






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 		_A						:

  def __init__(    self						:	int   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Dict=13   ,				lowerCamelCase__						:	int=3   ,				lowerCamelCase__						:	List[str]=True   ,				lowerCamelCase__						:	Union[str, Any]=True   ,				lowerCamelCase__						:	str=0.1   ,				lowerCamelCase__						:	Any=0.1   ,				lowerCamelCase__						:	Optional[Any]=2_24   ,				lowerCamelCase__						:	Union[str, Any]=10_00   ,				lowerCamelCase__						:	Union[str, Any]=[3, 3, 6, 4]   ,				lowerCamelCase__						:	Optional[Any]=[48, 56, 1_12, 2_20]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			int					=					parent
     __UpperCamelCase	:			Any					=					batch_size
     __UpperCamelCase	:			List[str]					=					num_channels
     __UpperCamelCase	:			Union[str, Any]					=					is_training
     __UpperCamelCase	:			List[Any]					=					use_labels
     __UpperCamelCase	:			Union[str, Any]					=					hidden_dropout_prob
     __UpperCamelCase	:			List[str]					=					attention_probs_dropout_prob
     __UpperCamelCase	:			Tuple					=					num_labels
     __UpperCamelCase	:			Dict					=					image_size
     __UpperCamelCase	:			Optional[Any]					=					layer_depths
     __UpperCamelCase	:			Optional[int]					=					embed_dims

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     __UpperCamelCase	:			List[str]					=					None
     if self.use_labels:
        __UpperCamelCase	:			List[str]					=					ids_tensor([self.batch_size]   ,				self.num_labels			)

     __UpperCamelCase	:			Union[str, Any]					=					self.get_config()

     return config, pixel_values, labels

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

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

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					SwiftFormerModel(config=lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Dict					=					model(lowerCamelCase__			)
     self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.embed_dims[-1], 7, 7)			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					self.num_labels
     __UpperCamelCase	:			List[str]					=					SwiftFormerForImageClassification(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Optional[Any]					=					model(lowerCamelCase__   ,				labels=lowerCamelCase__			)
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)			)

     __UpperCamelCase	:			int					=					SwiftFormerForImageClassification(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()

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

  def    a					(    self						:	List[str]			):
     """simple docstring"""

     ((__UpperCamelCase)	,     (__UpperCamelCase)	,     (__UpperCamelCase))	:			Any					=					self.prepare_config_and_inputs()
     __UpperCamelCase	:			Optional[Any]					=					{"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class 		_A						( UpperCAmelCase_	,				UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  lowercase_						:			str						=					(
      {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase_						:			Optional[Any]						=					False
  lowercase_						:			Optional[int]						=					False
  lowercase_						:			str						=					False
  lowercase_						:			str						=					False
  lowercase_						:			Union[str, Any]						=					False

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					SwiftFormerModelTester(self			)
     __UpperCamelCase	:			Optional[Any]					=					ConfigTester(
         self   ,				config_class=lowerCamelCase__   ,				has_text_modality=lowerCamelCase__   ,				hidden_size=37   ,				num_attention_heads=12   ,				num_hidden_layers=12   ,				)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     self.config_tester.run_common_tests()

  @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     pass

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __UpperCamelCase	:			int					=					model_class(lowerCamelCase__			)
        __UpperCamelCase	:			Dict					=					model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase__   ,				nn.Linear			)			)

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

        __UpperCamelCase	:			List[str]					=					["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,				lowerCamelCase__			)

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)

  @slow
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __UpperCamelCase	:			List[Any]					=					SwiftFormerModel.from_pretrained(lowerCamelCase__			)
        self.assertIsNotNone(lowerCamelCase__			)

  @unittest.skip(reason="""SwiftFormer does not output attentions"""			)
  def    a					(    self						:	str			):
     """simple docstring"""

     pass

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     def check_hidden_states_output(lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	str			):
        __UpperCamelCase	:			Dict					=					model_class(lowerCamelCase__			)
        model.to(lowerCamelCase__			)
        model.eval()

        with torch.no_grad():
           __UpperCamelCase	:			str					=					model(**self._prepare_for_class(lowerCamelCase__   ,				lowerCamelCase__			)			)

        __UpperCamelCase	:			str					=					outputs.hidden_states

        __UpperCamelCase	:			Any					=					8
        self.assertEqual(len(lowerCamelCase__			)   ,				lowerCamelCase__			)  # TODO

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

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

     for model_class in self.all_model_classes:
        __UpperCamelCase	:			Optional[Any]					=					True
        check_hidden_states_output(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)

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

        check_hidden_states_output(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	int			):
     """simple docstring"""

     def _config_zero_init(lowerCamelCase__						:	int			):
        __UpperCamelCase	:			Dict					=					copy.deepcopy(lowerCamelCase__			)
        for key in configs_no_init.__dict__.keys():
           if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
              setattr(lowerCamelCase__   ,				lowerCamelCase__   ,				1e-10			)
           if isinstance(getattr(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)   ,				lowerCamelCase__			):
              __UpperCamelCase	:			List[str]					=					_config_zero_init(getattr(lowerCamelCase__   ,				lowerCamelCase__			)			)
              setattr(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)
        return configs_no_init

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

     __UpperCamelCase	:			List[Any]					=					_config_zero_init(lowerCamelCase__			)
     for model_class in self.all_model_classes:
        __UpperCamelCase	:			Tuple					=					model_class(config=lowerCamelCase__			)
        for name, param in model.named_parameters():
           if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9) / 1e9).round().item()   ,				[0.0, 1.0]   ,				msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,				)

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
  def    a					(    self						:	int			):
     """simple docstring"""

     pass







def 				__lowerCamelCase     (							)     ->  Any:
   __UpperCamelCase	:			int					=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   return image







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

  @cached_property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

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

  @slow
  def    a					(    self						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(lowerCamelCase__			)

     __UpperCamelCase	:			int					=					self.default_image_processor
     __UpperCamelCase	:			Tuple					=					prepare_img()
     __UpperCamelCase	:			Any					=					image_processor(images=lowerCamelCase__   ,				return_tensors="""pt"""			).to(lowerCamelCase__			)

     # forward pass
     with torch.no_grad():
        __UpperCamelCase	:			List[Any]					=					model(**lowerCamelCase__			)

     # verify the logits
     __UpperCamelCase	:			Optional[Any]					=					torch.Size((1, 10_00)			)
     self.assertEqual(outputs.logits.shape   ,				lowerCamelCase__			)

     __UpperCamelCase	:			Tuple					=					torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]			).to(lowerCamelCase__			)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				lowerCamelCase__   ,				atol=1e-4			)			)











def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  bool:
   if not all(x.isalpha() for x in string							):
      raise ValueError("""String must only contain alphabetic characters."""							)

   __UpperCamelCase	:			List[str]					=					sorted(string.lower()							)
   return len(__lowerCAmelCase							) == len(set(__lowerCAmelCase							)							)


if __name__ == "__main__":
       UpperCamelCase    							=      input('Enter a string ').strip()

       UpperCamelCase    							=      is_isogram(input_str)
       print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")



