

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   UpperCamelCase			:

    def __init__(      self			:   Union[str, Any]				,     snake_case__			:   Dict				,     snake_case__			:   Union[str, Any]=1_3				,     snake_case__			:   Dict=3_2				,     snake_case__			:   Optional[int]=3				,     snake_case__			:   int=4				,     snake_case__			:   Tuple=[1_0, 2_0, 3_0, 4_0]				,     snake_case__			:   Any=[2, 2, 3, 2]				,     snake_case__			:   Any=True				,     snake_case__			:   List[str]=True				,     snake_case__			:   Tuple=3_7				,     snake_case__			:   int="gelu"				,     snake_case__			:   Optional[int]=1_0				,     snake_case__			:   Dict=0.02				,     snake_case__			:   int=["stage2", "stage3", "stage4"]				,     snake_case__			:   List[str]=3				,     snake_case__			:   List[str]=None				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      image_size
     SCREAMING_SNAKE_CASE		    =      num_channels
     SCREAMING_SNAKE_CASE		    =      num_stages
     SCREAMING_SNAKE_CASE		    =      hidden_sizes
     SCREAMING_SNAKE_CASE		    =      depths
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      intermediate_size
     SCREAMING_SNAKE_CASE		    =      hidden_act
     SCREAMING_SNAKE_CASE		    =      type_sequence_label_size
     SCREAMING_SNAKE_CASE		    =      initializer_range
     SCREAMING_SNAKE_CASE		    =      out_features
     SCREAMING_SNAKE_CASE		    =      num_labels
     SCREAMING_SNAKE_CASE		    =      scope
     SCREAMING_SNAKE_CASE		    =      num_stages

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE		    =      self.get_config()

     return config, pixel_values, labels

    def UpperCamelCase      (      self			:   Union[str, 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 UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return UperNetConfig(
         backbone_config=self.get_backbone_config()				,     hidden_size=5_1_2				,     pool_scales=[1, 2, 3, 6]				,     use_auxiliary_head=snake_case__				,     auxiliary_loss_weight=0.4				,     auxiliary_in_channels=4_0				,     auxiliary_channels=2_5_6				,     auxiliary_num_convs=1				,     auxiliary_concat_input=snake_case__				,     loss_ignore_index=2_5_5				,     num_labels=self.num_labels				,     )

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   str				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      UperNetForSemanticSegmentation(config=snake_case__     )
     model.to(snake_case__     )
     model.eval()
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(
         result.logits.shape				,     (self.batch_size, self.num_labels, self.image_size, self.image_size)     )




    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






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




@require_torch
class   UpperCamelCase			(  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 UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      UperNetModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__				,     has_text_modality=snake_case__				,     hidden_size=3_7     )

    def UpperCamelCase      (      self			:   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 UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     return

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

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

      SCREAMING_SNAKE_CASE		    =      ['pixel_values']
      self.assertListEqual(arg_names[:1]				,     snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__     )

    @unittest.skip(reason='UperNet does not use inputs_embeds'     )
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     pass

    @unittest.skip(reason='UperNet does not support input and output embeddings'     )
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     pass

    @unittest.skip(reason='UperNet does not have a base model'     )
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     pass

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

     """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			:   Dict     ):

     """simple docstring"""






     def check_hidden_states_output(snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   List[Any]     ):
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.eval()

      with torch.no_grad():
       SCREAMING_SNAKE_CASE		    =      model(**self._prepare_for_class(snake_case__				,     snake_case__     )     )

      SCREAMING_SNAKE_CASE		    =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      SCREAMING_SNAKE_CASE		    =      self.model_tester.num_stages
      self.assertEqual(len(snake_case__     )				,     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]				,     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      True
      check_hidden_states_output(snake_case__				,     snake_case__				,     snake_case__     )

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

      check_hidden_states_output(snake_case__				,     snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE		    =      _config_zero_init(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      _config_zero_init(configs_no_init.backbone_config     )
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class(config=snake_case__     )
      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			:   Optional[Any]     ):

     """simple docstring"""






     pass




    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE		    =      UperNetForSemanticSegmentation.from_pretrained(snake_case__     )
      self.assertIsNotNone(snake_case__     )


def 							__lowerCAmelCase     (							)		->				List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      hf_hub_download(
     repo_id='hf-internal-testing/fixtures_ade20k'		,				repo_type='dataset'		,				filename='ADE_val_00000001.jpg'						)
 SCREAMING_SNAKE_CASE		    =      Image.open(_UpperCamelCase						).convert('RGB'						)
 return image




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

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'     )
     SCREAMING_SNAKE_CASE		    =      UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      prepare_img()
     SCREAMING_SNAKE_CASE		    =      processor(images=snake_case__				,     return_tensors='pt'     ).to(snake_case__     )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)     )
     self.assertEqual(outputs.logits.shape				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      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(snake_case__     )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,     snake_case__				,     atol=1E-4     )     )




    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'     )
     SCREAMING_SNAKE_CASE		    =      UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      prepare_img()
     SCREAMING_SNAKE_CASE		    =      processor(images=snake_case__				,     return_tensors='pt'     ).to(snake_case__     )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)     )
     self.assertEqual(outputs.logits.shape				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      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(snake_case__     )
     self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,     snake_case__				,     atol=1E-4     )     )




import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

 SCREAMING_SNAKE_CASE		    =      args.model_name_or_path.rstrip('/'						)
 SCREAMING_SNAKE_CASE		    =      args.target_model_path

 print(f"""Load fine-pruned model from {model_name_or_path}"""						)
 SCREAMING_SNAKE_CASE		    =      torch.load(os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 SCREAMING_SNAKE_CASE		    =      {}

 for name, tensor in model.items():
  if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  elif "classifier" in name or "qa_output" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  elif "bias" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  else:
   if pruning_method == "magnitude":
    SCREAMING_SNAKE_CASE		    =      MagnitudeBinarizer.apply(inputs=_UpperCamelCase		,				threshold=_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "topK":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE		    =      TopKBinarizer.apply(_UpperCamelCase		,				_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "sigmoied_threshold":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE		    =      ThresholdBinarizer.apply(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "l0":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      -0.1, 1.1
    SCREAMING_SNAKE_CASE		    =      torch.sigmoid(_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      s * (r - l) + l
    SCREAMING_SNAKE_CASE		    =      s_bar.clamp(min=0.0		,				max=1.0						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   else:
    raise ValueError('Unknown pruning method'						)

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

 if not os.path.isdir(_UpperCamelCase						):
  shutil.copytree(_UpperCamelCase		,				_UpperCamelCase						)
  print(f"""\nCreated folder {target_model_path}"""						)

 torch.save(_UpperCamelCase		,				os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 print('\nPruned model saved! See you later!'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()

      parser.add_argument(
          "--pruning_method",
          choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
          type=str,
          required=True,
          help=(
              "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
              " sigmoied_threshold = Soft movement pruning)"
          ),
      )
      parser.add_argument(
          "--threshold",
          type=float,
          required=False,
          help=(
              "For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
              "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
              "Not needed for `l0`"
          ),
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          required=True,
          help="Folder containing the model that was previously fine-pruned",
      )
      parser.add_argument(
          "--target_model_path",
          default=None,
          type=str,
          required=False,
          help="Folder containing the model that was previously fine-pruned",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

