





'''simple docstring'''






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A_		:  Optional[int]		 =						logging.getLogger(__name__)
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''token-classification'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE       ):
    if type(__SCREAMING_SNAKE_CASE       ) == dict:
     snake_case__     :					Optional[Any]    =  Namespace(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  import_module("""tasks"""       )
    try:
     snake_case__     :					Optional[int]    =  getattr(__SCREAMING_SNAKE_CASE	,		hparams.task_type       )
     snake_case__     :					TokenClassificationTask    =  token_classification_task_clazz()
    except AttributeError:
     raise ValueError(
         f"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
         f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"       )
    snake_case__     :					Optional[int]    =  self.token_classification_task.get_labels(hparams.labels       )
    snake_case__     :					Optional[int]    =  CrossEntropyLoss().ignore_index
    super().__init__(__SCREAMING_SNAKE_CASE	,		len(self.labels       )	,		self.mode       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return self.model(**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					int    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     snake_case__     :					Any    =  (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids

    snake_case__     :					Any    =  self(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  outputs[0]
    # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
    return {"loss": loss}

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  self.hparams
    for mode in ["train", "dev", "test"]:
     snake_case__     :					str    =  self._feature_file(__SCREAMING_SNAKE_CASE       )
     if os.path.exists(__SCREAMING_SNAKE_CASE       ) and not args.overwrite_cache:
      logger.info("""Loading features from cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Tuple    =  torch.load(__SCREAMING_SNAKE_CASE       )
     else:
      logger.info("""Creating features from dataset file at %s"""	,		args.data_dir       )
      snake_case__     :					Optional[int]    =  self.token_classification_task.read_examples_from_file(args.data_dir	,		__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Optional[int]    =  self.token_classification_task.convert_examples_to_features(
          __SCREAMING_SNAKE_CASE	,		self.labels	,		args.max_seq_length	,		self.tokenizer	,		cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]       )	,		cls_token=self.tokenizer.cls_token	,		cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0	,		sep_token=self.tokenizer.sep_token	,		sep_token_extra=__SCREAMING_SNAKE_CASE	,		pad_on_left=bool(self.config.model_type in ["""xlnet"""]       )	,		pad_token=self.tokenizer.pad_token_id	,		pad_token_segment_id=self.tokenizer.pad_token_type_id	,		pad_token_label_id=self.pad_token_label_id	,		)
      logger.info("""Saving features into cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
      torch.save(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = False       ):
    snake_case__     :					Optional[int]    =  self._feature_file(__SCREAMING_SNAKE_CASE       )
    logger.info("""Loading features from cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  torch.tensor([f.input_ids for f in features]	,		dtype=torch.long       )
    snake_case__     :					int    =  torch.tensor([f.attention_mask for f in features]	,		dtype=torch.long       )
    if features[0].token_type_ids is not None:
     snake_case__     :					List[str]    =  torch.tensor([f.token_type_ids for f in features]	,		dtype=torch.long       )
    else:
     snake_case__     :					Optional[Any]    =  torch.tensor([0 for f in features]	,		dtype=torch.long       )
     # HACK(we will not use this anymore soon)
    snake_case__     :					Optional[Any]    =  torch.tensor([f.label_ids for f in features]	,		dtype=torch.long       )
    return DataLoader(
        TensorDataset(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )	,		batch_size=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    """Compute validation""" ""
    snake_case__     :					Optional[Any]    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     snake_case__     :					Tuple    =  (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids
    snake_case__     :					str    =  self(**__SCREAMING_SNAKE_CASE       )
    snake_case__  ,							snake_case__     :					Optional[Any]    =  outputs[:2]
    snake_case__     :					Union[str, Any]    =  logits.detach().cpu().numpy()
    snake_case__     :					Optional[int]    =  inputs["""labels"""].detach().cpu().numpy()
    return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Any    =  torch.stack([x["""val_loss"""] for x in outputs]       ).mean()
    snake_case__     :					Tuple    =  np.concatenate([x["""pred"""] for x in outputs]	,		axis=0       )
    snake_case__     :					List[Any]    =  np.argmax(__SCREAMING_SNAKE_CASE	,		axis=2       )
    snake_case__     :					Optional[int]    =  np.concatenate([x["""target"""] for x in outputs]	,		axis=0       )

    snake_case__     :					List[str]    =  dict(enumerate(self.labels       )       )
    snake_case__     :					Any    =  [[] for _ in range(out_label_ids.shape[0]       )]
    snake_case__     :					Optional[int]    =  [[] for _ in range(out_label_ids.shape[0]       )]

    for i in range(out_label_ids.shape[0]       ):
     for j in range(out_label_ids.shape[1]       ):
      if out_label_ids[i, j] != self.pad_token_label_id:
       out_label_list[i].append(label_map[out_label_ids[i][j]]       )
       preds_list[i].append(label_map[preds[i][j]]       )

    snake_case__     :					int    =  {
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """precision""": precision_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """recall""": recall_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """f1""": fa_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
    }

    snake_case__     :					Any    =  dict(results.items()       )
    snake_case__     :					Dict    =  results
    return ret, preds_list, out_label_list

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # when stable
    snake_case__  ,							snake_case__  ,							snake_case__     :					Any    =  self._eval_end(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # updating to test_epoch_end instead of deprecated test_end
    snake_case__  ,							snake_case__  ,							snake_case__     :					Union[str, Any]    =  self._eval_end(__SCREAMING_SNAKE_CASE       )

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    snake_case__     :					Dict    =  ret["""log"""]
    # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
    return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


   @staticmethod
   def 		__UpperCamelCase						(							__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    # Add NER specific options
    BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
    parser.add_argument(
        """--task_type"""	,		default="""NER"""	,		type=__SCREAMING_SNAKE_CASE	,		help="""Task type to fine tune in training (e.g. NER, POS, etc)"""       )
    parser.add_argument(
        """--max_seq_length"""	,		default=1_2_8	,		type=__SCREAMING_SNAKE_CASE	,		help=(
            """The maximum total input sequence length after tokenization. Sequences longer """
            """than this will be truncated, sequences shorter will be padded."""
        )	,		)

    parser.add_argument(
        """--labels"""	,		default=""""""	,		type=__SCREAMING_SNAKE_CASE	,		help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""	,		)
    parser.add_argument(
        """--gpus"""	,		default=0	,		type=__SCREAMING_SNAKE_CASE	,		help="""The number of GPUs allocated for this, it is by default 0 meaning none"""	,		)

    parser.add_argument(
        """--overwrite_cache"""	,		action="""store_true"""	,		help="""Overwrite the cached training and evaluation sets"""       )

    return parser


if __name__ == "__main__":
    A_		:  Dict		 =						argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    A_		:  int		 =						NERTransformer.add_model_specific_args(parser, os.getcwd())
    A_		:  List[Any]		 =						parser.parse_args()
    A_		:  Union[str, Any]		 =						NERTransformer(args)
    A_		:  str		 =						generic_train(model, args)

    if args.do_predict:
        # See https://github.com/huggingface/transformers/issues/3159
        # pl use this default format to create a checkpoint:
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
        # /pytorch_lightning/callbacks/model_checkpoint.py#L322
        A_		:  Union[str, Any]		 =						sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
        A_		:  List[str]		 =						model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)








import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class 	__lowercase					:





      def __init__(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       int=1_3       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       List[str]=True       ,							__lowerCamelCase    :       Any=True       ,							__lowerCamelCase    :       Optional[int]=0.1       ,							__lowerCamelCase    :       Optional[Any]=0.1       ,							__lowerCamelCase    :       Tuple=2_2_4       ,							__lowerCamelCase    :       int=1_0_0_0       ,							__lowerCamelCase    :       int=[3, 3, 6, 4]       ,							__lowerCamelCase    :       Dict=[4_8, 5_6, 1_1_2, 2_2_0]       ,							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_dropout_prob
         UpperCAmelCase      					=  attention_probs_dropout_prob
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  layer_depths
         UpperCAmelCase      					=  embed_dims





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[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.num_labels							)

         UpperCAmelCase      					=  self.get_config()

         return config, pixel_values, labels





      def 							_lowercase					(		self    :       Optional[Any]							)     -> str:






         """simple docstring"""







         return SwiftFormerConfig(
             depths=self.layer_depths       ,							embed_dims=self.embed_dims       ,							mlp_ratio=4       ,							downsamples=[True, True, True, True]       ,							hidden_act="""gelu"""       ,							num_labels=self.num_labels       ,							down_patch_size=3       ,							down_stride=2       ,							down_pad=1       ,							drop_rate=0.0       ,							drop_path_rate=0.0       ,							use_layer_scale=__lowerCamelCase       ,							layer_scale_init_value=1e-5       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Optional[int]							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerModel(config=__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.embed_dims[-1], 7, 7)							)





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Any							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  SwiftFormerForImageClassification(__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)

         UpperCAmelCase      					=  SwiftFormerForImageClassification(__lowerCamelCase							)
         model.to(__lowerCamelCase							)
         model.eval()

         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







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






@require_torch
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(
             self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase       ,							hidden_size=3_7       ,							num_attention_heads=1_2       ,							num_hidden_layers=1_2       ,							)





      def 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







         self.config_tester.run_common_tests()





      @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       str							)     -> 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(__lowerCamelCase							)
            UpperCAmelCase      					=  model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowerCamelCase       ,							nn.Linear							)							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

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





      def 							_lowercase					(		self    :       List[str]							)     -> Dict:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Optional[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





      @slow
      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  SwiftFormerModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)





      @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
      def 							_lowercase					(		self    :       List[str]							)     -> Union[str, Any]:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Dict							)     -> List[Any]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Optional[Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()

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

            UpperCAmelCase      					=  outputs.hidden_states

            UpperCAmelCase      					=  8
            self.assertEqual(len(__lowerCamelCase							)       ,							__lowerCamelCase							)  # TODO

            # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
            # with the width and height being successively divided by 2, after every 2 blocks
            for i in range(len(__lowerCamelCase							)							):
               self.assertEqual(
                   hidden_states[i].shape       ,							torch.Size(
                       [
                           self.model_tester.batch_size,
                           self.model_tester.embed_dims[i // 2],
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       ]							)       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  True
            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

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

            check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







         def _config_zero_init(__lowerCamelCase    :       Any							):
            UpperCAmelCase      					=  copy.deepcopy(__lowerCamelCase							)
            for key in configs_no_init.__dict__.keys():
               if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                  setattr(__lowerCamelCase       ,							__lowerCamelCase       ,							1e-1_0							)
               if isinstance(getattr(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)       ,							__lowerCamelCase							):
                  UpperCAmelCase      					=  _config_zero_init(getattr(__lowerCamelCase       ,							__lowerCamelCase							)							)
                  setattr(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)
            return configs_no_init

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase      					=  _config_zero_init(__lowerCamelCase							)
         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(config=__lowerCamelCase							)
            for name, param in model.named_parameters():
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9) / 1e9).round().item()       ,							[0.0, 1.0]       ,							msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,							)





      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         pass

def 	_UpperCamelCase					(    )    ->Dict:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





      @cached_property
      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None





      @slow
      def 							_lowercase					(		self    :       Optional[int]							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(__lowerCamelCase							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""pt"""							).to(__lowerCamelCase							)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  torch.Size((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(__lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,							__lowerCamelCase       ,							atol=1e-4							)							)



