






"""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 						snake_case  :





    def __init__(							self     : List[str]    ,		A     : Any    ,		A     : List[Any]=1_3    ,		A     : Optional[Any]=3    ,		A     : Union[str, Any]=True    ,		A     : List[Any]=True    ,		A     : Optional[Any]=0.1    ,		A     : int=0.1    ,		A     : Dict=2_2_4    ,		A     : int=1_0_0_0    ,		A     : List[str]=[3, 3, 6, 4]    ,		A     : Dict=[4_8, 5_6, 1_1_2, 2_2_0]    ,		):


      '''simple docstring'''




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





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


      '''simple docstring'''




      a					:      int    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

      a					:      Optional[Any]    =       self.get_config()

      return config, pixel_values, labels





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''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=A    ,		layer_scale_init_value=1E-5    ,		)





    def     lowerCamelCase__				(							self     : Dict    ,		A     : Tuple    ,		A     : int    ,		A     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Dict    =       SwiftFormerModel(config=A      )
      model.to(A      )
      model.eval()
      a					:      int    =       model(A      )
      self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.embed_dims[-1], 7, 7)      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : Optional[Any]    ,		A     : Union[str, Any]    ,		A     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Any    =       self.num_labels
      a					:      Tuple    =       SwiftFormerForImageClassification(A      )
      model.to(A      )
      model.eval()
      a					:      Tuple    =       model(A    ,		labels=A      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)      )

      a					:      List[Any]    =       SwiftFormerForImageClassification(A      )
      model.to(A      )
      model.eval()

      a					:      Optional[int]    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
      a					:      str    =       model(A      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)      )





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      ((a),					(a),					(a))					:      int    =       self.prepare_config_and_inputs()
      a					:      List[Any]    =       {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __magic_name__   					=					(
        {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      str    =       SwiftFormerModelTester(self      )
      a					:      Optional[Any]    =       ConfigTester(
          self    ,		config_class=A    ,		has_text_modality=A    ,		hidden_size=3_7    ,		num_attention_heads=1_2    ,		num_hidden_layers=1_2    ,		)





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    @unittest.skip(reason='SwiftFormer does not use inputs_embeds'      )
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a,					a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a					:      Dict    =       model_class(A      )
        a					:      Any    =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A    ,		nn.Linear      )      )





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a,					a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs_for_common()

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

        a					:      Any    =       ['pixel_values']
        self.assertListEqual(arg_names[:1]    ,		A      )





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Any    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A      )





    @slow
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a					:      List[str]    =       SwiftFormerModel.from_pretrained(A      )
        self.assertIsNotNone(A      )





    @unittest.skip(reason='SwiftFormer does not output attentions'      )
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      def check_hidden_states_output(A     : Any    ,		A     : Union[str, Any]    ,		A     : int      ):
        a					:      Optional[Any]    =       model_class(A      )
        model.to(A      )
        model.eval()

        with torch.no_grad():
          a					:      List[Any]    =       model(**self._prepare_for_class(A    ,		A      )      )

        a					:      Optional[Any]    =       outputs.hidden_states

        a					:      Union[str, Any]    =       8
        self.assertEqual(len(A      )    ,		A      )  # 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(A      )      ):
          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),
                  ]      )    ,		)

      a,					a					:      Dict    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a					:      List[str]    =       True
        check_hidden_states_output(A    ,		A    ,		A      )

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

        check_hidden_states_output(A    ,		A    ,		A      )





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


      '''simple docstring'''




      def _config_zero_init(A     : List[str]      ):
        a					:      Any    =       copy.deepcopy(A      )
        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(A    ,		A    ,		1E-10      )
          if isinstance(getattr(A    ,		A    ,		A      )    ,		A      ):
            a					:      Any    =       _config_zero_init(getattr(A    ,		A      )      )
            setattr(A    ,		A    ,		A      )
        return configs_no_init

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

      a					:      Optional[int]    =       _config_zero_init(A      )
      for model_class in self.all_model_classes:
        a					:      Optional[Any]    =       model_class(config=A      )
        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     : Union[str, Any]      ):


      '''simple docstring'''




      pass




def       snake_case  ():







  '''simple docstring'''

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






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





    @cached_property
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




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





    @slow
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      str    =       SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'      ).to(A      )

      a					:      Optional[int]    =       self.default_image_processor
      a					:      List[str]    =       prepare_img()
      a					:      Any    =       image_processor(images=A    ,		return_tensors='pt'      ).to(A      )

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

      # verify the logits
      a					:      Optional[int]    =       torch.Size((1, 1_0_0_0)      )
      self.assertEqual(outputs.logits.shape    ,		A      )

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












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




