dict						=      scope
											snake_case_			:Union[str, Any]						=      use_labels
											snake_case_			:str						=      type_sequence_label_size
											snake_case_			:Optional[Any]						=      encoder_stride






					def        lowerCAmelCase_    (	self:							List[str]						)       ->     List[str]:
											snake_case_			:int						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case_			:Union[str, Any]						=      None
											if self.use_labels:
																	snake_case_			:Dict						=      ids_tensor([self.batch_size]						,					self.type_sequence_label_size						)

											snake_case_			:Optional[int]						=      self.get_config()

											return config, pixel_values, labels






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     Tuple:
											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        lowerCAmelCase_    (	self:							List[Any]						,					snake_case:							Any						,					snake_case:							Optional[int]						,					snake_case:							Optional[int]						)       ->     Dict:
											snake_case_			:Dict						=      DonutSwinModel(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_			:str						=      model(snake_case						)

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

@require_torch
class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





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

					_A		:      Optional[int]						=					False
					_A		:      Optional[Any]						=					False
					_A		:      str						=					False






					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											snake_case_			:Tuple						=      DonutSwinModelTester(self						)
											snake_case_			:str						=      ConfigTester(self						,					config_class=snake_case						,					embed_dim=37						)






					def        lowerCAmelCase_    (	self:							Any						)       ->     Optional[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        lowerCAmelCase_    (	self:							List[str]						)       ->     List[Any]:
											return






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Dict:
											snake_case_			:Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case						)






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






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[Any]:
											snake_case_,	snake_case_			:Optional[int]						=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_			:List[str]						=      model_class(snake_case						)
																	self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)						)
																	snake_case_			:str						=      model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case						,					nn.Linear						)						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[int]:
											snake_case_,	snake_case_			:int						=      self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_			:Optional[Any]						=      ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,					snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Any:
											snake_case_,	snake_case_			:Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_			:Optional[Any]						=      True

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

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

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

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

																	if hasattr(self.model_tester						,					"""num_hidden_states_types"""						):
																							snake_case_			:Union[str, Any]						=      self.model_tester.num_hidden_states_types
																	else:
																							# also another +1 for reshaped_hidden_states
																							snake_case_			:Optional[Any]						=      2
																	self.assertEqual(out_len + added_hidden_states						,					len(snake_case						)						)

																	snake_case_			:List[Any]						=      outputs.attentions

																	self.assertEqual(len(snake_case						)						,					snake_case						)

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






					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							List[str]						,					snake_case:							Optional[Any]						,					snake_case:							Any						,					snake_case:							Union[str, Any]						)       ->     List[Any]:
											snake_case_			:Any						=      model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

											with torch.no_grad():
																	snake_case_			:Tuple						=      model(**self._prepare_for_class(snake_case						,					snake_case						)						)

											snake_case_			:Any						=      outputs.hidden_states

											snake_case_			:Dict						=      getattr(
											    self.model_tester						,					"""expected_num_hidden_layers"""						,					len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(snake_case						)						,					snake_case						)

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

											snake_case_			:Dict						=      (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]						,					)

											snake_case_			:Optional[Any]						=      outputs.reshaped_hidden_states
											self.assertEqual(len(snake_case						)						,					snake_case						)

											snake_case_,	snake_case_,	snake_case_,	snake_case_			:Optional[Any]						=      reshaped_hidden_states[0].shape
											snake_case_			:int						=      (
											    reshaped_hidden_states[0].view(snake_case						,					snake_case						,					height * width						).permute(0						,					2						,					1						)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]						)						,					[num_patches, self.model_tester.embed_dim]						,					)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     List[Any]:
											snake_case_,	snake_case_			:Optional[int]						=      self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_			: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:
																	snake_case_			:str						=      True
																	self.check_hidden_states_output(snake_case						,					snake_case						,					snake_case						,					snake_case						)

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

																	self.check_hidden_states_output(snake_case						,					snake_case						,					snake_case						,					snake_case						)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[Any]:
											snake_case_,	snake_case_			:Dict						=      self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_			:Tuple						=      3

											snake_case_			:Dict						=      (
											    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)
											)
											snake_case_			:str						=      (
											    config.patch_size
											    if isinstance(config.patch_size						,					collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

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

											for model_class in self.all_model_classes:
																	snake_case_			:str						=      True
																	self.check_hidden_states_output(snake_case						,					snake_case						,					snake_case						,					(padded_height, padded_width)						)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	snake_case_			:int						=      True
																	self.check_hidden_states_output(snake_case						,					snake_case						,					snake_case						,					(padded_height, padded_width)						)






					@slow
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[str]:
											for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_			:Tuple						=      DonutSwinModel.from_pretrained(snake_case						)
																	self.assertIsNotNone(snake_case						)






					def        lowerCAmelCase_    (	self:							Any						)       ->     Optional[Any]:
											snake_case_,	snake_case_			:str						=      self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_			:List[str]						=      _config_zero_init(snake_case						)
											for model_class in self.all_model_classes:
																	snake_case_			:Any						=      model_class(config=snake_case						)
																	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 gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					RePaintPipeline
					_A		:      List[Any]						=					IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_A		:      Union[str, Any]						=					PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_A		:      Tuple						=					IMAGE_INPAINTING_BATCH_PARAMS
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[str]:
											torch.manual_seed(0						)
											torch.manual_seed(0						)
											snake_case_			:Union[str, Any]						=      UNetaDModel(
											    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,					)
											snake_case_			:str						=      RePaintScheduler()
											snake_case_			:Any						=      {"""unet""": unet, """scheduler""": scheduler}
											return components






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Dict						,					snake_case:							List[Any]=0						)       ->     Tuple:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_			:int						=      torch.manual_seed(snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_			:Dict						=      np.random.RandomState(snake_case						).standard_normal((1, 3, 32, 32)						)
											snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Dict						=      (image > 0).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Union[str, Any]						=      {
											    """image""": image,
											    """mask_image""": mask,
											    """generator""": generator,
											    """num_inference_steps""": 5,
											    """eta""": 0.0,
											    """jump_length""": 2,
											    """jump_n_sample""": 2,
											    """output_type""": """numpy""",
											}
											return inputs






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											snake_case_			:int						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_			:List[str]						=      self.get_dummy_components()
											snake_case_			:Union[str, Any]						=      RePaintPipeline(**snake_case						)
											snake_case_			:Tuple						=      sd_pipe.to(snake_case						)
											sd_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_			:Tuple						=      self.get_dummy_inputs(snake_case						)
											snake_case_			:Dict						=      sd_pipe(**snake_case						).images
											snake_case_			:List[Any]						=      image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_			:Optional[int]						=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






					@skip_mps
					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""						)
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[Any]:
											return super().test_inference_batch_single_identical()






					@skip_mps
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[Any]:
											return super().test_save_load_optional_components()






					@skip_mps
					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											return super().test_attention_slicing_forward_pass()

@nightly
@require_torch_gpu
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     str:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											snake_case_			:Any						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""						)
											snake_case_			:Union[str, Any]						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
											snake_case_			:str						=      load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""						)

											snake_case_			:List[str]						=      """google/ddpm-ema-celebahq-256"""
											snake_case_			:Optional[int]						=      UNetaDModel.from_pretrained(snake_case						)
											snake_case_			:List[Any]						=      RePaintScheduler.from_pretrained(snake_case						)

											snake_case_			:str						=      RePaintPipeline(unet=snake_case						,					scheduler=snake_case						).to(snake_case						)
											repaint.set_progress_bar_config(disable=snake_case						)
											repaint.enable_attention_slicing()

											snake_case_			:int						=      torch.manual_seed(0						)
											snake_case_			:str						=      repaint(
											    snake_case						,					snake_case						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=snake_case						,					output_type="""np"""						,					)
											snake_case_			:List[str]						=      output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).mean() < 1E-2
