

'''simple docstring'''




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
      from fairscale.optim import OSS


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}
class   _SCREAMING_SNAKE_CASE   (   UpperCAmelCase_     ):



 def __init__(   self		:   List[Any]			,				a__		:   Tuple=None			,				a__		:   Union[str, Any]=None			,				*a__		:   Optional[int]			,				**a__		:   int      ):
      super().__init__(*__lowercase			,				**__lowercase      )

      if config is None:
           assert isinstance(self.model			,				__lowercase      ), (
               "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
               F''' {self.model.__class__}'''
           )
           __magic_name__						       =    self.model.config
      else:
           __magic_name__						       =    config

      __magic_name__						       =    data_args
      __magic_name__						       =    self.config.tgt_vocab_size if isinstance(self.config			,				__lowercase      ) else self.config.vocab_size

      if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
           assert self.config.pad_token_id is not None, (
               "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
               " calculation or doing label smoothing."
           )

      if self.config.pad_token_id is None and self.config.eos_token_id is not None:
           logger.warning(
               F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
               ''' padding..'''      )

      if self.args.label_smoothing == 0:
           __magic_name__						       =    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
      else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           __magic_name__						       =    label_smoothed_nll_loss



 def 			snake_case__  (   self		:   Any			,				a__		:   Dict      ):
      if self.optimizer is None:
           __magic_name__						       =    ['''bias''', '''LayerNorm.weight''']
           __magic_name__						       =    [
               {
                   '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay      )],
                   '''weight_decay''': self.args.weight_decay,
               },
               {
                   '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay      )],
                   '''weight_decay''': 0.0,
               },
           ]
           __magic_name__						       =    Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
                __magic_name__						       =    Adafactor
                __magic_name__						       =    {'''scale_parameter''': False, '''relative_step''': False}
           else:
                __magic_name__						       =    AdamW
                __magic_name__						       =    {
                    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                    '''eps''': self.args.adam_epsilon,
                }
           __magic_name__						       =    self.args.learning_rate
           if self.sharded_ddp:
                __magic_name__						       =    OSS(
                    params=__lowercase			,				optim=__lowercase			,				**__lowercase			,				)
           else:
                __magic_name__						       =    optimizer_cls(__lowercase			,				**__lowercase      )

      if self.lr_scheduler is None:
           __magic_name__						       =    self._get_lr_scheduler(__lowercase      )
      else:  # ignoring --lr_scheduler
           logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''      )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   int      ):
      __magic_name__						       =    arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
           __magic_name__						       =    schedule_func(self.optimizer      )
      elif self.args.lr_scheduler == "constant_w_warmup":
           __magic_name__						       =    schedule_func(self.optimizer			,				num_warmup_steps=self.args.warmup_steps      )
      else:
           __magic_name__						       =    schedule_func(
               self.optimizer			,				num_warmup_steps=self.args.warmup_steps			,				num_training_steps=__lowercase      )
      return scheduler



 def 			snake_case__  (   self		:   Optional[Any]      ):
      if isinstance(self.train_dataset			,				torch.utils.data.IterableDataset      ):
           return None
      elif is_torch_tpu_available():
           return get_tpu_sampler(self.train_dataset      )
      else:
           if self.args.sortish_sampler:
                self.train_dataset.make_sortish_sampler(
                    self.args.per_device_train_batch_size			,				distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)			,				)

           return (
               RandomSampler(self.train_dataset      )
               if self.args.local_rank == -1
               else DistributedSampler(self.train_dataset      )
           )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   int			,				a__		:   Union[str, Any]			,				a__		:   int      ):
      if self.args.label_smoothing == 0:
           if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                # force training to ignore pad token
                __magic_name__						       =    model(**__lowercase			,				use_cache=__lowercase      )[0]
                __magic_name__						       =    self.loss_fn(logits.view(-1			,				logits.shape[-1]      )			,				labels.view(-1      )      )
           else:
                # compute usual loss via models
                __magic_name__						       =    model(**__lowercase			,				labels=__lowercase			,				use_cache=__lowercase      )[:2]
      else:
           # compute label smoothed loss
           __magic_name__						       =    model(**__lowercase			,				use_cache=__lowercase      )[0]
           __magic_name__						       =    torch.nn.functional.log_softmax(__lowercase			,				dim=-1      )
           __magic_name__						       =    self.loss_fn(__lowercase			,				__lowercase			,				self.args.label_smoothing			,				ignore_index=self.config.pad_token_id      )
      return loss, logits



 def 			snake_case__  (   self		:   int			,				a__		:   int			,				a__		:   Dict      ):
      __magic_name__						       =    inputs.pop('''labels'''      )
      __magic_name__						       =    self._compute_loss(__lowercase			,				__lowercase			,				__lowercase      )
      return loss



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   Dict			,				a__		:   int			,				a__		:   Dict			,				a__		:   List[str] = None			,				):
      __magic_name__						       =    self._prepare_inputs(__lowercase      )

      __magic_name__						       =    {
          '''max_length''': self.data_args.val_max_target_length
          if self.data_args is not None
          else self.config.max_length,
          '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
      }

      if self.args.predict_with_generate and not self.args.prediction_loss_only:
           __magic_name__						       =    self.model.generate(
               inputs['''input_ids''']			,				attention_mask=inputs['''attention_mask''']			,				**__lowercase			,				)
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                __magic_name__						       =    self._pad_tensors_to_max_len(__lowercase			,				gen_kwargs['''max_length''']      )

      __magic_name__						       =    inputs.pop('''labels'''      )
      with torch.no_grad():
           # compute loss on predict data
           __magic_name__						       =    self._compute_loss(__lowercase			,				__lowercase			,				__lowercase      )

      __magic_name__						       =    loss.mean().detach()
      if self.args.prediction_loss_only:
           return (loss, None, None)

      __magic_name__						       =    generated_tokens if self.args.predict_with_generate else logits

      if labels.shape[-1] < gen_kwargs["max_length"]:
           __magic_name__						       =    self._pad_tensors_to_max_len(__lowercase			,				gen_kwargs['''max_length''']      )

      return (loss, logits, labels)



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Optional[int]			,				a__		:   List[Any]      ):
      # If PAD token is not defined at least EOS token has to be defined
      __magic_name__						       =    self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

      if pad_token_id is None:
           raise ValueError(
               '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
               F''' padded to `max_length`={max_length}'''      )

      __magic_name__						       =    pad_token_id * torch.ones(
          (tensor.shape[0], max_length)			,				dtype=tensor.dtype			,				device=tensor.device      )
      __magic_name__						       =    tensor
      return padded_tensor









'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
      from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class   _SCREAMING_SNAKE_CASE   :



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



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

      __magic_name__						       =    self.get_config()

      return config, pixel_values, labels



 def 			snake_case__  (   self		:   Optional[int]      ):
      return SwinvaConfig(
          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 			snake_case__  (   self		:   Optional[int]			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]			,				a__		:   Optional[int]      ):
      __magic_name__						       =    SwinvaModel(config=a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    model(a__      )

      __magic_name__						       =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Optional[Any]			,				a__		:   str			,				a__		:   int      ):
      __magic_name__						       =    SwinvaForMaskedImageModeling(config=a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(
          result.logits.shape			,				(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

      # test greyscale images
      __magic_name__						       =    1
      __magic_name__						       =    SwinvaForMaskedImageModeling(a__      )
      model.to(a__      )
      model.eval()

      __magic_name__						       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, 1, self.image_size, self.image_size)      )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[str]			,				a__		:   List[Any]			,				a__		:   Any      ):
      __magic_name__						       =    self.type_sequence_label_size
      __magic_name__						       =    SwinvaForImageClassification(a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    model(a__			,				labels=a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size)      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      __magic_name__       ,  __magic_name__       ,  __magic_name__						       =    config_and_inputs
      __magic_name__						       =    {'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:int      	=					(
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 __SCREAMING_SNAKE_CASE					:Tuple      	=					(
     {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					False
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					False
 __SCREAMING_SNAKE_CASE					:Dict      	=					False
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					False



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    SwinvaModelTester(self      )
      __magic_name__						       =    ConfigTester(self			,				config_class=a__			,				embed_dim=37      )



 def 			snake_case__  (   self		:   Tuple      ):
      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 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a__      )



 @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''      )
 def 			snake_case__  (   self		:   str      ):
      pass



 @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''      )
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      pass



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

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



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

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

           __magic_name__						       =    ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,				a__      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						       =    True

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

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

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

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

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

           __magic_name__						       =    outputs.attentions

           self.assertEqual(len(a__      )			,				a__      )

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



 def 			snake_case__  (   self		:   Any			,				a__		:   Dict			,				a__		:   str			,				a__		:   str			,				a__		:   List[Any]      ):
      __magic_name__						       =    model_class(a__      )
      model.to(a__      )
      model.eval()

      with torch.no_grad():
           __magic_name__						       =    model(**self._prepare_for_class(a__			,				a__      )      )

      __magic_name__						       =    outputs.hidden_states

      __magic_name__						       =    getattr(
          self.model_tester			,				'''expected_num_hidden_layers'''			,				len(self.model_tester.depths      ) + 1      )
      self.assertEqual(len(a__      )			,				a__      )

      # Swinv2 has a different seq_length
      __magic_name__						       =    (
          config.patch_size
          if isinstance(config.patch_size			,				collections.abc.Iterable      )
          else (config.patch_size, config.patch_size)
      )

      __magic_name__						       =    (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]			,				)

      __magic_name__						       =    outputs.reshaped_hidden_states
      self.assertEqual(len(a__      )			,				a__      )

      __magic_name__       ,  __magic_name__       ,  __magic_name__       ,  __magic_name__						       =    reshaped_hidden_states[0].shape
      __magic_name__						       =    (
          reshaped_hidden_states[0].view(a__			,				a__			,				height * width      ).permute(0			,				2			,				1      )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]      )			,				[num_patches, self.model_tester.embed_dim]			,				)



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

      __magic_name__						       =    (
          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:
           __magic_name__						       =    True
           self.check_hidden_states_output(a__			,				a__			,				a__			,				a__      )

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

           self.check_hidden_states_output(a__			,				a__			,				a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						       =    3

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

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

      for model_class in self.all_model_classes:
           __magic_name__						       =    True
           self.check_hidden_states_output(a__			,				a__			,				a__			,				(padded_height, padded_width)      )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __magic_name__						       =    True
           self.check_hidden_states_output(a__			,				a__			,				a__			,				(padded_height, padded_width)      )



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*a__      )



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*a__      )



 @slow
 def 			snake_case__  (   self		:   Any      ):
      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    SwinvaModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()

      __magic_name__						       =    _config_zero_init(a__      )
      for model_class in self.all_model_classes:
           __magic_name__						       =    model_class(config=a__      )
           for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" 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'''			,				)






@require_vision
@require_torch
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @cached_property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return (
          AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      )
          if is_vision_available()
          else None
      )



 @slow
 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      ).to(
          a__      )
      __magic_name__						       =    self.default_image_processor

      __magic_name__						       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
      __magic_name__						       =    image_processor(images=a__			,				return_tensors='''pt'''      ).to(a__      )

      # forward pass
      with torch.no_grad():
           __magic_name__						       =    model(**a__      )

      # verify the logits
      __magic_name__						       =    torch.Size((1, 1000)      )
      self.assertEqual(outputs.logits.shape			,				a__      )
      __magic_name__						       =    torch.tensor([-0.3_947, -0.4_306, 0.0_026]      ).to(a__      )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				a__			,				atol=1E-4      )      )






