



'''simple docstring'''





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__a  			=						importlib.util.find_spec("s3fs") is not None

if _has_safs:
       from .safilesystem import SaFileSystem  # noqa: F401

__a  			=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
       if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
              warnings.warn(F"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
       fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      if "://" in dataset_path:
            snake_case__    :       Tuple       		= dataset_path.split("""://"""						)[1]
      return dataset_path





def 		__snake_case(							_lowerCAmelCase						)						->					bool:
      if fs is not None and fs.protocol != "file":
            return True
      else:
            return False





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[int]:
      snake_case__    :       int       		= not is_remote_filesystem(_lowerCAmelCase						)
      if is_local:
            # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
            shutil.move(fs._strip_protocol(_lowerCAmelCase						)	,  fs._strip_protocol(_lowerCAmelCase						)						)
      else:
            fs.mv(_lowerCAmelCase	,  _lowerCAmelCase	,  recursive=_lowerCAmelCase						)





def 		__snake_case(							)						->					None:
      if hasattr(fsspec.asyn	,  """reset_lock"""						):
            # for future fsspec>2022.05.0
            fsspec.asyn.reset_lock()
      else:
            snake_case__    :       Dict       		= None
            snake_case__    :       List[str]       		= None
            snake_case__    :       Union[str, Any]       		= threading.Lock()





'''simple docstring'''





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        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	Optional[int]=3					,	snake_case_   :	List[Any]=True					,	snake_case_   :	Dict=True					,	snake_case_   :	Optional[Any]=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	Dict=224					,	snake_case_   :	Dict=1_000					,	snake_case_   :	Tuple=[3, 3, 6, 4]					,	snake_case_   :	Tuple=[48, 56, 112, 220]					,	):
          snake_case__    :       str       		= parent
          snake_case__    :       Dict       		= batch_size
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       Any       		= is_training
          snake_case__    :       Union[str, Any]       		= use_labels
          snake_case__    :       Tuple       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= num_labels
          snake_case__    :       Any       		= image_size
          snake_case__    :       str       		= layer_depths
          snake_case__    :       Union[str, Any]       		= embed_dims




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

          snake_case__    :       int       		= None
          if self.use_labels:
                snake_case__    :       List[Any]       		= ids_tensor([self.batch_size]					,	self.num_labels		)

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          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=snake_case_					,	layer_scale_init_value=1E-5					,	)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       Dict       		= SwiftFormerModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       List[str]       		= model(snake_case_		)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.embed_dims[-1], 7, 7)		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
          snake_case__    :       Any       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




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

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




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

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= inspect.signature(model.forward		)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__    :       List[str]       		= [*signature.parameters.keys()]

                snake_case__    :       Optional[int]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                with torch.no_grad():
                      snake_case__    :       Tuple       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 8
                self.assertEqual(len(snake_case_		)					,	snake_case_		)  # 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(snake_case_		)		):
                      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),
                              ]		)					,	)

          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= True
                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case__    :       str       		= True

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, Any]       		= copy.deepcopy(snake_case_		)
                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(snake_case_					,	snake_case_					,	1E-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(config=snake_case_		)
                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 lowerCamelCase			(       self   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	List[str]		):
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)
