





"""simple docstring"""




import argparse
import os
import shutil

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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		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(lowerCamelCase__			,  """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=lowerCamelCase__			,  threshold=lowerCamelCase__     )
								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(lowerCamelCase__			,  lowerCamelCase__     )
								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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
								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(lowerCamelCase__     )
								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(lowerCamelCase__     )			,  f"""bertarized_{os.path.basename(lowerCamelCase__     )}"""     )

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

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


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

			__lowerCAmelCase    :							int													=						parser.parse_args()

			main(args)





"""simple docstring"""




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   CLIPConfig()

									# Create a dummy config file with image_proceesor_type
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									# remove image_processor_type to make sure config.json alone is enough to load image processor locally
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     ).to_dict()

									config_dict.pop("""image_processor_type"""     )
									lowerCAmelCase__        =   CLIPImageProcessor(**snake_case__     )

									# save in new folder
									model_config.save_pretrained(snake_case__     )
									config.save_pretrained(snake_case__     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )

									# make sure private variable is not incorrectly saved
									lowerCAmelCase__        =   json.loads(config.to_json_string()     )
									self.assertTrue("""_processor_class""" not in dict_as_saved     )

							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""clip-base is not a local folder and is not a valid model identifier"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""clip-base"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						revision="""aaaaaa"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,						):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )

							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
							self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						trust_remote_code=snake_case__     )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(snake_case__     ):
											AutoImageProcessor.register(snake_case__  ,						snake_case__     )

									with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
											lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
											json.dump(
											    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
											json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

											lowerCAmelCase__        =   CustomImageProcessor.from_pretrained(snake_case__     )

									# Now that the config is registered, it can be used as any other config with the auto-API
									with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(snake_case__     )
											lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
											self.assertIsInstance(snake_case__  ,						snake_case__     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):

							class       a_      (     __UpperCamelCase			):
												UpperCamelCase_ :	Tuple			=    True

							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# If remote code is not set, the default is to use local
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote code is disabled, we load the local one.
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote is enabled, we load from the Hub
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(not hasattr(snake_case__  ,						"""is_local"""     )     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




