




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


_lowerCamelCase							=      logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowerCamelCase							=      {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase							=      sorted(arg_to_scheduler.keys())
_lowerCamelCase							=      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class        UpperCAmelCase__				(							pl.LightningModule				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase="base" ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    **_lowerCAmelCase ,    ):
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(_lowerCAmelCase	)
            a			   =0
            a			   =Path(self.hparams.output_dir	)
            a			   =self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   a			   =AutoConfig.from_pretrained(
                       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path ,    **({"""num_labels""": num_labels} if num_labels is not None else {}) ,    cache_dir=_lowerCAmelCase ,    **_lowerCAmelCase ,    )
            else:
                   a			   =config

            a			   =("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
            for p in extra_model_params:
                   if getattr(self.hparams ,    _lowerCAmelCase ,    _lowerCAmelCase	):
                          assert hasattr(self.config ,    _lowerCAmelCase	), F'''model config doesn\'t have a `{p}` attribute'''
                          setattr(self.config ,    _lowerCAmelCase ,    getattr(self.hparams ,    _lowerCAmelCase	)	)

            if tokenizer is None:
                   a			   =AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,    cache_dir=_lowerCAmelCase ,    )
            else:
                   a			   =tokenizer
            a			   =MODEL_MODES[mode]
            if model is None:
                   a			   =self.model_type.from_pretrained(
                       self.hparams.model_name_or_path ,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path	) ,    config=self.config ,    cache_dir=_lowerCAmelCase ,    )
            else:
                   a			   =model
     def        lowerCAmelCase__  (				self ,    *_lowerCAmelCase ,    **_lowerCAmelCase	):
            a			   =self.model_type.from_pretrained(*_lowerCAmelCase ,    **_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =arg_to_scheduler[self.hparams.lr_scheduler]
            a			   =get_schedule_func(
                self.opt ,    num_warmup_steps=self.hparams.warmup_steps ,    num_training_steps=self.total_steps()	)
            a			   ={"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
            return scheduler
     def        lowerCAmelCase__  (				self	):
            a			   =self.model
            a			   =["""bias""", """LayerNorm.weight"""]
            a			   =[
                {
                    """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
                    """weight_decay""": self.hparams.weight_decay,
                },
                {
                    """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
                    """weight_decay""": 0.0,
                },
            ]
            if self.hparams.adafactor:
                   a			   =Adafactor(
                       _lowerCAmelCase ,    lr=self.hparams.learning_rate ,    scale_parameter=_lowerCAmelCase ,    relative_step=_lowerCAmelCase	)

            else:
                   a			   =AdamW(
                       _lowerCAmelCase ,    lr=self.hparams.learning_rate ,    eps=self.hparams.adam_epsilon	)
            a			   =optimizer

            a			   =self.get_lr_scheduler()

            return [optimizer], [scheduler]
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            return self.validation_step(_lowerCAmelCase ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            return self.validation_end(_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =max(1 ,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
            a			   =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            if mode == "test":
                   a			   =len(self.test_dataloader().dataset	)
            else:
                   a			   =self.get_dataloader("""train""" ,    self.hparams.train_batch_size ,    shuffle=_lowerCAmelCase	)
                   a			   =len(self.train_dataloader().dataset	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = False	):
            raise NotImplementedError("""You must implement this for your task"""	)
     def        lowerCAmelCase__  (				self	):
            return self.train_loader
     def        lowerCAmelCase__  (				self	):
            return self.get_dataloader("""dev""" ,    self.hparams.eval_batch_size ,    shuffle=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            return self.get_dataloader("""test""" ,    self.hparams.eval_batch_size ,    shuffle=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            return os.path.join(
                self.hparams.data_dir ,    """cached_{}_{}_{}""".format(
                    _lowerCAmelCase ,    list(filter(_lowerCAmelCase ,    self.hparams.model_name_or_path.split("""/"""	)	)	).pop() ,    str(self.hparams.max_seq_length	) ,    ) ,    )
     @pl.utilities.rank_zero_only
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =self.output_dir.joinpath("""best_tfmr"""	)
            a			   =self.step_count
            self.model.save_pretrained(_lowerCAmelCase	)
            self.tokenizer.save_pretrained(_lowerCAmelCase	)






     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase	):
            parser.add_argument(
                """--model_name_or_path""" ,    default=_lowerCAmelCase ,    type=_lowerCAmelCase ,    required=_lowerCAmelCase ,    help="""Path to pretrained model or model identifier from huggingface.co/models""" ,    )
            parser.add_argument(
                """--config_name""" ,    default="""""" ,    type=_lowerCAmelCase ,    help="""Pretrained config name or path if not the same as model_name"""	)
            parser.add_argument(
                """--tokenizer_name""" ,    default=_lowerCAmelCase ,    type=_lowerCAmelCase ,    help="""Pretrained tokenizer name or path if not the same as model_name""" ,    )
            parser.add_argument(
                """--cache_dir""" ,    default="""""" ,    type=_lowerCAmelCase ,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co""" ,    )
            parser.add_argument(
                """--encoder_layerdrop""" ,    type=_lowerCAmelCase ,    help="""Encoder layer dropout probability (Optional). Goes into model.config""" ,    )
            parser.add_argument(
                """--decoder_layerdrop""" ,    type=_lowerCAmelCase ,    help="""Decoder layer dropout probability (Optional). Goes into model.config""" ,    )
            parser.add_argument(
                """--dropout""" ,    type=_lowerCAmelCase ,    help="""Dropout probability (Optional). Goes into model.config""" ,    )
            parser.add_argument(
                """--attention_dropout""" ,    type=_lowerCAmelCase ,    help="""Attention dropout probability (Optional). Goes into model.config""" ,    )
            parser.add_argument("""--learning_rate""" ,    default=5E-5 ,    type=_lowerCAmelCase ,    help="""The initial learning rate for Adam."""	)
            parser.add_argument(
                """--lr_scheduler""" ,    default="""linear""" ,    choices=_lowerCAmelCase ,    metavar=_lowerCAmelCase ,    type=_lowerCAmelCase ,    help="""Learning rate scheduler""" ,    )
            parser.add_argument("""--weight_decay""" ,    default=0.0 ,    type=_lowerCAmelCase ,    help="""Weight decay if we apply some."""	)
            parser.add_argument("""--adam_epsilon""" ,    default=1E-8 ,    type=_lowerCAmelCase ,    help="""Epsilon for Adam optimizer."""	)
            parser.add_argument("""--warmup_steps""" ,    default=0 ,    type=_lowerCAmelCase ,    help="""Linear warmup over warmup_steps."""	)
            parser.add_argument("""--num_workers""" ,    default=4 ,    type=_lowerCAmelCase ,    help="""kwarg passed to DataLoader"""	)
            parser.add_argument("""--num_train_epochs""" ,    dest="""max_epochs""" ,    default=3 ,    type=_lowerCAmelCase	)
            parser.add_argument("""--train_batch_size""" ,    default=32 ,    type=_lowerCAmelCase	)
            parser.add_argument("""--eval_batch_size""" ,    default=32 ,    type=_lowerCAmelCase	)
            parser.add_argument("""--adafactor""" ,    action="""store_true"""	)



class        UpperCAmelCase__				(							pl.Callback				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =trainer.lr_schedulers[0]["""scheduler"""]
            a			   ={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
            pl_module.logger.log_metrics(_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            rank_zero_info("""***** Validation results *****"""	)
            a			   =trainer.callback_metrics
            # Log results
            for key in sorted(_lowerCAmelCase	):
                   if key not in ["log", "progress_bar"]:
                          rank_zero_info("""{} = {}\n""".format(_lowerCAmelCase ,    str(metrics[key]	)	)	)






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            rank_zero_info("""***** Test results *****"""	)
            a			   =trainer.callback_metrics
            # Log and save results to file
            a			   =os.path.join(pl_module.hparams.output_dir ,    """test_results.txt"""	)
            with open(_lowerCAmelCase ,    """w"""	) as writer:
                   for key in sorted(_lowerCAmelCase	):
                          if key not in ["log", "progress_bar"]:
                                 rank_zero_info("""{} = {}\n""".format(_lowerCAmelCase ,    str(metrics[key]	)	)	)
                                 writer.write("""{} = {}\n""".format(_lowerCAmelCase ,    str(metrics[key]	)	)	)





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      Any      )->			None:

       """simple docstring"""




       parser.add_argument(
           """--output_dir"""   ,       default=UpperCAmelCase_   ,       type=UpperCAmelCase_   ,       required=UpperCAmelCase_   ,       help="""The output directory where the model predictions and checkpoints will be written."""   ,       )
       parser.add_argument(
           """--fp16"""   ,       action="""store_true"""   ,       help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""   ,       )

       parser.add_argument(
           """--fp16_opt_level"""   ,       type=UpperCAmelCase_   ,       default="""O2"""   ,       help=(
               """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
               """See details at https://nvidia.github.io/apex/amp.html"""
           )   ,       )
       parser.add_argument("""--n_tpu_cores"""   ,       dest="""tpu_cores"""   ,       type=UpperCAmelCase_      )
       parser.add_argument("""--max_grad_norm"""   ,       dest="""gradient_clip_val"""   ,       default=1.0   ,       type=UpperCAmelCase_   ,       help="""Max gradient norm"""      )
       parser.add_argument("""--do_train"""   ,       action="""store_true"""   ,       help="""Whether to run training."""      )
       parser.add_argument("""--do_predict"""   ,       action="""store_true"""   ,       help="""Whether to run predictions on the test set."""      )
       parser.add_argument(
           """--gradient_accumulation_steps"""   ,       dest="""accumulate_grad_batches"""   ,       type=UpperCAmelCase_   ,       default=1   ,       help="""Number of updates steps to accumulate before performing a backward/update pass."""   ,       )
       parser.add_argument("""--seed"""   ,       type=UpperCAmelCase_   ,       default=42   ,       help="""random seed for initialization"""      )
       parser.add_argument(
           """--data_dir"""   ,       default=UpperCAmelCase_   ,       type=UpperCAmelCase_   ,       required=UpperCAmelCase_   ,       help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""   ,       )






def 							lowerCamelCase    (							UpperCAmelCase_	:      BaseTransformer   ,       UpperCAmelCase_	:      argparse.Namespace   ,       UpperCAmelCase_	:      Optional[int]=None   ,       UpperCAmelCase_	:      Optional[int]=True   ,       UpperCAmelCase_	:      Tuple=[]   ,       UpperCAmelCase_	:      str=None   ,       UpperCAmelCase_	:      List[str]=None   ,       **UpperCAmelCase_	:      Tuple   ,       )->			Dict:

       """simple docstring"""




       pl.seed_everything(args.seed      )

       # init model
       a			   =Path(model.hparams.output_dir      )
       odir.mkdir(exist_ok=UpperCAmelCase_      )

       # add custom checkpoints
       if checkpoint_callback is None:
              a			   =pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir   ,       prefix="""checkpoint"""   ,       monitor="""val_loss"""   ,       mode="""min"""   ,       save_top_k=1      )
       if early_stopping_callback:
              extra_callbacks.append(UpperCAmelCase_      )
       if logging_callback is None:
              a			   =LoggingCallback()

       a			   ={}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              a			   =16
              a			   =args.fpaa_opt_level

       if args.gpus > 1:
              a			   ="""ddp"""

       a			   =args.accumulate_grad_batches
       a			   =extra_train_kwargs.get("""accelerator"""   ,       UpperCAmelCase_      )
       a			   =extra_train_kwargs.get("""profiler"""   ,       UpperCAmelCase_      )

       a			   =pl.Trainer.from_argparse_args(
           UpperCAmelCase_   ,       weights_summary=UpperCAmelCase_   ,       callbacks=[logging_callback] + extra_callbacks   ,       logger=UpperCAmelCase_   ,       checkpoint_callback=UpperCAmelCase_   ,       **UpperCAmelCase_   ,       )

       if args.do_train:
              trainer.fit(UpperCAmelCase_      )

       return trainer






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
    from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =self.config_class(**self.inputs_dict	)
            self.parent.assertTrue(hasattr(_lowerCAmelCase ,    """hidden_sizes"""	)	)
            self.parent.assertTrue(hasattr(_lowerCAmelCase ,    """num_encoder_blocks"""	)	)



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=13 ,    _lowerCAmelCase=64 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=4 ,    _lowerCAmelCase=[2, 2, 2, 2] ,    _lowerCAmelCase=[8, 4, 2, 1] ,    _lowerCAmelCase=[16, 32, 64, 128] ,    _lowerCAmelCase=[1, 4, 8, 16] ,    _lowerCAmelCase=False ,    _lowerCAmelCase=True ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=None ,    ):
            a			   =parent
            a			   =batch_size
            a			   =image_size
            a			   =num_channels
            a			   =num_encoder_blocks
            a			   =sr_ratios
            a			   =depths
            a			   =hidden_sizes
            a			   =downsampling_rates
            a			   =is_training
            a			   =use_labels
            a			   =hidden_act
            a			   =hidden_dropout_prob
            a			   =initializer_range
            a			   =num_labels
            a			   =scope
     def        lowerCAmelCase__  (				self	):
            a			   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

            a			   =None
            if self.use_labels:
                   a			   =ids_tensor([self.batch_size, self.image_size, self.image_size] ,    self.num_labels	)

            a			   =PoolFormerConfig(
                image_size=self.image_size ,    num_channels=self.num_channels ,    num_encoder_blocks=self.num_encoder_blocks ,    depths=self.depths ,    hidden_sizes=self.hidden_sizes ,    hidden_act=self.hidden_act ,    hidden_dropout_prob=self.hidden_dropout_prob ,    initializer_range=self.initializer_range ,    )

            return config, pixel_values, labels
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =PoolFormerModel(config=_lowerCAmelCase	)
            model.to(_lowerCAmelCase	)
            model.eval()
            a			   =model(_lowerCAmelCase	)
            a			   =a			   =self.image_size // 32.0
            self.parent.assertEqual(
                result.last_hidden_state.shape ,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)






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



@require_torch
class        UpperCAmelCase__				(							UpperCAmelCase__      ,		UpperCAmelCase__      ,		unittest.TestCase				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE      :					Any     					=    (
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    False
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    False
     _SCREAMING_SNAKE_CASE      :					str     					=    False
     _SCREAMING_SNAKE_CASE      :					Optional[int]     					=    False
     _SCREAMING_SNAKE_CASE      :					Optional[int]     					=    False
     def        lowerCAmelCase__  (				self	):
            a			   =PoolFormerModelTester(self	)
            a			   =PoolFormerConfigTester(self ,    config_class=_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            self.config_tester.run_common_tests()
     def        lowerCAmelCase__  (				self	):
            a			   =self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_lowerCAmelCase	)
     @unittest.skip("""PoolFormer does not use inputs_embeds"""	)
     def        lowerCAmelCase__  (				self	):
            pass
     @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""	)
     def        lowerCAmelCase__  (				self	):
            pass
     def        lowerCAmelCase__  (				self	):
            def check_hidden_states_output(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
                   a			   =model_class(_lowerCAmelCase	)
                   model.to(_lowerCAmelCase	)
                   model.eval()

                   with torch.no_grad():
                          a			   =model(**self._prepare_for_class(_lowerCAmelCase ,    _lowerCAmelCase	)	)

                   a			   =outputs.hidden_states

                   a			   =self.model_tester.num_encoder_blocks
                   self.assertEqual(len(_lowerCAmelCase	) ,    _lowerCAmelCase	)

                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-3:]	) ,    [
                           self.model_tester.hidden_sizes[0],
                           self.model_tester.image_size // 4,
                           self.model_tester.image_size // 4,
                       ] ,    )

            a   ,      a			   =self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   a			   =True
                   check_hidden_states_output(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)

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

                   check_hidden_states_output(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            if not self.model_tester.is_training:
                   return

            a   ,      a			   =self.model_tester.prepare_config_and_inputs_for_common()
            a			   =True

            for model_class in self.all_model_classes:
                   if model_class in get_values(_lowerCAmelCase	):
                          continue
                   a			   =model_class(_lowerCAmelCase	)
                   model.to(_lowerCAmelCase	)
                   model.train()
                   a			   =self._prepare_for_class(_lowerCAmelCase ,    _lowerCAmelCase ,    return_labels=_lowerCAmelCase	)
                   a			   =model(**_lowerCAmelCase	).loss
                   loss.backward()
     def        lowerCAmelCase__  (				self	):
            a   ,      a			   =self.model_tester.prepare_config_and_inputs_for_common()

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

                   a			   =["""pixel_values"""]
                   self.assertListEqual(arg_names[:1] ,    _lowerCAmelCase	)






     @slow
     def        lowerCAmelCase__  (				self	):
            for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   a			   =PoolFormerModel.from_pretrained(_lowerCAmelCase	)
                   self.assertIsNotNone(_lowerCAmelCase	)





def 							lowerCamelCase    (							)->			Tuple:

       """simple docstring"""




       a			   =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
       return image






@require_torch
class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     @slow
     def        lowerCAmelCase__  (				self	):
            a			   =PoolFormerImageProcessor()
            a			   =PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""	).to(_lowerCAmelCase	)

            a			   =image_processor(images=prepare_img() ,    return_tensors="""pt"""	).to(_lowerCAmelCase	)

            # forward pass
            with torch.no_grad():
                   a			   =model(**_lowerCAmelCase	)

            # verify the logits
            a			   =torch.Size((1, 1_000)	)
            self.assertEqual(outputs.logits.shape ,    _lowerCAmelCase	)

            a			   =torch.tensor([-0.61_13, 0.16_85, -0.04_92]	).to(_lowerCAmelCase	)
            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,    _lowerCAmelCase ,    atol=1E-4	)	)
