from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE						:    Any     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:    Any     = [
				    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'FlaubertForMultipleChoice',
				    'FlaubertForQuestionAnswering',
				    'FlaubertForQuestionAnsweringSimple',
				    'FlaubertForSequenceClassification',
				    'FlaubertForTokenClassification',
				    'FlaubertModel',
				    'FlaubertWithLMHeadModel',
				    'FlaubertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:    int     = [
				    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFFlaubertForMultipleChoice',
				    'TFFlaubertForQuestionAnsweringSimple',
				    'TFFlaubertForSequenceClassification',
				    'TFFlaubertForTokenClassification',
				    'TFFlaubertModel',
				    'TFFlaubertPreTrainedModel',
				    'TFFlaubertWithLMHeadModel',
				]


if TYPE_CHECKING:
				from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
				from .tokenization_flaubert import FlaubertTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flaubert import (
								    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaubertForMultipleChoice,
								    FlaubertForQuestionAnswering,
								    FlaubertForQuestionAnsweringSimple,
								    FlaubertForSequenceClassification,
								    FlaubertForTokenClassification,
								    FlaubertModel,
								    FlaubertPreTrainedModel,
								    FlaubertWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_flaubert import (
								    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFFlaubertForMultipleChoice,
								    TFFlaubertForQuestionAnsweringSimple,
								    TFFlaubertForSequenceClassification,
								    TFFlaubertForTokenClassification,
								    TFFlaubertModel,
								    TFFlaubertPreTrainedModel,
								    TFFlaubertWithLMHeadModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:    int     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

						_snake_case						:     Dict				=    0  # Number of remaining (not pruned) params in the encoder
						_snake_case						:     int				=    0  # Number of params in the encoder

						print("name".ljust(60					,			" "						)					,			"Remaining Weights %"					,			"Remaining Weight"						)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								_snake_case						:     Union[str, Any]				=    TopKBinarizer.apply(__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_snake_case						:     str				=    ThresholdBinarizer.apply(__lowercase					,			__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "l0":
																								_snake_case				,_snake_case						:     List[str]				=    -0.1, 1.1
																								_snake_case						:     Any				=    torch.sigmoid(__lowercase						)
																								_snake_case						:     str				=    s * (r - l) + l
																								_snake_case						:     Dict				=    s_bar.clamp(min=0.0					,			max=1.0						)
																								_snake_case						:     Tuple				=    (mask > 0.0).sum().item()
																		else:
																								raise ValueError("Unknown pruning method"						)
																		remaining_count += mask_ones
																		print(name.ljust(60					,			" "						)					,			str(round(100 * mask_ones / param.numel()					,			3						)						).ljust(20					,			" "						)					,			str(__lowercase						)						)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""						)
						print("Remaining Weights (global) %: "					,			100 * remaining_count / encoder_count						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    str     = argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
				        ' pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `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(
				    '--serialization_dir',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)