




import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     YosoForMaskedLM,
     YosoForMultipleChoice,
     YosoForQuestionAnswering,
     YosoForSequenceClassification,
     YosoForTokenClassification,
     YosoModel,
 )
 from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       Dict       ,				snake_case    :       Dict=13       ,				snake_case    :       Union[str, Any]=7       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       List[str]=True       ,				snake_case    :       Dict=True       ,				snake_case    :       str=True       ,				snake_case    :       Optional[Any]=99       ,				snake_case    :       Any=32       ,				snake_case    :       List[str]=5       ,				snake_case    :       Optional[int]=4       ,				snake_case    :       int=37       ,				snake_case    :       Optional[int]="gelu"       ,				snake_case    :       Optional[int]=0.1       ,				snake_case    :       Tuple=0.1       ,				snake_case    :       Any=512       ,				snake_case    :       List[str]=16       ,				snake_case    :       Any=2       ,				snake_case    :       Optional[Any]=0.02       ,				snake_case    :       int=3       ,				snake_case    :       Union[str, Any]=4       ,				snake_case    :       List[Any]=None       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  seq_length
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  use_input_mask
          __UpperCamelCase     						=  use_token_type_ids
          __UpperCamelCase     						=  use_labels
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  hidden_size
          __UpperCamelCase     						=  num_hidden_layers
          __UpperCamelCase     						=  num_attention_heads
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  type_vocab_size
          __UpperCamelCase     						=  type_sequence_label_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  num_labels
          __UpperCamelCase     						=  num_choices
          __UpperCamelCase     						=  scope







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size )

          __UpperCamelCase     						=  None
          if self.use_input_mask:
              __UpperCamelCase     						=  random_attention_mask([self.batch_size, self.seq_length] )

          __UpperCamelCase     						=  None
          if self.use_token_type_ids:
              __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size )

          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  None
          if self.use_labels:
              __UpperCamelCase     						=  ids_tensor([self.batch_size]       ,				self.type_sequence_label_size )
              __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels )
              __UpperCamelCase     						=  ids_tensor([self.batch_size]       ,				self.num_choices )

          __UpperCamelCase     						=  self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







      def 					snake_case		(   self    :       int ):
          return YosoConfig(
              vocab_size=self.vocab_size       ,				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       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				is_decoder=snake_case       ,				initializer_range=self.initializer_range       ,				)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.get_config()
          __UpperCamelCase     						=  300
          return config







      def 					snake_case		(   self    :       Optional[Any] ):
          (
              (
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					
          )     						=  self.prepare_config_and_inputs()

          __UpperCamelCase     						=  True
          __UpperCamelCase     						=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				vocab_size=2 )

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )







      def 					snake_case		(   self    :       str       ,				snake_case    :       str       ,				snake_case    :       str       ,				snake_case    :       str       ,				snake_case    :       int       ,				snake_case    :       Tuple       ,				snake_case    :       int       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  YosoModel(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case )
          __UpperCamelCase     						=  model(snake_case       ,				token_type_ids=snake_case )
          __UpperCamelCase     						=  model(snake_case )
          self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[Any]       ,				snake_case    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Optional[int]       ,				):
          __UpperCamelCase     						=  True
          __UpperCamelCase     						=  YosoModel(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				encoder_hidden_states=snake_case       ,				encoder_attention_mask=snake_case       ,				)
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				encoder_hidden_states=snake_case       ,				)
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case )
          self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size) )







      def 					snake_case		(   self    :       Any       ,				snake_case    :       str       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       str       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Dict       ,				snake_case    :       str ):
          __UpperCamelCase     						=  YosoForMaskedLM(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size) )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Any       ,				snake_case    :       Tuple       ,				snake_case    :       Dict       ,				snake_case    :       List[str]       ,				snake_case    :       Dict       ,				snake_case    :       List[str]       ,				snake_case    :       Dict ):
          __UpperCamelCase     						=  YosoForQuestionAnswering(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				start_positions=snake_case       ,				end_positions=snake_case       ,				)
          self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length) )







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[Any]       ,				snake_case    :       List[Any]       ,				snake_case    :       str       ,				snake_case    :       Any       ,				snake_case    :       str       ,				snake_case    :       Optional[int]       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  self.num_labels
          __UpperCamelCase     						=  YosoForSequenceClassification(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels) )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       str       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       List[Any] ):
          __UpperCamelCase     						=  self.num_labels
          __UpperCamelCase     						=  YosoForTokenClassification(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels) )







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[int]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       int       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  self.num_choices
          __UpperCamelCase     						=  YosoForMultipleChoice(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  input_ids.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  token_type_ids.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  input_mask.unsqueeze(1 ).expand(-1       ,				self.num_choices       ,				-1 ).contiguous()
          __UpperCamelCase     						=  model(
              snake_case       ,				attention_mask=snake_case       ,				token_type_ids=snake_case       ,				labels=snake_case       ,				)
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices) )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					(
              __UpperCamelCase
          )						,					
          )     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_torch
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    (
          (
              YosoModel,
              YosoForMaskedLM,
              YosoForMultipleChoice,
              YosoForQuestionAnswering,
              YosoForSequenceClassification,
              YosoForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__	:       Union[str, Any]			=    False
      lowerCAmelCase__	:       Optional[Any]			=    False
      lowerCAmelCase__	:       List[Any]			=    False

      lowerCAmelCase__	:       str			=    ()
      lowerCAmelCase__	:       str			=    (
          {
              "feature-extraction": YosoModel,
              "fill-mask": YosoForMaskedLM,
              "question-answering": YosoForQuestionAnswering,
              "text-classification": YosoForSequenceClassification,
              "token-classification": YosoForTokenClassification,
              "zero-shot": YosoForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  YosoModelTester(self )
          __UpperCamelCase     						=  ConfigTester(self       ,				config_class=snake_case       ,				hidden_size=37 )







      def 					snake_case		(   self    :       int ):
          self.config_tester.run_common_tests()







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase     						=  type
              self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case )







      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case )







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case )







      @slow
      def 					snake_case		(   self    :       Dict ):
          for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase     						=  YosoModel.from_pretrained(snake_case )
              self.assertIsNotNone(snake_case )







      def 					snake_case		(   self    :       Tuple ):
          return







@require_torch
class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      @slow
      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  YosoModel.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  torch.Size((1, 6, 768) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-0.06_11, 0.12_42, 0.08_40], [0.02_80, -0.00_48, 0.11_25], [0.01_06, 0.02_26, 0.07_51]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )







      @slow
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.tensor([[0, 1, 2, 3, 4, 5]] )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  50265

          __UpperCamelCase     						=  torch.Size((1, 6, vocab_size) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-2.13_13, -3.72_85, -2.24_07], [-2.70_47, -3.33_14, -2.64_08], [0.06_29, -2.51_66, -0.33_56]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )







      @slow
      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096''' )
          __UpperCamelCase     						=  torch.arange(4096 ).unsqueeze(0 )

          with torch.no_grad():
              __UpperCamelCase     						=  model(snake_case )[0]

          __UpperCamelCase     						=  50265

          __UpperCamelCase     						=  torch.Size((1, 4096, vocab_size) )
          self.assertEqual(output.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor(
              [[[-2.39_14, -4.37_42, -5.09_56], [-4.09_88, -4.23_84, -7.04_06], [-3.14_27, -3.71_92, -6.68_00]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]       ,				snake_case       ,				atol=1E-4 ) )







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

						_snake_case						:     Dict				=    0  # Number of remaining (not pruned) params in the encoder
						_snake_case						:     int				=    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":
																								_snake_case						:     Union[str, Any]				=    TopKBinarizer.apply(__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_snake_case						:     str				=    ThresholdBinarizer.apply(__lowercase					,			__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "l0":
																								_snake_case				,_snake_case						:     List[str]				=    -0.1, 1.1
																								_snake_case						:     Any				=    torch.sigmoid(__lowercase						)
																								_snake_case						:     str				=    s * (r - l) + l
																								_snake_case						:     Dict				=    s_bar.clamp(min=0.0					,			max=1.0						)
																								_snake_case						:     Tuple				=    (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(__lowercase						)						)
												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__":
				__SCREAMING_SNAKE_CASE						:    str     = 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',
				)

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)