
"""simple docstring"""




import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__ :       Optional[int]				= get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		XLMProphetNetTokenizer
       UpperCamelCase_ =		False
       UpperCamelCase_ =		True
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase:      Tuple   			=  XLMProphetNetTokenizer(A_			,					keep_accents=A_							)
         tokenizer.save_pretrained(self.tmpdirname							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  '''[PAD]'''
         _lowercase:      Any   			=  0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_							)			,					A_							)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_							)			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  list(self.get_tokenizer().get_vocab().keys()							)

         self.assertEqual(vocab_keys[0]			,					'''[PAD]'''							)
         self.assertEqual(vocab_keys[1]			,					'''[CLS]'''							)
         self.assertEqual(vocab_keys[-1]			,					'''j'''							)
         self.assertEqual(len(A_							)			,					1012							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         self.assertEqual(self.get_tokenizer().vocab_size			,					1012							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  XLMProphetNetTokenizer(A_			,					keep_accents=A_							)

         _lowercase:      Union[str, Any]   			=  tokenizer.tokenize('''This is a test'''							)
         self.assertListEqual(A_			,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(A_							)			,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,					)

         _lowercase:      Optional[int]   			=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
         self.assertListEqual(
             A_			,					[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''9''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''é''',
                 '''.''',
             ]			,					)
         _lowercase:      Optional[int]   			=  tokenizer.convert_tokens_to_ids(A_							)
         self.assertListEqual(
             A_			,					[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
             ]			,					)

         _lowercase:      int   			=  tokenizer.convert_ids_to_tokens(A_							)
         self.assertListEqual(
             A_			,					[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''[UNK]''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''[UNK]''',
                 '''.''',
             ]			,					)
       @cached_property
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''							)
       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  '''Hello World!'''
         _lowercase:      List[str]   			=  [3_5389, 6672, 49, 2]
         self.assertListEqual(A_			,					self.big_tokenizer.encode(A_							)							)



       @slow
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  {'''input_ids''': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=A_			,					model_name='''microsoft/xprophetnet-large-wiki100-cased'''			,					revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''			,					)


"""simple docstring"""




import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import UperNetForSemanticSegmentation
  from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class       __magic_name__							:
       def __init__(  self			,					A_			,					A_=13			,					A_=32			,					A_=3			,					A_=4			,					A_=[10, 20, 30, 40]			,					A_=[2, 2, 3, 2]			,					A_=True			,					A_=True			,					A_=37			,					A_="gelu"			,					A_=10			,					A_=0.02			,					A_=["stage2", "stage3", "stage4"]			,					A_=3			,					A_=None			,					)  ->						List[str]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      Union[str, Any]   			=  batch_size
         _lowercase:      Tuple   			=  image_size
         _lowercase:      Tuple   			=  num_channels
         _lowercase:      Optional[int]   			=  num_stages
         _lowercase:      List[Any]   			=  hidden_sizes
         _lowercase:      Dict   			=  depths
         _lowercase:      int   			=  is_training
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      Any   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_act
         _lowercase:      str   			=  type_sequence_label_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  out_features
         _lowercase:      Any   			=  num_labels
         _lowercase:      Any   			=  scope
         _lowercase:      str   			=  num_stages
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)

         _lowercase:      Tuple   			=  self.get_config()

         return config, pixel_values, labels
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return ConvNextConfig(
             num_channels=self.num_channels			,					num_stages=self.num_stages			,					hidden_sizes=self.hidden_sizes			,					depths=self.depths			,					is_training=self.is_training			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					out_features=self.out_features			,					)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return UperNetConfig(
             backbone_config=self.get_backbone_config()			,					hidden_size=512			,					pool_scales=[1, 2, 3, 6]			,					use_auxiliary_head=A_			,					auxiliary_loss_weight=0.4			,					auxiliary_in_channels=40			,					auxiliary_channels=256			,					auxiliary_num_convs=1			,					auxiliary_concat_input=A_			,					loss_ignore_index=255			,					num_labels=self.num_labels			,					)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  UperNetForSemanticSegmentation(config=A_							)
         model.to(A_							)
         model.eval()
         _lowercase:      int   			=  model(A_							)
         self.parent.assertEqual(
             result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size, self.image_size)							)



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





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.prepare_config_and_inputs()
         (
             (
             _lowercase
         )			,					(
             _lowercase
         )			,					(
             _lowercase
         )			,					
         ):      Tuple   			=  config_and_inputs
         _lowercase:      str   			=  {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  UperNetModelTester(self							)
         _lowercase:      Optional[Any]   			=  ConfigTester(self			,					config_class=A_			,					has_text_modality=A_			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




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

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

           _lowercase:      int   			=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A_							)
       @unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @require_torch_multi_gpu
       @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         pass
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         def check_hidden_states_output(A_			,					A_			,					A_							):
           _lowercase:      Dict   			=  model_class(A_							)
           model.to(A_							)
           model.eval()

           with torch.no_grad():
             _lowercase:      List[str]   			=  model(**self._prepare_for_class(A_			,					A_							)							)

           _lowercase:      Tuple   			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase:      Union[str, Any]   			=  self.model_tester.num_stages
           self.assertEqual(len(A_							)			,					expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)			,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,					)

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

         for model_class in self.all_model_classes:
           _lowercase:      Optional[int]   			=  True
           check_hidden_states_output(A_			,					A_			,					A_							)

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

           check_hidden_states_output(A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:      int   			=  _config_zero_init(A_							)
         _lowercase:      Optional[Any]   			=  _config_zero_init(configs_no_init.backbone_config							)
         for model_class in self.all_model_classes:
           _lowercase:      Any   			=  model_class(config=A_							)
           for name, param in model.named_parameters():
             if param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item()			,					[0.0, 1.0]			,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,					)
       @unittest.skip(reason='''UperNet does not have tied weights'''							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         pass



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      Union[str, Any]   			=  UperNetForSemanticSegmentation.from_pretrained(A_							)
           self.assertIsNotNone(A_							)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  hf_hub_download(
      repo_id='''hf-internal-testing/fixtures_ade20k'''			,					repo_type='''dataset'''			,					filename='''ADE_val_00000001.jpg'''    )
  _lowercase:      Optional[int]   			=  Image.open(_UpperCamelCase    ).convert('''RGB'''    )
  return image







@require_torch
@require_vision
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
         _lowercase:      Optional[Any]   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(A_							)

         _lowercase:      List[Any]   			=  prepare_img()
         _lowercase:      Any   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Union[str, Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Optional[Any]   			=  torch.tensor(
             [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
         _lowercase:      str   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(A_							)

         _lowercase:      Union[str, Any]   			=  prepare_img()
         _lowercase:      Optional[Any]   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Optional[Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Tuple   			=  torch.tensor(
             [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)
