



'''simple docstring'''

from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
		import numpy as np
		from decord import VideoReader


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

_A				:    List[Any]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Any     ,  *UpperCamelCase_				:	List[Any]     ,  **UpperCamelCase_				:	Tuple					)  ->       Optional[int]:



						'''simple docstring'''





						super().__init__(*UpperCamelCase_     ,  **UpperCamelCase_					)
						requires_backends(self     ,  'decord'					)
						self.check_model_type(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	List[Any]=None					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if frame_sampling_rate is not None:
							_lowercase					:       str       =		frame_sampling_rate
						if num_frames is not None:
							_lowercase					:       int       =		num_frames

						_lowercase					:       str       =		{}
						if top_k is not None:
							_lowercase					:       Any       =		top_k
						return preprocess_params, {}, postprocess_params




					def __call__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[str, List[str]]     ,  **UpperCamelCase_				:	str					)  ->       int:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Tuple=1					)  ->       Dict:



						'''simple docstring'''





						if num_frames is None:
							_lowercase					:       str       =		self.model.config.num_frames

						if video.startswith('http://'					) or video.startswith('https://'					):
							_lowercase					:       Tuple       =		BytesIO(requests.get(UpperCamelCase_					).content					)

						_lowercase					:       str       =		VideoReader(UpperCamelCase_					)
						videoreader.seek(0					)

						_lowercase					:       Any       =		0
						_lowercase					:       Dict       =		num_frames * frame_sampling_rate - 1
						_lowercase					:       Optional[int]       =		np.linspace(UpperCamelCase_     ,  UpperCamelCase_     ,  num=UpperCamelCase_     ,  dtype=np.intaa					)

						_lowercase					:       Union[str, Any]       =		videoreader.get_batch(UpperCamelCase_					).asnumpy()
						_lowercase					:       Dict       =		list(UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		self.image_processor(UpperCamelCase_     ,  return_tensors=self.framework					)
						return model_inputs




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.model(**UpperCamelCase_					)
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Optional[int]=5					)  ->       Optional[Any]:



						'''simple docstring'''





						if top_k > self.model.config.num_labels:
							_lowercase					:       Optional[int]       =		self.model.config.num_labels

						if self.framework == "pt":
							_lowercase					:       List[Any]       =		model_outputs.logits.softmax(-1					)[0]
							_lowercase  ,    _lowercase					:       Tuple       =		probs.topk(UpperCamelCase_					)
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		scores.tolist()
						_lowercase					:       Optional[int]       =		ids.tolist()
						return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_     ,  UpperCamelCase_					)]





'''simple docstring'''

import argparse
import os
import shutil

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







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Tuple       =		args.pruning_method
	_lowercase					:       int       =		args.threshold

	_lowercase					:       str       =		args.model_name_or_path.rstrip('/'					)
	_lowercase					:       Dict       =		args.target_model_path

	print(f'''Load fine-pruned model from {model_name_or_path}'''					)
	_lowercase					:       str       =		torch.load(os.path.join(_lowercase,				'pytorch_model.bin'					)					)
	_lowercase					:       List[Any]       =		{}

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

	if target_model_path is None:
		_lowercase					:       List[Any]       =		os.path.join(
		    os.path.dirname(_lowercase					),				f'''bertarized_{os.path.basename(_lowercase					)}'''					)

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

	torch.save(_lowercase,				os.path.join(_lowercase,				'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				:    List[Any]     						=parser.parse_args()

		main(args)
