code: 
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  A_		:			Any										=   getLogger(__name__)
  
  
  A_		:			List[Any]										=   'cuda' if torch.cuda.is_available() else 'cpu'
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE = 8       ,   SCREAMING_SNAKE_CASE = DEFAULT_DEVICE       ,   SCREAMING_SNAKE_CASE=False       ,   SCREAMING_SNAKE_CASE="summarization"       ,   SCREAMING_SNAKE_CASE=None       ,   **SCREAMING_SNAKE_CASE       ,   )      ->     Dict:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			Path(SCREAMING_SNAKE_CASE      ).open('''w'''       ,   encoding='''utf-8'''      )
      __UpperCAmelCase									=			str(SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )
      if fpaa:
          __UpperCAmelCase									=			model.half()
  
      __UpperCAmelCase									=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''      )  # if this is wrong, check config.model_type.
  
      __UpperCAmelCase									=			time.time()
      # update config with task specific params
      use_task_specific_params(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      if prefix is None:
          __UpperCAmelCase									=			prefix or getattr(model.config       ,   '''prefix'''       ,   ''''''      ) or ''''''
      for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      )      ):
          __UpperCAmelCase									=			[prefix + text for text in examples_chunk]
          __UpperCAmelCase									=			tokenizer(SCREAMING_SNAKE_CASE       ,   return_tensors='''pt'''       ,   truncation=SCREAMING_SNAKE_CASE       ,   padding='''longest'''      ).to(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			model.generate(
              input_ids=batch.input_ids       ,   attention_mask=batch.attention_mask       ,   **SCREAMING_SNAKE_CASE       ,   )
          __UpperCAmelCase									=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE       ,   skip_special_tokens=SCREAMING_SNAKE_CASE       ,   clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
          for hypothesis in dec:
              fout.write(hypothesis + '''\n'''      )
              fout.flush()
      fout.close()
      __UpperCAmelCase									=			int(time.time() - start_time      )  # seconds
      __UpperCAmelCase									=			len(SCREAMING_SNAKE_CASE      )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs       ,   4      )}
  
  
  def       __a       (   )      ->     List[str]:
  
  
  
  
  
  
      '''simple docstring'''
      return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE=True      )      ->     List[str]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			argparse.ArgumentParser()
      parser.add_argument('''model_name'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''      )
      parser.add_argument('''input_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''like cnn_dm/test.source'''      )
      parser.add_argument('''save_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''where to save summaries'''      )
      parser.add_argument('''--reference_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   help='''like cnn_dm/test.target'''      )
      parser.add_argument('''--score_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default='''metrics.json'''       ,   help='''where to save metrics'''      )
      parser.add_argument('''--device'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   help='''cuda, cuda:1, cpu etc.'''      )
      parser.add_argument(
          '''--prefix'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   help='''will be added to the begininng of src examples'''      )
      parser.add_argument('''--task'''       ,   type=SCREAMING_SNAKE_CASE       ,   default='''summarization'''       ,   help='''used for task_specific_params + metrics'''      )
      parser.add_argument('''--bs'''       ,   type=SCREAMING_SNAKE_CASE       ,   default=8       ,   required=SCREAMING_SNAKE_CASE       ,   help='''batch size'''      )
      parser.add_argument(
          '''--n_obs'''       ,   type=SCREAMING_SNAKE_CASE       ,   default=-1       ,   required=SCREAMING_SNAKE_CASE       ,   help='''How many observations. Defaults to all.'''      )
      parser.add_argument('''--fp16'''       ,   action='''store_true'''      )
      parser.add_argument('''--dump-args'''       ,   action='''store_true'''       ,   help='''print the custom hparams with the results'''      )
      parser.add_argument(
          '''--info'''       ,   nargs='''?'''       ,   type=SCREAMING_SNAKE_CASE       ,   const=datetime_now()       ,   help=(
              '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
              ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
          )       ,   )
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_known_args()
      __UpperCAmelCase									=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE      )
      if parsed_args and verbose:
          print(f'''parsed the following generate kwargs: {parsed_args}'''      )
      with open(args.input_path      ) as f:
          __UpperCAmelCase									=			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
          __UpperCAmelCase									=			examples[: args.n_obs]
      Path(args.save_path      ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
      if args.reference_path is None and Path(args.score_path      ).exists():
          warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''      )
      __UpperCAmelCase									=			generate_summaries_or_translations(
          SCREAMING_SNAKE_CASE       ,   args.save_path       ,   args.model_name       ,   batch_size=args.bs       ,   device=args.device       ,   fpaa=args.fpaa       ,   task=args.task       ,   prefix=args.prefix       ,   **SCREAMING_SNAKE_CASE       ,   )
  
      if args.reference_path is None:
          return {}
  
      # Compute scores
      __UpperCAmelCase									=			calculate_bleu if '''translation''' in args.task else calculate_rouge
      __UpperCAmelCase									=			[x.rstrip() for x in open(args.save_path      ).readlines()]
      __UpperCAmelCase									=			[x.rstrip() for x in open(args.reference_path      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
      __UpperCAmelCase									=			score_fn(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      scores.update(SCREAMING_SNAKE_CASE      )
  
      if args.dump_args:
          scores.update(SCREAMING_SNAKE_CASE      )
      if args.info:
          __UpperCAmelCase									=			args.info
  
      if verbose:
          print(SCREAMING_SNAKE_CASE      )
  
      if args.score_path is not None:
          json.dump(SCREAMING_SNAKE_CASE       ,   open(args.score_path       ,   '''w'''      )      )
  
      return scores
  
  
  if __name__ == "__main__":
         # Usage for MT:
         # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
         run_generate(verbose=True)
  
  
  
  
  
style_context: 
  
  import collections
  import inspect
  import unittest
  
  from transformers import DonutSwinConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
         import torch
         from torch import nn
  
         from transformers import DonutSwinModel
         from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=13     ,      lowercase__=32     ,      lowercase__=2     ,      lowercase__=3     ,      lowercase__=16     ,      lowercase__=[1, 2, 1]     ,      lowercase__=[2, 2, 4]     ,      lowercase__=2     ,      lowercase__=2.0     ,      lowercase__=True     ,      lowercase__=0.0     ,      lowercase__=0.0     ,      lowercase__=0.1     ,      lowercase__="gelu"     ,      lowercase__=False     ,      lowercase__=True     ,      lowercase__=0.02     ,      lowercase__=1E-5     ,      lowercase__=True     ,      lowercase__=None     ,      lowercase__=True     ,      lowercase__=10     ,      lowercase__=8     ,      )     ->      str:
          __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      lowerCAmelCase_						(self  )     ->      Any:
          __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      lowerCAmelCase_						(self  )     ->      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     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
          __UpperCAmelCase									=			DonutSwinModel(config=lowercase__  )
          model.to(lowercase__  )
          model.eval()
          __UpperCAmelCase									=			model(lowercase__  )
  
          __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      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     
          )									=			config_and_inputs
          __UpperCAmelCase									=			{'''pixel_values''': pixel_values}
          return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 					A_   (      _a ,     _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (DonutSwinModel,) if is_torch_available() else ()
      a__     							=       {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
      a__     							=       True
  
      a__     							=       False
      a__     							=       False
      a__     							=       False
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			DonutSwinModelTester(self  )
          __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      embed_dim=37  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, 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  )     ->      Optional[int]:
          return
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          # DonutSwin does not use inputs_embeds
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(lowercase__  )
              self.assertIsInstance(model.get_input_embeddings()     ,      (nn.Module)  )
              __UpperCAmelCase									=			model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase__     ,      nn.Linear  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(lowercase__  )
              __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]     ,      lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __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(lowercase__  )
              model.to(lowercase__  )
              model.eval()
              with torch.no_grad():
                  __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
              __UpperCAmelCase									=			outputs.attentions
              __UpperCAmelCase									=			len(self.model_tester.depths  )
              self.assertEqual(len(lowercase__  )     ,      lowercase__  )
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              __UpperCAmelCase									=			True
              __UpperCAmelCase									=			config.window_size**2
              __UpperCAmelCase									=			model_class(lowercase__  )
              model.to(lowercase__  )
              model.eval()
              with torch.no_grad():
                  __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
              __UpperCAmelCase									=			outputs.attentions
              self.assertEqual(len(lowercase__  )     ,      lowercase__  )
  
              self.assertListEqual(
                  list(attentions[0].shape[-3:]  )     ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,      )
              __UpperCAmelCase									=			len(lowercase__  )
  
              # Check attention is always last and order is fine
              __UpperCAmelCase									=			True
              __UpperCAmelCase									=			True
              __UpperCAmelCase									=			model_class(lowercase__  )
              model.to(lowercase__  )
              model.eval()
              with torch.no_grad():
                  __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
  
              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(lowercase__  )  )
  
              __UpperCAmelCase									=			outputs.attentions
  
              self.assertEqual(len(lowercase__  )     ,      lowercase__  )
  
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]  )     ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
          __UpperCAmelCase									=			model_class(lowercase__  )
          model.to(lowercase__  )
          model.eval()
  
          with torch.no_grad():
              __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
  
          __UpperCAmelCase									=			outputs.hidden_states
  
          __UpperCAmelCase									=			getattr(
              self.model_tester     ,      '''expected_num_hidden_layers'''     ,      len(self.model_tester.depths  ) + 1  )
          self.assertEqual(len(lowercase__  )     ,      lowercase__  )
  
          # 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(lowercase__  )     ,      lowercase__  )
  
          __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			reshaped_hidden_states[0].shape
          __UpperCAmelCase									=			(
              reshaped_hidden_states[0].view(lowercase__     ,      lowercase__     ,      height * width  ).permute(0     ,      2     ,      1  )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]  )     ,      [num_patches, self.model_tester.embed_dim]     ,      )
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __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(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCAmelCase									=			True
  
              self.check_hidden_states_output(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __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(lowercase__     ,      lowercase__     ,      lowercase__     ,      (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(lowercase__     ,      lowercase__     ,      lowercase__     ,      (padded_height, padded_width)  )
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCAmelCase									=			DonutSwinModel.from_pretrained(lowercase__  )
              self.assertIsNotNone(lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
  
          __UpperCAmelCase									=			_config_zero_init(lowercase__  )
          for model_class in self.all_model_classes:
              __UpperCAmelCase									=			model_class(config=lowercase__  )
              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'''     ,      )
  
  
  
  
  