



"""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 :

 def __init__(  self	: List[Any]						,  lowercase_	: Dict						,  lowercase_	: Union[str, Any]=13						,  lowercase_	: str=3						,  lowercase_	: Optional[int]=True						,  lowercase_	: int=True						,  lowercase_	: int=0.1						,  lowercase_	: int=0.1						,  lowercase_	: Any=224						,  lowercase_	: Optional[int]=1000						,  lowercase_	: Dict=[3, 3, 6, 4]						,  lowercase_	: Dict=[48, 56, 112, 220]						,  ):
     snake_case_				:  str       =			parent
     snake_case_				:  List[str]       =			batch_size
     snake_case_				:  Optional[Any]       =			num_channels
     snake_case_				:  Optional[Any]       =			is_training
     snake_case_				:  Optional[Any]       =			use_labels
     snake_case_				:  str       =			hidden_dropout_prob
     snake_case_				:  Dict       =			attention_probs_dropout_prob
     snake_case_				:  List[Any]       =			num_labels
     snake_case_				:  int       =			image_size
     snake_case_				:  Any       =			layer_depths
     snake_case_				:  List[str]       =			embed_dims

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  int       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

     snake_case_				:  int       =			None
     if self.use_labels:
         snake_case_				:  Union[str, Any]       =			ids_tensor([self.batch_size]						,  self.num_labels       )

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

     return config, pixel_values, labels

 def 		_snake_case      (  self	: Tuple       ):
     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=lowercase_						,  layer_scale_init_value=1E-5						,  )

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

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

     snake_case_				:  Any       =			SwiftFormerForImageClassification(lowercase_       )
     model.to(lowercase_       )
     model.eval()

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






 def 		_snake_case      (  self	: Tuple       ):
     ((snake_case_),			(snake_case_),			(snake_case_))				:  Any       =			self.prepare_config_and_inputs()
     snake_case_				:  Optional[Any]       =			{'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_torch
class  _UpperCAmelCase (    lowercase__					,  lowercase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Optional[Any]	      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase     :    int	      =	(
     {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase     :    List[Any]	      =	False
 _lowerCAmelCase     :    Dict	      =	False
 _lowerCAmelCase     :    int	      =	False
 _lowerCAmelCase     :    str	      =	False
 _lowerCAmelCase     :    Optional[Any]	      =	False

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  List[str]       =			SwiftFormerModelTester(self       )
     snake_case_				:  Tuple       =			ConfigTester(
         self						,  config_class=lowercase_						,  has_text_modality=lowercase_						,  hidden_size=37						,  num_attention_heads=12						,  num_hidden_layers=12						,  )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     self.config_tester.run_common_tests()

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

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_,			snake_case_				:  Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()

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

 def 		_snake_case      (  self	: Dict       ):
     snake_case_,			snake_case_				:  Optional[int]       =			self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_				:  Tuple       =			['''pixel_values''']
         self.assertListEqual(arg_names[:1]						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Any       =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_       )

 @slow
 def 		_snake_case      (  self	: Tuple       ):
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_				:  Any       =			SwiftFormerModel.from_pretrained(lowercase_       )
         self.assertIsNotNone(lowercase_       )

 @unittest.skip(reason='''SwiftFormer does not output attentions'''       )
 def 		_snake_case      (  self	: int       ):
     pass

 def 		_snake_case      (  self	: Dict       ):
     def check_hidden_states_output(lowercase_	: Union[str, Any]						,  lowercase_	: str						,  lowercase_	: Optional[int]       ):
         snake_case_				:  Optional[Any]       =			model_class(lowercase_       )
         model.to(lowercase_       )
         model.eval()

         with torch.no_grad():
             snake_case_				:  int       =			model(**self._prepare_for_class(lowercase_						,  lowercase_       )       )

         snake_case_				:  Optional[int]       =			outputs.hidden_states

         snake_case_				:  str       =			8
         self.assertEqual(len(lowercase_       )						,  lowercase_       )  # 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(lowercase_       )       ):
             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_				:  Any       =			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_				:  List[Any]       =			True
         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_				:  Union[str, Any]       =			True

         check_hidden_states_output(lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     def _config_zero_init(lowercase_	: Union[str, Any]       ):
         snake_case_				:  Optional[int]       =			copy.deepcopy(lowercase_       )
         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(lowercase_						,  lowercase_						,  1E-10       )
             if isinstance(getattr(lowercase_						,  lowercase_						,  lowercase_       )						,  lowercase_       ):
                 snake_case_				:  str       =			_config_zero_init(getattr(lowercase_						,  lowercase_       )       )
                 setattr(lowercase_						,  lowercase_						,  lowercase_       )
         return configs_no_init

     snake_case_,			snake_case_				:  Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_				:  str       =			_config_zero_init(lowercase_       )
     for model_class in self.all_model_classes:
         snake_case_				:  Optional[Any]       =			model_class(config=lowercase_       )
         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 		_snake_case      (  self	: Union[str, Any]       ):
     pass





def __lowercase    (							):
    snake_case_				:  Optional[Any]       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image






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

 @cached_property
 def 		_snake_case      (  self	: Any       ):
     return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''       ) if is_vision_available() else None






 @slow
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  int       =			SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''       ).to(lowercase_       )

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

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[str]       =			model(**lowercase_       )

     # verify the logits
     snake_case_				:  Optional[int]       =			torch.Size((1, 1000)       )
     self.assertEqual(outputs.logits.shape						,  lowercase_       )

     snake_case_				:  List[str]       =			torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]       ).to(lowercase_       )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





