





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
    from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VivitImageProcessor



class  SCREAMING_SNAKE_CASE_  :


  """simple docstring"""





  def __init__(  self	,  A	,  A=2	,  A=True	,  A=True	,  A=10	,  A=10	,  A=8	,  A=[2, 4, 4]	,  A=3	,  A=7_68	,  A=5	,  A=4	,  A=37	,  A="gelu_fast"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-06	,  A=True	,  A=None	,  )		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       parent
       __magic_name__				=       batch_size
       __magic_name__				=       is_training
       __magic_name__				=       use_labels
       __magic_name__				=       num_labels
       __magic_name__				=       image_size
       __magic_name__				=       num_frames
       __magic_name__				=       tubelet_size
       __magic_name__				=       num_channels
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       qkv_bias
       __magic_name__				=       scope

       __magic_name__				=       (
           (self.image_size // self.tubelet_size[2])
           * (self.image_size // self.tubelet_size[1])
           * (self.num_frames // self.tubelet_size[0])
       ) + 1  # CLS token





  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







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

       __magic_name__				=       None
       if self.use_labels:
            __magic_name__				=       ids_tensor([self.batch_size]	,  self.num_labels						)

       __magic_name__				=       self.get_config()

       return config, pixel_values, labels





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       VivitConfig(
           num_frames=self.num_frames	,  image_size=self.image_size	,  tubelet_size=self.tubelet_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  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	,  initializer_range=self.initializer_range	,  layer_norm_eps=self.layer_norm_eps	,  qkv_bias=self.qkv_bias	,  )
       __magic_name__				=       self.num_labels
       return config





  def 			__A      (  self	,  A	,  A	,  A						)		->   Any:





       '''simple docstring'''







       __magic_name__				=       VivitModel(config=A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A						)
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)						)





  def 			__A      (  self	,  A	,  A	,  A						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       VivitForVideoClassification(A						)
       model.to(A						)
       model.eval()

       __magic_name__				=       model(A						)

       # verify the logits shape
       __magic_name__				=       torch.Size((self.batch_size, self.num_labels)						)
       self.parent.assertEqual(result.logits.shape	,  A						)







  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







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



@require_torch
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__   ,							unittest.TestCase			):


  """simple docstring"""





  _a								=							(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

  _a								=							False
  _a								=							False
  _a								=							False
  _a								=							False





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       VivitModelTester(self						)
       __magic_name__				=       ConfigTester(self	,  config_class=A	,  has_text_modality=A	,  hidden_size=37						)





  def 			__A      (  self	,  A	,  A	,  A=False						)		->   Union[str, Any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(A						)

       if return_labels:
            if model_class in get_values(A						):
                 __magic_name__				=       torch.zeros(
                     self.model_tester.batch_size	,  dtype=torch.long	,  device=A						)

       return inputs_dict





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





       '''simple docstring'''







       self.config_tester.run_common_tests()





  @unittest.skip(reason='''Vivit does not use inputs_embeds'''						)
  def 			__A      (  self						)		->   Union[str, Any]:





       '''simple docstring'''







       pass





  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__		,  __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __magic_name__				=       model_class(A						)
            self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)						)
            __magic_name__				=       model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A	,  nn.Linear						)						)





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__		,  __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

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

            __magic_name__				=       ['''pixel_values''', '''head_mask''']
            self.assertListEqual(arg_names[:2]	,  A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A						)





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





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_video_classification(*A						)





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





       '''simple docstring'''







       for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __magic_name__				=       VivitModel.from_pretrained(A						)
            self.assertIsNotNone(A						)





  def 			__A      (  self						)		->   int:





       '''simple docstring'''







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

       for model_class in self.all_model_classes:
            __magic_name__				=       self.model_tester.seq_length

            __magic_name__				=       True
            __magic_name__				=       False
            __magic_name__				=       True
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.eval()
            with torch.no_grad():
                 __magic_name__				=       model(**self._prepare_for_class(A	,  A						)						)
            __magic_name__				=       outputs.attentions
            self.assertEqual(len(A						)	,  self.model_tester.num_hidden_layers						)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __magic_name__				=       True
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.eval()
            with torch.no_grad():
                 __magic_name__				=       model(**self._prepare_for_class(A	,  A						)						)
            __magic_name__				=       outputs.attentions
            self.assertEqual(len(A						)	,  self.model_tester.num_hidden_layers						)

            self.assertListEqual(
                list(attentions[0].shape[-3:]						)	,  [self.model_tester.num_attention_heads, seq_len, seq_len]	,  )
            __magic_name__				=       len(A						)

            # Check attention is always last and order is fine
            __magic_name__				=       True
            __magic_name__				=       True
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.eval()
            with torch.no_grad():
                 __magic_name__				=       model(**self._prepare_for_class(A	,  A						)						)

            self.assertEqual(out_len + 1	,  len(A						)						)

            __magic_name__				=       outputs.attentions

            self.assertEqual(len(A						)	,  self.model_tester.num_hidden_layers						)

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]						)	,  [self.model_tester.num_attention_heads, seq_len, seq_len]	,  )





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       def check_hidden_states_output(A	,  A	,  A						):
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.eval()

            with torch.no_grad():
                 __magic_name__				=       model(**self._prepare_for_class(A	,  A						)						)

            __magic_name__				=       outputs.hidden_states
            __magic_name__				=       self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(A						)	,  A						)

            __magic_name__				=       self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]						)	,  [seq_length, self.model_tester.hidden_size]	,  )

       __magic_name__		,  __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __magic_name__				=       True
            check_hidden_states_output(A	,  A	,  A						)

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

            check_hidden_states_output(A	,  A	,  A						)







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





       '''simple docstring'''







       pass




def   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video''' ,     filename='''eating_spaghetti_32_frames.npy''' ,     repo_type='''dataset'''						)
     __magic_name__				=       np.load(snake_case_						)
     return list(snake_case_						)



@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  @cached_property
  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       return VivitImageProcessor() if is_vision_available() else None







  @slow
  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''						).to(A						)

       __magic_name__				=       self.default_image_processor
       __magic_name__				=       prepare_video()
       __magic_name__				=       image_processor(A	,  return_tensors='''pt'''						).to(A						)

       # forward pass
       with torch.no_grad():
            __magic_name__				=       model(**A						)

       # verify the logits
       __magic_name__				=       torch.Size((1, 4_00)						)
       self.assertEqual(outputs.logits.shape	,  A						)

       # taken from original model
       __magic_name__				=       torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]						).to(A						)

       self.assertTrue(torch.allclose(outputs.logits[0, :5]	,  A	,  atol=1E-4						)						)






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       0  # Number of params in the encoder

     print('''name'''.ljust(60 ,     ''' '''						) ,     '''Remaining Weights %''' ,     '''Remaining Weight'''						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (mask > 0.0).sum().item()
               else:
                    raise ValueError('''Unknown pruning method'''						)
               remaining_count += mask_ones
               print(name.ljust(60 ,     ''' '''						) ,     str(round(100 * mask_ones / param.numel() ,     3						)						).ljust(20 ,     ''' '''						) ,     str(snake_case_						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''''''						)
     print('''Remaining Weights (global) %: ''' ,     100 * remaining_count / encoder_count						)


if __name__ == "__main__":
    a_    :				Optional[Any]					=			argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `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(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)