


'''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     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Dict  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Any=13  ,		UpperCamelCase       :       Any=3  ,		UpperCamelCase       :       Tuple=True  ,		UpperCamelCase       :       Optional[int]=True  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       str=0.1  ,		UpperCamelCase       :       Dict=224  ,		UpperCamelCase       :       Union[str, Any]=1000  ,		UpperCamelCase       :       List[str]=[3, 3, 6, 4]  ,		UpperCamelCase       :       Optional[Any]=[48, 56, 112, 220]  ,		):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







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

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

             lowercase__      =					self.get_config()

             return config, pixel_values, labels



      def  UpperCamelCase__						(self       :       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=UpperCamelCase  ,		layer_scale_init_value=1E-5  ,		)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







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

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

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






      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             ((lowercase__)     ,(lowercase__)     ,(lowercase__))      =					self.prepare_config_and_inputs()
             lowercase__      =					{'''pixel_values''': pixel_values}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






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

      lowerCAmelCase__   :	List[str]       =				False
      lowerCAmelCase__   :	Optional[Any]       =				False
      lowerCAmelCase__   :	Optional[Any]       =				False
      lowerCAmelCase__   :	Optional[int]       =				False
      lowerCAmelCase__   :	str       =				False



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''					)
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__      =					['''pixel_values''']
                    self.assertListEqual(arg_names[:1]  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







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



      @slow
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







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



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

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             def check_hidden_states_output(UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       List[str]					):
                    lowercase__      =					model_class(UpperCamelCase					)
                    model.to(UpperCamelCase					)
                    model.eval()

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

                    lowercase__      =					outputs.hidden_states

                    lowercase__      =					8
                    self.assertEqual(len(UpperCamelCase					)  ,		UpperCamelCase					)  # 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(UpperCamelCase					)					):
                           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),
                                   ]					)  ,		)

             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

                    check_hidden_states_output(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)



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

             '''simple docstring'''







             def _config_zero_init(UpperCamelCase       :       Dict					):
                    lowercase__      =					copy.deepcopy(UpperCamelCase					)
                    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(UpperCamelCase  ,		UpperCamelCase  ,		1E-10					)
                           if isinstance(getattr(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)  ,		UpperCamelCase					):
                                  lowercase__      =					_config_zero_init(getattr(UpperCamelCase  ,		UpperCamelCase					)					)
                                  setattr(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)
                    return configs_no_init

             lowercase__     ,lowercase__      =					self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      =					_config_zero_init(UpperCamelCase					)
             for model_class in self.all_model_classes:
                    lowercase__      =					model_class(config=UpperCamelCase					)
                    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  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






def _SCREAMING_SNAKE_CASE ()			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
       return image

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





      '''simple docstring'''






      @cached_property
      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







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






      @slow
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''					).to(UpperCamelCase					)

             lowercase__      =					self.default_image_processor
             lowercase__      =					prepare_img()
             lowercase__      =					image_processor(images=UpperCamelCase  ,		return_tensors='''pt'''					).to(UpperCamelCase					)

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

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

             lowercase__      =					torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]					).to(UpperCamelCase					)
             self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)








'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



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

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






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

             '''simple docstring'''







             pass




