

'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase				   =  16
UpperCAmelCase				   =  32

def 				__UpperCamelCase							(	lowercase__						:  Accelerator,					lowercase__						:  int = 16 ):






     '''simple docstring'''





     __lowercase					=AutoTokenizer.from_pretrained('bert-base-cased' )
     __lowercase					=load_dataset('glue',					'mrpc' )

     def tokenize_function(lowercase__						:  List[str] ):
          # max_length=None => use the model max length (it's actually the default)
          __lowercase					=tokenizer(examples['sentence1'],					examples['sentence2'],					truncation=lowercase__,					max_length=lowercase__ )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          __lowercase					=datasets.map(
              lowercase__,					batched=lowercase__,					remove_columns=['idx', 'sentence1', 'sentence2'],					)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __lowercase					=tokenized_datasets.rename_column('label',					'labels' )

     def collate_fn(lowercase__						:  Any ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowercase					=1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __lowercase					=16
          elif accelerator.mixed_precision != "no":
               __lowercase					=8
          else:
               __lowercase					=None

          return tokenizer.pad(
              lowercase__,					padding='longest',					max_length=lowercase__,					pad_to_multiple_of=lowercase__,					return_tensors='pt',					)

     # Instantiate dataloaders.
     __lowercase					=DataLoader(
         tokenized_datasets['train'],					shuffle=lowercase__,					collate_fn=lowercase__,					batch_size=lowercase__ )
     __lowercase					=DataLoader(
         tokenized_datasets['validation'],					shuffle=lowercase__,					collate_fn=lowercase__,					batch_size=lowercase__ )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       UpperCAmelCase				   =  mocked_dataloaders  # noqa: F811

def 				__UpperCamelCase							(	lowercase__						:  List[Any],					lowercase__						:  Optional[Any] ):






     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS',					lowercase__ ) == "1":
          __lowercase					=2
     # Initialize accelerator
     __lowercase					=Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowercase					=config['lr']
     __lowercase					=int(config['num_epochs'] )
     __lowercase					=int(config['seed'] )
     __lowercase					=int(config['batch_size'] )

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps,					'isdigit' ):
          if args.checkpointing_steps == "epoch":
               __lowercase					=args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __lowercase					=int(args.checkpointing_steps )
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
     else:
          __lowercase					=None

     set_seed(lowercase__ )

     __lowercase		,     __lowercase					=get_dataloaders(lowercase__,					lowercase__ )
     __lowercase					=evaluate.load('glue',					'mrpc' )

     # If the batch size is too big we use gradient accumulation
     __lowercase					=1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __lowercase					=batch_size // MAX_GPU_BATCH_SIZE
          __lowercase					=MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowercase					=AutoModelForSequenceClassification.from_pretrained('bert-base-cased',					return_dict=lowercase__ )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     __lowercase					=model.to(accelerator.device )

     # Instantiate optimizer
     __lowercase					=AdamW(params=model.parameters(),					lr=lowercase__ )

     # Instantiate scheduler
     __lowercase					=get_linear_schedule_with_warmup(
         optimizer=lowercase__,					num_warmup_steps=1_00,					num_training_steps=(len(lowercase__ ) * num_epochs) // gradient_accumulation_steps,					)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     __lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     __lowercase					=accelerator.prepare(
         lowercase__,					lowercase__,					lowercase__,					lowercase__,					lowercase__ )

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     __lowercase					=0
     # We also need to keep track of the stating epoch so files are named properly
     __lowercase					=0

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
               accelerator.load_state(args.resume_from_checkpoint )
               __lowercase					=os.path.basename(args.resume_from_checkpoint )
          else:
               # Get the most recent checkpoint
               __lowercase					=[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
               dirs.sort(key=os.path.getctime )
               __lowercase					=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __lowercase					=os.path.splitext(lowercase__ )[0]

          if "epoch" in training_difference:
               __lowercase					=int(training_difference.replace('epoch_',					'' ) ) + 1
               __lowercase					=None
          else:
               __lowercase					=int(training_difference.replace('step_',					'' ) )
               __lowercase					=resume_step // len(lowercase__ )
               resume_step -= starting_epoch * len(lowercase__ )

    # Now we train the model
     for epoch in range(lowercase__,					lowercase__ ):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               __lowercase					=accelerator.skip_first_batches(lowercase__,					lowercase__ )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __lowercase					=train_dataloader
          for step, batch in enumerate(lowercase__ ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               __lowercase					=model(**lowercase__ )
               __lowercase					=outputs.loss
               __lowercase					=loss / gradient_accumulation_steps
               accelerator.backward(lowercase__ )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(lowercase__,					lowercase__ ):
                    __lowercase					=F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __lowercase					=os.path.join(args.output_dir,					lowercase__ )
                         accelerator.save_state(lowercase__ )

          model.eval()
          for step, batch in enumerate(lowercase__ ):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device )
               with torch.no_grad():
                    __lowercase					=model(**lowercase__ )
               __lowercase					=outputs.logits.argmax(dim=-1 )
               __lowercase		,     __lowercase					=accelerator.gather_for_metrics((predictions, batch['labels']) )
               metric.add_batch(
                   predictions=lowercase__,					references=lowercase__,					)

          __lowercase					=metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:''',					lowercase__ )

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               __lowercase					=F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __lowercase					=os.path.join(args.output_dir,					lowercase__ )
               accelerator.save_state(lowercase__ )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=argparse.ArgumentParser(description='Simple example of training script.' )
     parser.add_argument(
         '--mixed_precision',					type=lowercase__,					default=lowercase__,					choices=['no', 'fp16', 'bf16', 'fp8'],					help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.',					)
     parser.add_argument('--cpu',					action='store_true',					help='If passed, will train on the CPU.' )
     parser.add_argument(
         '--checkpointing_steps',					type=lowercase__,					default=lowercase__,					help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',					)
     parser.add_argument(
         '--output_dir',					type=lowercase__,					default='.',					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',					)
     parser.add_argument(
         '--resume_from_checkpoint',					type=lowercase__,					default=lowercase__,					help='If the training should continue from a checkpoint folder.',					)
     __lowercase					=parser.parse_args()
     __lowercase					={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowercase__,					lowercase__ )


if __name__ == "__main__":
       main()





'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   lowerCAmelCase     :







       def __init__(							self			: List[str]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[int]=13						,      __lowercase			: Dict=32						,      __lowercase			: int=2						,      __lowercase			: Tuple=3						,      __lowercase			: Optional[int]=16						,      __lowercase			: Dict=[1, 2, 1]						,      __lowercase			: Dict=[2, 2, 4]						,      __lowercase			: int=2						,      __lowercase			: Optional[Any]=2.0						,      __lowercase			: int=True						,      __lowercase			: List[Any]=0.0						,      __lowercase			: int=0.0						,      __lowercase			: Optional[Any]=0.1						,      __lowercase			: List[str]="gelu"						,      __lowercase			: Any=False						,      __lowercase			: Dict=True						,      __lowercase			: Optional[Any]=0.0_2						,      __lowercase			: int=1E-5						,      __lowercase			: Dict=True						,      __lowercase			: Optional[int]=None						,      __lowercase			: List[Any]=True						,      __lowercase			: List[Any]=10						,      __lowercase			: Any=8						,      __lowercase			: Optional[int]=["stage1", "stage2"]						,      __lowercase			: str=[1, 2]						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=image_size
            __lowercase					=patch_size
            __lowercase					=num_channels
            __lowercase					=embed_dim
            __lowercase					=depths
            __lowercase					=num_heads
            __lowercase					=window_size
            __lowercase					=mlp_ratio
            __lowercase					=qkv_bias
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=drop_path_rate
            __lowercase					=hidden_act
            __lowercase					=use_absolute_embeddings
            __lowercase					=patch_norm
            __lowercase					=layer_norm_eps
            __lowercase					=initializer_range
            __lowercase					=is_training
            __lowercase					=scope
            __lowercase					=use_labels
            __lowercase					=type_sequence_label_size
            __lowercase					=encoder_stride
            __lowercase					=out_features
            __lowercase					=out_indices







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

            __lowercase					=self.get_config()

            return config, pixel_values, labels







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            return SwinConfig(
                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						,      out_features=self.out_features						,      out_indices=self.out_indices						,      )







       def 			snake_case   (							self			: Optional[Any]						,      __lowercase			: str						,      __lowercase			: Tuple						,      __lowercase			: int      ):







            """simple docstring"""







            __lowercase					=SwinModel(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            __lowercase					=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
            __lowercase					=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			: str						,      __lowercase			: str						,      __lowercase			: Dict						,      __lowercase			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify hidden states
            self.parent.assertEqual(len(result.feature_maps      )						,      len(config.out_features      )      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[0], 16, 16]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      len(config.out_features      )      )

            # verify backbone works with out_features=None
            __lowercase					=None
            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps      )						,      1      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[-1], 4, 4]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      1      )







       def 			snake_case   (							self			: int						,      __lowercase			: int						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForMaskedImageModeling(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(
                result.logits.shape						,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForMaskedImageModeling(__lowercase      )
            model.to(__lowercase      )
            model.eval()

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







       def 			snake_case   (							self			: Any						,      __lowercase			: Optional[Any]						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=self.type_sequence_label_size
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      labels=__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()

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







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.prepare_config_and_inputs()
            (
                (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     
            )					=config_and_inputs
            __lowercase					={'pixel_values': pixel_values}
            return config, inputs_dict






@require_torch
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_		= (
           {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase_		= True

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=__lowercase						,      embed_dim=37      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            return







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase      )







       @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            super().test_training_gradient_checkpointing()







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowercase      )







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase      )







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowercase      )







       @unittest.skip(reason='Swin does not use inputs_embeds'      )
       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            pass







       @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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

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







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







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

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

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

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

                 # also another +1 for reshaped_hidden_states
                 __lowercase					=1 if model_class.__name__ == 'SwinBackbone' else 2
                 self.assertEqual(out_len + added_hidden_states						,      len(__lowercase      )      )

                 __lowercase					=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 			snake_case   (							self			: List[str]						,      __lowercase			: Dict						,      __lowercase			: List[str]						,      __lowercase			: List[str]						,      __lowercase			: Any      ):







            """simple docstring"""







            __lowercase					=model_class(__lowercase      )
            model.to(__lowercase      )
            model.eval()

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

            __lowercase					=outputs.hidden_states

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

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

            __lowercase					=(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]						,      )

            if not model_class.__name__ == "SwinBackbone":
                 __lowercase					=outputs.reshaped_hidden_states
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 __lowercase		,     __lowercase		,     __lowercase		,     __lowercase					=reshaped_hidden_states[0].shape
                 __lowercase					=(
                     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 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            __lowercase					=(
                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:
                 __lowercase					=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"]
                 __lowercase					=True

                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            __lowercase					=3

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

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

            for model_class in self.all_model_classes:
                 __lowercase					=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"]
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      (padded_height, padded_width)      )







       @slow
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=SwinModel.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            __lowercase					=_config_zero_init(__lowercase      )
            for model_class in self.all_model_classes:
                 __lowercase					=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'''						,      )






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







       @cached_property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return (
                AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
                if is_vision_available()
                else None
            )







       @slow
       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(__lowercase      )
            __lowercase					=self.default_image_processor

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

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

            # verify the logits
            __lowercase					=torch.Size((1, 1000)      )
            self.assertEqual(outputs.logits.shape						,      __lowercase      )
            __lowercase					=torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]      ).to(__lowercase      )
            self.assertTrue(torch.allclose(outputs.logits[0, :3]						,      __lowercase						,      atol=1E-4      )      )






@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ,			A   ):
       lowerCAmelCase_		= (SwinBackbone,) if is_torch_available() else ()
       lowerCAmelCase_		= SwinConfig







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )


