






"""simple docstring"""







import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
 import torch

 from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
 import pyanvml.pyanvml as nvml


lowerCAmelCase_		:			Optional[int]				   =					logging.get_logger(__name__)





class      UpperCamelCase_       (				a_     ):
  _A				:    PyTorchBenchmarkArguments
  _A				:    PretrainedConfig
  _A				:    str =		"PyTorch"



  @property
  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         return torch.__version__



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	float:



         """simple docstring"""





         UpperCAmelCase					=	self._prepare_inference_func(snake_case__       ,						snake_case__       ,						snake_case__ )
         return self._measure_speed(_inference )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	[Memory, Optional[MemorySummary]]:



         """simple docstring"""





         UpperCAmelCase					=	self._prepare_inference_func(snake_case__       ,						snake_case__       ,						snake_case__ )
         return self._measure_memory(_inference )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	float:



         """simple docstring"""





         UpperCAmelCase					=	self._prepare_train_func(snake_case__       ,						snake_case__       ,						snake_case__ )
         return self._measure_speed(_train )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	[Memory, Optional[MemorySummary]]:



         """simple docstring"""





         UpperCAmelCase					=	self._prepare_train_func(snake_case__       ,						snake_case__       ,						snake_case__ )
         return self._measure_memory(_train )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Callable[[], None]:



         """simple docstring"""





         UpperCAmelCase					=	self.config_dict[model_name]

         if self.args.torchscript:
                UpperCAmelCase					=	True

         UpperCAmelCase					=	(
             hasattr(snake_case__       ,						"""architectures""" )
             and isinstance(config.architectures       ,						snake_case__ )
             and len(config.architectures ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
                try:
                       UpperCAmelCase					=	config.architectures[0]
                       UpperCAmelCase					=	__import__("""transformers"""       ,						fromlist=[model_class] )
                       UpperCAmelCase					=	getattr(snake_case__       ,						snake_case__ )
                       UpperCAmelCase					=	model_cls(snake_case__ )
                except ImportError:
                       raise ImportError(
                           f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                           """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
         else:
                UpperCAmelCase					=	MODEL_MAPPING[config.__class__](snake_case__ )

         model.eval()
         model.to(self.args.device )

         # encoder-decoder has vocab size saved differently
         UpperCAmelCase					=	config.vocab_size if hasattr(snake_case__       ,						"""vocab_size""" ) else config.encoder.vocab_size
         UpperCAmelCase					=	torch.randint(snake_case__       ,						(batch_size, sequence_length)       ,						dtype=torch.long       ,						device=self.args.device )

         if self.args.fpaa:
                logger.info("""Running training in Mixed Precision...""" )
                if not self.args.is_gpu:
                       raise ValueError("""Mixed precision is possible only for GPU.""" )
                # amp seems to have memory leaks so that memory usage
                # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                model.half()

         if self.args.torchscript:
                with torch.no_grad():
                       UpperCAmelCase					=	torch.jit.trace(snake_case__       ,						snake_case__ )
         else:
                UpperCAmelCase					=	model

         def encoder_decoder_forward():
                with torch.no_grad():
                       UpperCAmelCase					=	inference_model(snake_case__       ,						decoder_input_ids=snake_case__ )
                return outputs

         def encoder_forward():
                with torch.no_grad():
                       UpperCAmelCase					=	inference_model(snake_case__ )
                return outputs

         UpperCAmelCase					=	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
         return _forward



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Callable[[], None]:



         """simple docstring"""





         UpperCAmelCase					=	self.config_dict[model_name]

         UpperCAmelCase					=	(
             hasattr(snake_case__       ,						"""architectures""" )
             and isinstance(config.architectures       ,						snake_case__ )
             and len(config.architectures ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
                try:
                       UpperCAmelCase					=	config.architectures[0]
                       UpperCAmelCase					=	__import__("""transformers"""       ,						fromlist=[model_class] )
                       UpperCAmelCase					=	getattr(snake_case__       ,						snake_case__ )
                       UpperCAmelCase					=	model_cls(snake_case__ )
                except ImportError:
                       raise ImportError(
                           f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                           """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
         else:
                UpperCAmelCase					=	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case__ )

         if self.args.torchscript:
                raise NotImplementedError("""Training for torchscript is currently not implemented""" )
         else:
                UpperCAmelCase					=	model

         model.train()
         model.to(self.args.device )

         # encoder-decoder has vocab size saved differently
         UpperCAmelCase					=	config.vocab_size if hasattr(snake_case__       ,						"""vocab_size""" ) else config.encoder.vocab_size
         UpperCAmelCase					=	torch.randint(snake_case__       ,						(batch_size, sequence_length)       ,						dtype=torch.long       ,						device=self.args.device )

         if self.args.fpaa:
                logger.info("""Running training in Mixed Precision...""" )
                if not self.args.is_gpu:
                       raise ValueError("""Mixed precision is possible only for GPU.""" )

                # amp seems to have memory leaks so that memory usage
                # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                model.half()

         def compute_loss_and_backprob_encoder():
                UpperCAmelCase					=	train_model(snake_case__       ,						labels=snake_case__ )[0]
                loss.backward()
                return loss

         def compute_loss_and_backprob_encoder_decoder():
                UpperCAmelCase					=	train_model(snake_case__       ,						decoder_input_ids=snake_case__       ,						labels=snake_case__ )[0]
                loss.backward()
                return loss

         UpperCAmelCase					=	(
             compute_loss_and_backprob_encoder_decoder
             if config.is_encoder_decoder
             else compute_loss_and_backprob_encoder
         )
         return _train



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	float:



         """simple docstring"""





         try:
                if self.args.is_tpu or self.args.torchscript:
                       # run additional 10 times to stabilize compilation for tpu and torchscript
                       logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation""" )
                       timeit.repeat(
                           snake_case__       ,						repeat=1       ,						number=5       ,						)

                # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                UpperCAmelCase					=	timeit.repeat(
                    snake_case__       ,						repeat=self.args.repeat       ,						number=10       ,						)

                if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                       import torch_xla.debug.metrics as met

                       self.print_fn(met.metrics_report() )

                return min(snake_case__ ) / 10.0
         except RuntimeError as e:
                self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
                return "N/A"



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	[Memory, MemorySummary]:



         """simple docstring"""





         try:
                if self.args.trace_memory_line_by_line:
                       UpperCAmelCase					=	start_memory_tracing("""transformers""" )

                if self.args.is_tpu:
                       # tpu
                       raise NotImplementedError(
                           """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
                           """ `--no-memory` or `args.memory=False`""" )
                elif self.args.is_gpu:
                       if not is_pyanvml_available():
                              logger.warning(
                                  """py3nvml not installed, we won't log GPU memory usage. """
                                  """Install py3nvml (pip install py3nvml) to log information about GPU.""" )
                              UpperCAmelCase					=	"""N/A"""
                       else:
                              logger.info(
                                  """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
                                  """ on the same GPU.""" )
                              # init nvml
                              nvml.nvmlInit()
                              func()
                              UpperCAmelCase					=	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
                              UpperCAmelCase					=	nvml.nvmlDeviceGetMemoryInfo(snake_case__ )
                              UpperCAmelCase					=	meminfo.used
                              UpperCAmelCase					=	Memory(snake_case__ )
                              # shutdown nvml
                              nvml.nvmlShutdown()
                else:
                       # cpu
                       UpperCAmelCase					=	measure_peak_memory_cpu(snake_case__ )
                       UpperCAmelCase					=	Memory(snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else memory_bytes

                if self.args.trace_memory_line_by_line:
                       UpperCAmelCase					=	stop_memory_tracing(snake_case__ )
                else:
                       UpperCAmelCase					=	None

                return memory, summary
         except RuntimeError as e:
                self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
                return "N/A", None








"""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      UpperCamelCase_       :



  def __init__(					self       ,						snake_case__       ,						snake_case__=13       ,						snake_case__=32       ,						snake_case__=2       ,						snake_case__=3       ,						snake_case__=16       ,						snake_case__=[1, 2, 1]       ,						snake_case__=[2, 2, 4]       ,						snake_case__=2       ,						snake_case__=2.0       ,						snake_case__=True       ,						snake_case__=0.0       ,						snake_case__=0.0       ,						snake_case__=0.1       ,						snake_case__="gelu"       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=0.02       ,						snake_case__=1e-5       ,						snake_case__=True       ,						snake_case__=None       ,						snake_case__=True       ,						snake_case__=10       ,						snake_case__=8       ,						)	->	Optional[int]:



         """simple docstring"""





         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]:



         """simple docstring"""





         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 )	->	List[str]:



         """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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModel(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(snake_case__ )

         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]:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				
         )					=	config_and_inputs
         UpperCAmelCase					=	{"""pixel_values""": pixel_values}
         return config, inputs_dict





@require_torch
class      UpperCamelCase_       (				a_      ,    a_      ,    unittest.TestCase     ):
  _A				:    Tuple =		(DonutSwinModel,) if is_torch_available() else ()
  _A				:    Any =		{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
  _A				:    Optional[Any] =		True

  _A				:    Dict =		False
  _A				:    Any =		False
  _A				:    Tuple =		False



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModelTester(self )
         UpperCAmelCase					=	ConfigTester(self       ,						config_class=snake_case__       ,						embed_dim=37 )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """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 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         return



  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         pass



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

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



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



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(snake_case__ )
                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]       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         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(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )
                UpperCAmelCase					=	outputs.attentions
                UpperCAmelCase					=	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"]
                UpperCAmelCase					=	True
                UpperCAmelCase					=	config.window_size**2
                UpperCAmelCase					=	model_class(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )
                UpperCAmelCase					=	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]       ,						)
                UpperCAmelCase					=	len(snake_case__ )

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

                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(snake_case__ ) )

                UpperCAmelCase					=	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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	model_class(snake_case__ )
         model.to(snake_case__ )
         model.eval()

         with torch.no_grad():
                UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )

         UpperCAmelCase					=	outputs.hidden_states

         UpperCAmelCase					=	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
         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(snake_case__ )       ,						snake_case__ )

         UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	reshaped_hidden_states[0].shape
         UpperCAmelCase					=	(
             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 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         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(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

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

                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         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(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"]
                UpperCAmelCase					=	True
                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						(padded_height, padded_width) )



  @slow
  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DonutSwinModel.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase					=	_config_zero_init(snake_case__ )
         for model_class in self.all_model_classes:
                UpperCAmelCase					=	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'''       ,						)
