






'''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        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=224    ,	_SCREAMING_SNAKE_CASE=1000    ,	_SCREAMING_SNAKE_CASE=[3, 3, 6, 4]    ,	_SCREAMING_SNAKE_CASE=[48, 56, 112, 220]    ,	)						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      hidden_dropout_prob
        UpperCamelCase       						=      attention_probs_dropout_prob
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      layer_depths
        UpperCamelCase       						=      embed_dims

  def 						A__		(   self   )						->   str:



        """simple docstring"""






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

        UpperCamelCase       						=      None
        if self.use_labels:
              UpperCamelCase       						=      ids_tensor([self.batch_size]    ,	self.num_labels   )

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels

  def 						A__		(   self   )						->   Tuple:



        """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=_SCREAMING_SNAKE_CASE    ,	layer_scale_init_value=1e-5    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      SwiftFormerModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )

        UpperCamelCase       						=      SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()

        UpperCamelCase       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        ((UpperCamelCase)						,(UpperCamelCase)						,(UpperCamelCase))       						=      self.prepare_config_and_inputs()
        UpperCamelCase       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  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 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      SwiftFormerModelTester(self   )
        UpperCamelCase       						=      ConfigTester(
            self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE    ,	nn.Linear   )   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )

  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )

  @unittest.skip(reason="""SwiftFormer does not output attentions"""   )
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

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

              UpperCamelCase       						=      outputs.hidden_states

              UpperCamelCase       						=      8
              self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_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(_SCREAMING_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),
                            ]   )    ,	)

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase       						=      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        def _config_zero_init(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      copy.deepcopy(_SCREAMING_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(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	1e-10   )
                    if isinstance(getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   ):
                          UpperCamelCase       						=      _config_zero_init(getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )
                          setattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              return configs_no_init

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase       						=      _config_zero_init(_SCREAMING_SNAKE_CASE   )
        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(config=_SCREAMING_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 						A__		(   self   )						->   int:



        """simple docstring"""






        pass







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   str:



        """simple docstring"""






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


  @slow
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.default_image_processor
        UpperCamelCase       						=      prepare_img()
        UpperCamelCase       						=      image_processor(images=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).to(_SCREAMING_SNAKE_CASE   )

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

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 1000)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

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








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
