
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)





class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: List[str]							,A					: Tuple=13							,A					: List[Any]=7							,A					: Optional[int]=True							,A					: Tuple=True							,A					: List[Any]=True							,A					: Optional[int]=True							,A					: str=99							,A					: Tuple=32							,A					: List[str]=5							,A					: Dict=4							,A					: int=37							,A					: Optional[int]="gelu"							,A					: Optional[int]=0.1							,A					: Any=0.1							,A					: int=5_12							,A					: List[str]=16							,A					: List[str]=2							,A					: int=0.02							,A					: Union[str, Any]=4							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= seq_length
					__A   			= is_training
					__A   			= use_attention_mask
					__A   			= use_token_type_ids
					__A   			= use_labels
					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= type_sequence_label_size
					__A   			= initializer_range
					__A   			= num_choices






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

					__A   			= None
					if self.use_attention_mask:
									__A   			= random_attention_mask([self.batch_size, self.seq_length] )

					__A   			= DistilBertConfig(
					    vocab_size=self.vocab_size							,dim=self.hidden_size							,n_layers=self.num_hidden_layers							,n_heads=self.num_attention_heads							,hidden_dim=self.intermediate_size							,hidden_act=self.hidden_act							,dropout=self.hidden_dropout_prob							,attention_dropout=self.attention_probs_dropout_prob							,max_position_embeddings=self.max_position_embeddings							,initializer_range=self.initializer_range							,tie_weights_=A							,)

					return config, input_ids, attention_mask







	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.prepare_config_and_inputs()
					__A			,							__A			,							__A   			= config_and_inputs
					__A   			= {"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict





@require_flax
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= FlaxDistilBertModelTester(self )







	@slow
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					for model_class_name in self.all_model_classes:
									__A   			= model_class_name.from_pretrained("distilbert-base-uncased" )
									__A   			= model(np.ones((1, 1) ) )
									self.assertIsNotNone(A )





@require_flax
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= FlaxDistilBertModel.from_pretrained("distilbert-base-uncased" )
					__A   			= np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
					__A   			= np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
					__A   			= model(A							,attention_mask=A )[0]
					__A   			= (1, 11, 7_68)
					self.assertEqual(output.shape							,A )
					__A   			= np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] )

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,A							,atol=1E-4 ) )


import argparse
import os
import shutil

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


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

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

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

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

				if not os.path.isdir(a_						):
								shutil.copytree(a_		,   a_						)
								print(F'''\nCreated folder {target_model_path}'''						)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
