






"""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


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       a_ ):
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			"PyTorch"






    @property
    def 				_UpperCamelCase							( self					):
          return torch.__version__






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          UpperCAmelCase							=       self._prepare_inference_func(snake_case__	,snake_case__	,snake_case__					)
          return self._measure_speed(_inference					)






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          UpperCAmelCase							=       self._prepare_inference_func(snake_case__	,snake_case__	,snake_case__					)
          return self._measure_memory(_inference					)






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          UpperCAmelCase							=       self._prepare_train_func(snake_case__	,snake_case__	,snake_case__					)
          return self._measure_speed(_train					)






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          UpperCAmelCase							=       self._prepare_train_func(snake_case__	,snake_case__	,snake_case__					)
          return self._measure_memory(_train					)






    def 				_UpperCamelCase							( self	,A	,A	,A					):
          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	,A	,A	,A					):
          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	,A					):





          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	,A					):





          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'''       ,						)
