




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=["""image_processor""", """tokenizer"""]
				__a							: List[str]										="""OwlViTImageProcessor"""
				__a							: List[Any]										=("""CLIPTokenizer""", """CLIPTokenizerFast""")



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
											lowerCAmelCase   =		None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																		    ''' instead.'''       , UpperCAmelCase_       , )
																		lowerCAmelCase   =		kwargs.pop('''feature_extractor'''						)

											lowerCAmelCase   =		image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('''You need to specify an `image_processor`.'''						)
											if tokenizer is None:
																		raise ValueError('''You need to specify a `tokenizer`.'''						)

											super().__init__(UpperCAmelCase_       , UpperCAmelCase_						)



				def __call__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_="max_length"       , UpperCAmelCase_="np"       , **UpperCAmelCase_						):

											if text is None and query_images is None and images is None:
																		raise ValueError(
																		    '''You have to specify at least one text or query image or image. All three cannot be none.'''						)

											if text is not None:
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						) or (isinstance(UpperCAmelCase_       , UpperCAmelCase_						) and not isinstance(text[0]       , UpperCAmelCase_						)):
																									lowerCAmelCase   =		[self.tokenizer(UpperCAmelCase_       , padding=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)]

																		elif isinstance(UpperCAmelCase_       , UpperCAmelCase_						) and isinstance(text[0]       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[]

																									# Maximum number of queries across batch
																									lowerCAmelCase   =		max([len(UpperCAmelCase_						) for t in text]						)

																									# Pad all batch samples to max number of text queries
																									for t in text:
																																if len(UpperCAmelCase_						) != max_num_queries:
																																							lowerCAmelCase   =		t + [''' '''] * (max_num_queries - len(UpperCAmelCase_						))

																																lowerCAmelCase   =		self.tokenizer(UpperCAmelCase_       , padding=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)
																																encodings.append(UpperCAmelCase_						)
																		else:
																									raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''						)

																		if return_tensors == "np":
																									lowerCAmelCase   =		np.concatenate([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		np.concatenate([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		elif return_tensors == "jax" and is_flax_available():
																									import jax.numpy as jnp

																									lowerCAmelCase   =		jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		elif return_tensors == "pt" and is_torch_available():
																									import torch

																									lowerCAmelCase   =		torch.cat([encoding['''input_ids'''] for encoding in encodings]       , dim=0						)
																									lowerCAmelCase   =		torch.cat([encoding['''attention_mask'''] for encoding in encodings]       , dim=0						)

																		elif return_tensors == "tf" and is_tf_available():
																									import tensorflow as tf

																									lowerCAmelCase   =		tf.stack([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		tf.stack([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		else:
																									raise ValueError('''Target return tensor type could not be returned'''						)

																		lowerCAmelCase   =		BatchEncoding()
																		lowerCAmelCase   =		input_ids
																		lowerCAmelCase   =		attention_mask

											if query_images is not None:
																		lowerCAmelCase   =		BatchEncoding()
																		lowerCAmelCase   =		self.image_processor(
																		    UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						).pixel_values
																		lowerCAmelCase   =		query_pixel_values

											if images is not None:
																		lowerCAmelCase   =		self.image_processor(UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)

											if text is not None and images is not None:
																		lowerCAmelCase   =		image_features.pixel_values
																		return encoding
											elif query_images is not None and images is not None:
																		lowerCAmelCase   =		image_features.pixel_values
																		return encoding
											elif text is not None or query_images is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**UpperCAmelCase_						)       , tensor_type=UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process_object_detection(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.batch_decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor_class






				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor









import argparse
import os
import shutil

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



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

							lowerCAmelCase   =		args.model_name_or_path.rstrip('''/'''							)
							lowerCAmelCase   =		args.target_model_path

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

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

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

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

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


if __name__ == "__main__":
		UpperCAmelCase_						=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""",
		)

		UpperCAmelCase_						=parser.parse_args()

		main(args)



