




import argparse
import os
import re

import packaging.version


UpperCAmelCase_						="""examples/"""
UpperCAmelCase_						={
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCAmelCase_						={
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
UpperCAmelCase_						="""README.md"""



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							with open(_snake_case						, '''r'''						, encoding='''utf-8'''						, newline='''\n'''							) as f:
														lowerCAmelCase   =		f.read()
							lowerCAmelCase   ,     lowerCAmelCase   =		REPLACE_PATTERNS[pattern]
							lowerCAmelCase   =		replace.replace('''VERSION'''						, _snake_case							)
							lowerCAmelCase   =		re_pattern.sub(_snake_case						, _snake_case							)
							with open(_snake_case						, '''w'''						, encoding='''utf-8'''						, newline='''\n'''							) as f:
														f.write(_snake_case							)



def       UpperCAmelCase       (     _snake_case							):
							for folder, directories, fnames in os.walk(_snake_case							):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove('''research_projects'''							)
														if "legacy" in directories:
																					directories.remove('''legacy'''							)
														for fname in fnames:
																					if fname.endswith('''.py'''							):
																												update_version_in_file(os.path.join(_snake_case						, _snake_case							)						, _snake_case						, pattern='''examples'''							)



def       UpperCAmelCase       (     _snake_case						, _snake_case=False							):
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(_snake_case						, _snake_case						, _snake_case							)
							if not patch:
														update_version_in_examples(_snake_case							)



def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		'''🤗 Transformers currently provides the following architectures'''
							lowerCAmelCase   =		'''1. Want to contribute a new model?'''
							with open(_snake_case						, '''r'''						, encoding='''utf-8'''						, newline='''\n'''							) as f:
														lowerCAmelCase   =		f.readlines()

							# Find the start of the list.
							lowerCAmelCase   =		0
							while not lines[start_index].startswith(_start_prompt							):
														start_index += 1
							start_index += 1

							lowerCAmelCase   =		start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt							):
														if lines[index].startswith('''1.'''							):
																					lowerCAmelCase   =		lines[index].replace(
																					    '''https://huggingface.co/docs/transformers/main/model_doc'''						, '''https://huggingface.co/docs/transformers/model_doc'''						, )
														index += 1

							with open(_snake_case						, '''w'''						, encoding='''utf-8'''						, newline='''\n'''							) as f:
														f.writelines(_snake_case							)



def       UpperCAmelCase       (     ):
							with open(REPLACE_FILES['''init''']						, '''r'''							) as f:
														lowerCAmelCase   =		f.read()
							lowerCAmelCase   =		REPLACE_PATTERNS['''init'''][0].search(_snake_case							).groups()[0]
							return packaging.version.parse(_snake_case							)



def       UpperCAmelCase       (     _snake_case=False							):
							lowerCAmelCase   =		get_version()
							if patch and default_version.is_devrelease:
														raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''							)
							if default_version.is_devrelease:
														lowerCAmelCase   =		default_version.base_version
							elif patch:
														lowerCAmelCase   =		F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														lowerCAmelCase   =		F"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							lowerCAmelCase   =		input(F"""Which version are you releasing? [{default_version}]"""							)
							if len(_snake_case							) == 0:
														lowerCAmelCase   =		default_version

							print(F"""Updating version to {version}."""							)
							global_version_update(_snake_case						, patch=_snake_case							)
							if not patch:
														print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''							)
														clean_main_ref_in_model_list()



def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		get_version()
							lowerCAmelCase   =		F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							lowerCAmelCase   =		current_version.base_version

							# Check with the user we got that right.
							lowerCAmelCase   =		input(F"""Which version are we developing now? [{dev_version}]"""							)
							if len(_snake_case							) == 0:
														lowerCAmelCase   =		dev_version

							print(F"""Updating version to {version}."""							)
							global_version_update(_snake_case							)
							print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''							)
							clean_main_ref_in_model_list()


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()
		parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
		parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
		UpperCAmelCase_						=parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("""Nothing to do after a patch :-)""")
		else:
				post_release_work()









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)



