




"""simple docstring"""


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A_      			=       get_tests_dir('''fixtures''')
A_      			=       get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
A_      			=       get_tests_dir('''fixtures/dummy-config.json''')






class       lowercase(  unittest.TestCase				):







	'''simple docstring'''





	def 				UpperCamelCase_     (       self:	Tuple	):




			'''simple docstring'''



			_snake_case							:	Any			=						0


	def 				UpperCamelCase_     (       self:	Dict	):




			'''simple docstring'''



			_snake_case							:	Tuple			=						AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""	)
			self.assertIsInstance(_A,   _A	)


	def 				UpperCamelCase_     (       self:	Optional[int]	):




			'''simple docstring'''



			_snake_case							:	str			=						AutoFeatureExtractor.from_pretrained(_A	)
			self.assertIsInstance(_A,   _A	)


	def 				UpperCamelCase_     (       self:	int	):




			'''simple docstring'''



			with tempfile.TemporaryDirectory() as tmpdirname:
					_snake_case							:	int			=						WavaVecaConfig()

					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
					_snake_case							:	Any			=						AutoFeatureExtractor.from_pretrained(_A	).to_dict()

					config_dict.pop("""feature_extractor_type"""	)
					_snake_case							:	str			=						WavaVecaFeatureExtractor(**_A	)

					# save in new folder
					model_config.save_pretrained(_A	)
					config.save_pretrained(_A	)

					_snake_case							:	List[str]			=						AutoFeatureExtractor.from_pretrained(_A	)

					# make sure private variable is not incorrectly saved
					_snake_case							:	Dict			=						json.loads(config.to_json_string()	)
					self.assertTrue("""_processor_class""" not in dict_as_saved	)

			self.assertIsInstance(_A,   _A	)


	def 				UpperCamelCase_     (       self:	Any	):




			'''simple docstring'''



			_snake_case							:	Union[str, Any]			=						AutoFeatureExtractor.from_pretrained(_A	)
			self.assertIsInstance(_A,   _A	)


	def 				UpperCamelCase_     (       self:	str	):




			'''simple docstring'''



			with self.assertRaisesRegex(
			    _A,   """bert-base is not a local folder and is not a valid model identifier"""	):
					_snake_case							:	Union[str, Any]			=						AutoFeatureExtractor.from_pretrained("""bert-base"""	)


	def 				UpperCamelCase_     (       self:	Any	):




			'''simple docstring'''



			with self.assertRaisesRegex(
			    _A,   r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""	):
					_snake_case							:	Union[str, Any]			=						AutoFeatureExtractor.from_pretrained(_A,   revision="""aaaaaa"""	)


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




			'''simple docstring'''



			with self.assertRaisesRegex(
			    _A,   """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""",   ):
					_snake_case							:	str			=						AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""	)


	def 				UpperCamelCase_     (       self:	Any	):




			'''simple docstring'''



			with self.assertRaises(_A	):
					_snake_case							:	List[Any]			=						AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor"""	)
			# If remote code is disabled, we can't load this config.
			with self.assertRaises(_A	):
					_snake_case							:	Optional[Any]			=						AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor""",   trust_remote_code=_A	)

			_snake_case							:	Union[str, Any]			=						AutoFeatureExtractor.from_pretrained(
			    """hf-internal-testing/test_dynamic_feature_extractor""",   trust_remote_code=_A	)
			self.assertEqual(feature_extractor.__class__.__name__,   """NewFeatureExtractor"""	)

			# Test feature extractor can be reloaded.
			with tempfile.TemporaryDirectory() as tmp_dir:
					feature_extractor.save_pretrained(_A	)
					_snake_case							:	str			=						AutoFeatureExtractor.from_pretrained(_A,   trust_remote_code=_A	)
			self.assertEqual(reloaded_feature_extractor.__class__.__name__,   """NewFeatureExtractor"""	)


	def 				UpperCamelCase_     (       self:	str	):




			'''simple docstring'''



			try:
					AutoConfig.register("""custom""",   _A	)
					AutoFeatureExtractor.register(_A,   _A	)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(_A	):
							AutoFeatureExtractor.register(_A,   _A	)

					# Now that the config is registered, it can be used as any other config with the auto-API
					_snake_case							:	Tuple			=						CustomFeatureExtractor.from_pretrained(_A	)
					with tempfile.TemporaryDirectory() as tmp_dir:
							feature_extractor.save_pretrained(_A	)
							_snake_case							:	Union[str, Any]			=						AutoFeatureExtractor.from_pretrained(_A	)
							self.assertIsInstance(_A,   _A	)

			finally:
					if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
							del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]


	def 				UpperCamelCase_     (       self:	Tuple	):




			'''simple docstring'''



			class       lowercase(  a__				):







				'''simple docstring'''





				lowercase__				      =      True

			try:
					AutoConfig.register("""custom""",   _A	)
					AutoFeatureExtractor.register(_A,   _A	)
					# If remote code is not set, the default is to use local
					_snake_case							:	Tuple			=						AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor"""	)
					self.assertEqual(feature_extractor.__class__.__name__,   """NewFeatureExtractor"""	)
					self.assertTrue(feature_extractor.is_local	)

					# If remote code is disabled, we load the local one.
					_snake_case							:	Dict			=						AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor""",   trust_remote_code=_A	)
					self.assertEqual(feature_extractor.__class__.__name__,   """NewFeatureExtractor"""	)
					self.assertTrue(feature_extractor.is_local	)

					# If remote is enabled, we load from the Hub
					_snake_case							:	Optional[int]			=						AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor""",   trust_remote_code=_A	)
					self.assertEqual(feature_extractor.__class__.__name__,   """NewFeatureExtractor"""	)
					self.assertTrue(not hasattr(_A,   """is_local"""	)	)

			finally:
					if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
							del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]








"""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       lowercase:







 '''simple docstring'''





 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Tuple=13,   a_:	Dict=32,   a_:	Optional[Any]=3,   a_:	Optional[Any]=4,   a_:	Optional[int]=[10, 20, 30, 40],   a_:	Any=[2, 2, 3, 2],   a_:	Dict=True,   a_:	Dict=True,   a_:	List[str]=37,   a_:	Dict="gelu",   a_:	List[str]=10,   a_:	Union[str, Any]=0.02,   a_:	Any=["stage2", "stage3", "stage4"],   a_:	Optional[int]=3,   a_:	Tuple=None,   ):




   '''simple docstring'''



   _snake_case							:	Dict			=						parent
   _snake_case							:	Dict			=						batch_size
   _snake_case							:	Optional[Any]			=						image_size
   _snake_case							:	int			=						num_channels
   _snake_case							:	Tuple			=						num_stages
   _snake_case							:	int			=						hidden_sizes
   _snake_case							:	List[str]			=						depths
   _snake_case							:	str			=						is_training
   _snake_case							:	Dict			=						use_labels
   _snake_case							:	List[str]			=						intermediate_size
   _snake_case							:	Optional[int]			=						hidden_act
   _snake_case							:	Any			=						type_sequence_label_size
   _snake_case							:	List[str]			=						initializer_range
   _snake_case							:	Union[str, Any]			=						out_features
   _snake_case							:	Dict			=						num_labels
   _snake_case							:	int			=						scope
   _snake_case							:	Dict			=						num_stages


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Optional[int]			=						None
   if self.use_labels:
     _snake_case							:	Optional[Any]			=						ids_tensor([self.batch_size],   self.type_sequence_label_size	)

   _snake_case							:	Tuple			=						self.get_config()

   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''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 				UpperCamelCase_     (       self:	List[Any]	):




   '''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 				UpperCamelCase_     (       self:	Tuple,   a_:	List[Any],   a_:	Dict,   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						UperNetForSemanticSegmentation(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Tuple			=						model(a_	)
   self.parent.assertEqual(
       result.logits.shape,   (self.batch_size, self.num_labels, self.image_size, self.image_size)	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	List[Any]			=						config_and_inputs
   _snake_case							:	Any			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (UperNetForSemanticSegmentation,) if is_torch_available() else ()
 lowercase__				      =      {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						UperNetModelTester(self	)
   _snake_case							:	Dict			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''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 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return


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




   '''simple docstring'''



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

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

     _snake_case							:	Any			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						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 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not have a base model"""	)
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""UperNet does not have a base model"""	)
 def 				UpperCamelCase_     (       self:	int	):




   '''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 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


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




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   def check_hidden_states_output(a_:	Dict,   a_:	List[str],   a_:	Optional[int]	):
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Any			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Any			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _snake_case							:	List[str]			=						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],   )

   _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	int			=						True
     check_hidden_states_output(a_,   a_,   a_	)

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

     check_hidden_states_output(a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



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

   _snake_case							:	Tuple			=						_config_zero_init(a_	)
   _snake_case							:	Dict			=						_config_zero_init(configs_no_init.backbone_config	)
   for model_class in self.all_model_classes:
     _snake_case							:	Optional[int]			=						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 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   pass


 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	int			=						UperNetForSemanticSegmentation.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						hf_hub_download(
      repo_id="""hf-internal-testing/fixtures_ade20k"""	,     repo_type="""dataset"""	,     filename="""ADE_val_00000001.jpg"""					)
  _snake_case							:	List[Any]			=						Image.open(snake_case__					).convert("""RGB"""					)
  return image






@require_torch
@require_vision
@slow
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""	)
   _snake_case							:	Any			=						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""	).to(a_	)

   _snake_case							:	Dict			=						prepare_img()
   _snake_case							:	str			=						processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   with torch.no_grad():
     _snake_case							:	Tuple			=						model(**a_	)

   _snake_case							:	Tuple			=						torch.Size((1, model.config.num_labels, 512, 512)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	int			=						torch.tensor(
       [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],   a_,   atol=1E-4	)	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""	)
   _snake_case							:	Optional[int]			=						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""	).to(a_	)

   _snake_case							:	List[str]			=						prepare_img()
   _snake_case							:	Tuple			=						processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   with torch.no_grad():
     _snake_case							:	Optional[Any]			=						model(**a_	)

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

   _snake_case							:	Optional[Any]			=						torch.tensor(
       [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]	).to(a_	)
   self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],   a_,   atol=1E-4	)	)


