






import argparse
import os
import shutil

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





def 					lowercase_ (    __snake_case     :							List[Any]      )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   args.pruning_method
				snake_case__      :str         =   args.threshold

				snake_case__      :List[str]         =   args.model_name_or_path.rstrip("/"      )
				snake_case__      :List[Any]         =   args.target_model_path

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

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

				if target_model_path is None:
								snake_case__      :Optional[int]         =   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							:   Optional[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",
	)

	__UpperCAmelCase							:   Dict							=  parser.parse_args()

	main(args)







import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
	from cookiecutter.main import cookiecutter

	__UpperCAmelCase							:   Dict							=  True
except ImportError:
	__UpperCAmelCase							:   List[Any]							=  False

__UpperCAmelCase							:   Dict							=  logging.get_logger(__name__)  # pylint: disable=invalid-name





def 					lowercase_ (    __snake_case     :							Namespace      )     -> Dict:





				'''simple docstring'''


				return AddNewModelCommand(args.testing	,	args.testing_file	,	path=args.path      )


class 					_snake_case						(			_A ):






	@staticmethod
	def    lowerCAmelCase_							(		UpperCamelCase       )						->    Any:
					snake_case__      :Dict         =   parser.add_parser("add-new-model"       )
					add_new_model_parser.add_argument("--testing"					,action="store_true"					,help="If in testing mode."       )
					add_new_model_parser.add_argument("--testing_file"					,type=UpperCamelCase					,help="Configuration file on which to run."       )
					add_new_model_parser.add_argument(
					    "--path"					,type=UpperCamelCase					,help="Path to cookiecutter. Should only be used for testing purposes."       )
					add_new_model_parser.set_defaults(func=UpperCamelCase       )






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None					,*UpperCamelCase       )						->    Any:
					snake_case__      :Union[str, Any]         =   testing
					snake_case__      :Union[str, Any]         =   testing_file
					snake_case__      :List[str]         =   path






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					warnings.warn(
					    "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
					    "It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
					    "checks, you should use `transformers-cli add-new-model-like` instead."       )
					if not _has_cookiecutter:
									raise ImportError(
									    "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
									    "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"       )
					# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
					snake_case__      :Tuple         =   [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
					if len(UpperCamelCase       ) > 0:
									raise ValueError(
									    "Several directories starting with `cookiecutter-template-` in current working directory. "
									    "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
									    "change your working directory."       )

					snake_case__      :str         =   (
					    Path(UpperCamelCase       ).parent.parent.parent.parent if self._path is None else Path(self._path       ).parent.parent
					)
					snake_case__      :Tuple         =   path_to_transformer_root / "templates" / "adding_a_new_model"

					# Execute cookiecutter
					if not self._testing:
									cookiecutter(str(UpperCamelCase       )       )
					else:
									with open(self._testing_file					,"r"       ) as configuration_file:
													snake_case__      :str         =   json.load(UpperCamelCase       )

									cookiecutter(
									    str(path_to_cookiecutter if self._path is None else self._path       )					,no_input=UpperCamelCase					,extra_context=UpperCamelCase					,)

					snake_case__      :List[Any]         =   [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

					# Retrieve configuration
					with open(directory + "/configuration.json"					,"r"       ) as configuration_file:
									snake_case__      :Dict         =   json.load(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   configuration["lowercase_modelname"]
					snake_case__      :List[Any]         =   configuration["generate_tensorflow_pytorch_and_flax"]
					os.remove(f'{directory}/configuration.json'       )

					snake_case__      :Any         =   "PyTorch" in generate_tensorflow_pytorch_and_flax
					snake_case__      :Any         =   "TensorFlow" in generate_tensorflow_pytorch_and_flax
					snake_case__      :Any         =   "Flax" in generate_tensorflow_pytorch_and_flax

					snake_case__      :Dict         =   f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
					os.makedirs(UpperCamelCase					,exist_ok=UpperCamelCase       )
					os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}'					,exist_ok=UpperCamelCase       )

					# Tests require submodules as they have parent imports
					with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'					,"w"       ):
									pass

					shutil.move(
					    f'{directory}/__init__.py'					,f'{model_dir}/__init__.py'					,)
					shutil.move(
					    f'{directory}/configuration_{lowercase_model_name}.py'					,f'{model_dir}/configuration_{lowercase_model_name}.py'					,)

					def remove_copy_lines(UpperCamelCase       ):
									with open(UpperCamelCase					,"r"       ) as f:
													snake_case__      :List[str]         =   f.readlines()
									with open(UpperCamelCase					,"w"       ) as f:
													for line in lines:
																	if "# Copied from transformers." not in line:
																					f.write(UpperCamelCase       )

					if output_pytorch:
									if not self._testing:
													remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py'       )

									shutil.move(
									    f'{directory}/modeling_{lowercase_model_name}.py'					,f'{model_dir}/modeling_{lowercase_model_name}.py'					,)

									shutil.move(
									    f'{directory}/test_modeling_{lowercase_model_name}.py'					,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'					,)
					else:
									os.remove(f'{directory}/modeling_{lowercase_model_name}.py'       )
									os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py'       )

					if output_tensorflow:
									if not self._testing:
													remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py'       )

									shutil.move(
									    f'{directory}/modeling_tf_{lowercase_model_name}.py'					,f'{model_dir}/modeling_tf_{lowercase_model_name}.py'					,)

									shutil.move(
									    f'{directory}/test_modeling_tf_{lowercase_model_name}.py'					,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'					,)
					else:
									os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py'       )
									os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py'       )

					if output_flax:
									if not self._testing:
													remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py'       )

									shutil.move(
									    f'{directory}/modeling_flax_{lowercase_model_name}.py'					,f'{model_dir}/modeling_flax_{lowercase_model_name}.py'					,)

									shutil.move(
									    f'{directory}/test_modeling_flax_{lowercase_model_name}.py'					,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'					,)
					else:
									os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py'       )
									os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py'       )

					shutil.move(
					    f'{directory}/{lowercase_model_name}.md'					,f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'					,)

					shutil.move(
					    f'{directory}/tokenization_{lowercase_model_name}.py'					,f'{model_dir}/tokenization_{lowercase_model_name}.py'					,)

					shutil.move(
					    f'{directory}/tokenization_fast_{lowercase_model_name}.py'					,f'{model_dir}/tokenization_{lowercase_model_name}_fast.py'					,)

					from os import fdopen, remove
					from shutil import copymode, move
					from tempfile import mkstemp

					def replace(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       ):
									# Create temp file
									snake_case__    ,						snake_case__      :Optional[Any]         =   mkstemp()
									snake_case__      :Optional[Any]         =   False
									with fdopen(UpperCamelCase					,"w"       ) as new_file:
													with open(UpperCamelCase       ) as old_file:
																	for line in old_file:
																					new_file.write(UpperCamelCase       )
																					if line_to_copy_below in line:
																									snake_case__      :Optional[Any]         =   True
																									for line_to_copy in lines_to_copy:
																													new_file.write(UpperCamelCase       )

									if not line_found:
													raise ValueError(f'Line {line_to_copy_below} was not found in file.'       )

									# Copy the file permissions from the old file to the new file
									copymode(UpperCamelCase					,UpperCamelCase       )
									# Remove original file
									remove(UpperCamelCase       )
									# Move new file
									move(UpperCamelCase					,UpperCamelCase       )

					def skip_units(UpperCamelCase       ):
									return (
									    ("generating PyTorch" in line and not output_pytorch)
									    or ("generating TensorFlow" in line and not output_tensorflow)
									    or ("generating Flax" in line and not output_flax)
									)

					def replace_in_files(UpperCamelCase       ):
									with open(UpperCamelCase       ) as datafile:
													snake_case__      :int         =   []
													snake_case__      :Optional[int]         =   False
													snake_case__      :List[str]         =   False
													for line in datafile:
																	if "# To replace in: " in line and "##" not in line:
																					snake_case__      :Optional[Any]         =   line.split("\""       )[1]
																					snake_case__      :Tuple         =   skip_units(UpperCamelCase       )
																	elif "# Below: " in line and "##" not in line:
																					snake_case__      :Optional[Any]         =   line.split("\""       )[1]
																					snake_case__      :List[str]         =   skip_units(UpperCamelCase       )
																	elif "# End." in line and "##" not in line:
																					if not skip_file and not skip_snippet:
																									replace(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )

																					snake_case__      :Tuple         =   []
																	elif "# Replace with" in line and "##" not in line:
																					snake_case__      :Optional[Any]         =   []
																	elif "##" not in line:
																					lines_to_copy.append(UpperCamelCase       )

									remove(UpperCamelCase       )

					replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py'       )
					os.rmdir(UpperCamelCase       )