

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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






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






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     __lowerCAmelCase  :      Tuple    =						None
     if self.use_labels:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size]	,							self.type_sequence_label_size   )

     __lowerCAmelCase  :      List[Any]    =						self.get_config()

     return config, pixel_values, labels







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     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 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     return UperNetConfig(
         backbone_config=self.get_backbone_config()	,							hidden_size=5_12	,							pool_scales=[1, 2, 3, 6]	,							use_auxiliary_head=lowerCAmelCase	,							auxiliary_loss_weight=0.4	,							auxiliary_in_channels=40	,							auxiliary_channels=2_56	,							auxiliary_num_convs=1	,							auxiliary_concat_input=lowerCAmelCase	,							loss_ignore_index=2_55	,							num_labels=self.num_labels	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]   )							->     Dict:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Any:






     """simple docstring"""







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






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Any										=(UperNetForSemanticSegmentation,) if is_torch_available() else ()
  lowerCamelCase   :      List[str]										={"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
  lowerCamelCase   :      Dict										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      str										=False
  lowerCamelCase   :      List[Any]										=False
  lowerCamelCase   :      Optional[Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						UperNetModelTester(self   )
     __lowerCAmelCase  :      Dict    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							has_text_modality=lowerCAmelCase	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     return







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Dict:






     """simple docstring"""







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

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

        __lowerCAmelCase  :      Optional[int]    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase   )







  @unittest.skip(reason="""UperNet does not use inputs_embeds"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not support input and output embeddings"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not have a base model"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







     pass







  @unittest.skip(reason="""UperNet does not have a base model"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     def check_hidden_states_output(lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   List[Any]   ):
        __lowerCAmelCase  :      int    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase  :      Optional[int]    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )

        __lowerCAmelCase  :      str    =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        __lowerCAmelCase  :      Optional[Any]    =						self.model_tester.num_stages
        self.assertEqual(len(lowerCAmelCase   )	,							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]	,							)

     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Dict    =						True
        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCAmelCase  :      str    =						True

        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







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

     __lowerCAmelCase  :      Any    =						_config_zero_init(lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						_config_zero_init(configs_no_init.backbone_config   )
     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      str    =						model_class(config=lowerCAmelCase   )
        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 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







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






def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      int    =						hf_hub_download(
       repo_id="""hf-internal-testing/fixtures_ade20k""" ,       repo_type="""dataset""" ,       filename="""ADE_val_00000001.jpg"""					)
   __lowerCAmelCase  :      str    =						Image.open(__A					).convert("""RGB"""					)
   return image



@require_torch
@require_vision
@slow
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     int:






     """simple docstring"""







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

     __lowerCAmelCase  :      List[str]    =						prepare_img()
     __lowerCAmelCase  :      int    =						processor(images=lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )

     with torch.no_grad():
        __lowerCAmelCase  :      Any    =						model(**lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
     self.assertEqual(outputs.logits.shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						torch.tensor(
         [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""   )
     __lowerCAmelCase  :      Optional[int]    =						UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""   ).to(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						prepare_img()
     __lowerCAmelCase  :      List[Any]    =						processor(images=lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )

     with torch.no_grad():
        __lowerCAmelCase  :      Union[str, Any]    =						model(**lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						torch.Size((1, model.config.num_labels, 5_12, 5_12)   )
     self.assertEqual(outputs.logits.shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						torch.tensor(
         [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]   ).to(lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )





from pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


