

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

      from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
      from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DPTImageProcessor




class   UpperCamelCase			:

    def __init__(      self			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   Optional[Any]=2				,     snake_case__			:   List[str]=3_2				,     snake_case__			:   Optional[int]=1_6				,     snake_case__			:   Union[str, Any]=3				,     snake_case__			:   Dict=True				,     snake_case__			:   List[Any]=True				,     snake_case__			:   Tuple=3_2				,     snake_case__			:   Any=4				,     snake_case__			:   int=[0, 1, 2, 3]				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   str=3_7				,     snake_case__			:   Optional[int]="gelu"				,     snake_case__			:   Union[str, Any]=0.1				,     snake_case__			:   int=0.1				,     snake_case__			:   List[Any]=0.02				,     snake_case__			:   Optional[Any]=3				,     snake_case__			:   Dict=[1, 3_8_4, 2_4, 2_4]				,     snake_case__			:   Tuple=True				,     snake_case__			:   str=None				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      image_size
     SCREAMING_SNAKE_CASE		    =      patch_size
     SCREAMING_SNAKE_CASE		    =      num_channels
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      hidden_size
     SCREAMING_SNAKE_CASE		    =      num_hidden_layers
     SCREAMING_SNAKE_CASE		    =      backbone_out_indices
     SCREAMING_SNAKE_CASE		    =      num_attention_heads
     SCREAMING_SNAKE_CASE		    =      intermediate_size
     SCREAMING_SNAKE_CASE		    =      hidden_act
     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      initializer_range
     SCREAMING_SNAKE_CASE		    =      num_labels
     SCREAMING_SNAKE_CASE		    =      backbone_featmap_shape
     SCREAMING_SNAKE_CASE		    =      scope
     SCREAMING_SNAKE_CASE		    =      is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     SCREAMING_SNAKE_CASE		    =      (image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE		    =      num_patches + 1

    def UpperCamelCase      (      self			:   Tuple     ):

     """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.image_size, self.image_size]				,     self.num_labels     )

     SCREAMING_SNAKE_CASE		    =      self.get_config()

     return config, pixel_values, labels

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      {
         'global_padding': 'same',
         'layer_type': 'bottleneck',
         'depths': [3, 4, 9],
         'out_features': ['stage1', 'stage2', 'stage3'],
         'embedding_dynamic_padding': True,
         'hidden_sizes': [9_6, 1_9_2, 3_8_4, 7_6_8],
         'num_groups': 2,
     }

     return DPTConfig(
         image_size=self.image_size				,     patch_size=self.patch_size				,     num_channels=self.num_channels				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     backbone_out_indices=self.backbone_out_indices				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     is_decoder=snake_case__				,     initializer_range=self.initializer_range				,     is_hybrid=self.is_hybrid				,     backbone_config=snake_case__				,     backbone_featmap_shape=self.backbone_featmap_shape				,     )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      DPTModel(config=snake_case__     )
     model.to(snake_case__     )
     model.eval()
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)     )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.num_labels
     SCREAMING_SNAKE_CASE		    =      DPTForDepthEstimation(snake_case__     )
     model.to(snake_case__     )
     model.eval()
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.predicted_depth.shape				,     (self.batch_size, self.image_size, self.image_size)     )

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

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   List[Any]     ):

     """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    =(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    __UpperCamelCase    =(
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __UpperCamelCase    =False
    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     self.config_tester.run_common_tests()

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

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Any     ):

     """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__     )
      self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module)     )
      SCREAMING_SNAKE_CASE		    =      model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(snake_case__				,     nn.Linear     )     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """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			:   List[Any]     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
       continue

      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE		    =      True

      if model_class in get_values(snake_case__     ):
       continue

      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.train()
      SCREAMING_SNAKE_CASE		    =      self._prepare_for_class(snake_case__				,     snake_case__				,     return_labels=snake_case__     )
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     ).loss
      loss.backward()

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
       continue

      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE		    =      False
      SCREAMING_SNAKE_CASE		    =      True

      if model_class in get_values(snake_case__     ) or not model_class.supports_gradient_checkpointing:
       continue
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.gradient_checkpointing_enable()
      model.train()
      SCREAMING_SNAKE_CASE		    =      self._prepare_for_class(snake_case__				,     snake_case__				,     return_labels=snake_case__     )
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     ).loss
      loss.backward()

    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__     )
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class(config=snake_case__     )
      # Skip the check for the backbone
      SCREAMING_SNAKE_CASE		    =      []
      for name, module in model.named_modules():
       if module.__class__.__name__ == "DPTViTHybridEmbeddings":
        SCREAMING_SNAKE_CASE		    =      [F"""{name}.{key}""" for key in module.state_dict().keys()]
        break

      for name, param in model.named_parameters():
       if param.requires_grad:
        if name in backbone_params:
         continue
        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('Will be fixed soon by reducing the size of the model used for common tests.'     )
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     pass

    @slow
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE		    =      'add'
     with self.assertRaises(snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      DPTForDepthEstimation(snake_case__     )


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


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




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

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'     )
     SCREAMING_SNAKE_CASE		    =      DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'     ).to(snake_case__     )

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

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )
      SCREAMING_SNAKE_CASE		    =      outputs.predicted_depth

     # verify the predicted depth
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 3_8_4, 3_8_4)     )
     self.assertEqual(predicted_depth.shape				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]     ).to(snake_case__     )

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0				,     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)

