




'''simple docstring'''


import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
					from transformers import (
					    FlaxBertModel,
					    FlaxCLIPVisionModel,
					    FlaxVisionTextDualEncoderModel,
					    FlaxViTModel,
					    VisionTextDualEncoderConfig,
					    VisionTextDualEncoderProcessor,
					)
					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)


if is_torch_available():
					import torch

					from transformers import VisionTextDualEncoderModel

if is_vision_available():
					from PIL import Image




def 	_a       (				__lowerCAmelCase		:      List[str]     ):

		"""simple docstring"""

		if isinstance(__lowerCAmelCase   ,							collections.abc.Iterable     ):
				return x
		return (x, x)




@require_flax
class     a			:






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      np.ndarray			,			snake_case_					:      np.ndarray			,			snake_case_					:      float  ):



								'''simple docstring'''



								snake_case__						:       str       						=  np.abs((a - b)  ).max()
								self.assertLessEqual(snake_case_			,			snake_case_			,			F"""Difference between torch and flax is {diff} (>= {tol})."""  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple			,			snake_case_					:      Any			,			snake_case_					:      Tuple=None			,			**snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       str       						=  VisionTextDualEncoderConfig.from_vision_text_configs(snake_case_			,			snake_case_  )

								snake_case__						:       Any       						=  FlaxVisionTextDualEncoderModel(snake_case_  )

								snake_case__						:       Union[str, Any]       						=  model(input_ids=snake_case_			,			pixel_values=snake_case_			,			attention_mask=snake_case_  )

								self.assertEqual(output['''text_embeds'''].shape			,			(input_ids.shape[0], config.projection_dim)  )
								self.assertEqual(output['''image_embeds'''].shape			,			(pixel_values.shape[0], config.projection_dim)  )







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Dict			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple=None			,			**snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.get_vision_text_model(snake_case_			,			snake_case_  )
								snake_case__						:       Dict       						=  {'''vision_model''': vision_model, '''text_model''': text_model}
								snake_case__						:       Optional[int]       						=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case_  )

								snake_case__						:       Any       						=  model(input_ids=snake_case_			,			pixel_values=snake_case_			,			attention_mask=snake_case_  )

								self.assertEqual(output['''text_embeds'''].shape			,			(input_ids.shape[0], model.config.projection_dim)  )
								self.assertEqual(output['''image_embeds'''].shape			,			(pixel_values.shape[0], model.config.projection_dim)  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Any			,			snake_case_					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Any			,			snake_case_					:      int=None			,			**snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.get_vision_text_model(snake_case_			,			snake_case_  )
								snake_case__						:       Union[str, Any]       						=  {'''vision_model''': vision_model, '''text_model''': text_model}
								snake_case__						:       Tuple       						=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case_  )

								snake_case__						:       int       						=  model(input_ids=snake_case_			,			pixel_values=snake_case_			,			attention_mask=snake_case_  )
								snake_case__						:       str       						=  output[0]

								with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(snake_case_  )
										snake_case__						:       Any       						=  FlaxVisionTextDualEncoderModel.from_pretrained(snake_case_  )

										snake_case__						:       str       						=  model(input_ids=snake_case_			,			pixel_values=snake_case_			,			attention_mask=snake_case_  )
										snake_case__						:       Dict       						=  after_output[0]
										snake_case__						:       int       						=  np.amax(np.abs(out_a - out_a  )  )
										self.assertLessEqual(snake_case_			,			1e-3  )







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[Any]=None			,			**snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.get_vision_text_model(snake_case_			,			snake_case_  )
								snake_case__						:       Tuple       						=  {'''vision_model''': vision_model, '''text_model''': text_model}
								snake_case__						:       str       						=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case_  )

								snake_case__						:       Dict       						=  model(
								    input_ids=snake_case_			,			pixel_values=snake_case_			,			attention_mask=snake_case_			,			output_attentions=snake_case_  )

								snake_case__						:       Tuple       						=  output.vision_model_output.attentions
								self.assertEqual(len(snake_case_  )			,			vision_config.num_hidden_layers  )

								# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
								snake_case__						:       Optional[int]       						=  to_atuple(vision_model.config.image_size  )
								snake_case__						:       Union[str, Any]       						=  to_atuple(vision_model.config.patch_size  )
								snake_case__						:       Optional[Any]       						=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								snake_case__						:       List[Any]       						=  num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:]			,			(vision_config.num_attention_heads, seq_len, seq_len)  )

								snake_case__						:       str       						=  output.text_model_output.attentions
								self.assertEqual(len(snake_case_  )			,			text_config.num_hidden_layers  )

								self.assertEqual(
								    text_attentions[0].shape[-3:]			,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,			)







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								pt_model.to(snake_case_  )
								pt_model.eval()

								# prepare inputs
								snake_case__						:       Union[str, Any]       						=  inputs_dict
								snake_case__						:       Any       						=  {k: torch.tensor(v.tolist()  ) for k, v in flax_inputs.items()}

								with torch.no_grad():
										snake_case__						:       Dict       						=  pt_model(**snake_case_  ).to_tuple()

								snake_case__						:       str       						=  fx_model(**snake_case_  ).to_tuple()
								self.assertEqual(len(snake_case_  )			,			len(snake_case_  )			,			'''Output lengths differ between Flax and PyTorch'''  )
								for fx_output, pt_output in zip(fx_outputs[:4]			,			pt_outputs[:4]  ):
										self.assert_almost_equals(snake_case_			,			pt_output.numpy()			,			4e-2  )

								# PT -> Flax
								with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(snake_case_  )
										snake_case__						:       Union[str, Any]       						=  FlaxVisionTextDualEncoderModel.from_pretrained(snake_case_			,			from_pt=snake_case_  )

								snake_case__						:       Any       						=  fx_model_loaded(**snake_case_  ).to_tuple()
								self.assertEqual(len(snake_case_  )			,			len(snake_case_  )			,			'''Output lengths differ between Flax and PyTorch'''  )
								for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]			,			pt_outputs[:4]  ):
										self.assert_almost_equals(snake_case_			,			pt_output.numpy()			,			4e-2  )

								# Flax -> PT
								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(snake_case_  )
										snake_case__						:       str       						=  VisionTextDualEncoderModel.from_pretrained(snake_case_			,			from_flax=snake_case_  )

								pt_model_loaded.to(snake_case_  )
								pt_model_loaded.eval()

								with torch.no_grad():
										snake_case__						:       Optional[int]       						=  pt_model_loaded(**snake_case_  ).to_tuple()

								self.assertEqual(len(snake_case_  )			,			len(snake_case_  )			,			'''Output lengths differ between Flax and PyTorch'''  )
								for fx_output, pt_output_loaded in zip(fx_outputs[:4]			,			pt_outputs_loaded[:4]  ):
										self.assert_almost_equals(snake_case_			,			pt_output_loaded.numpy()			,			4e-2  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      Dict			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  VisionTextDualEncoderConfig.from_vision_text_configs(snake_case_			,			snake_case_  )

								snake_case__						:       List[str]       						=  VisionTextDualEncoderModel(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  FlaxVisionTextDualEncoderModel(snake_case_  )

								snake_case__						:       Union[str, Any]       						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,			snake_case_  )
								snake_case__						:       Optional[int]       						=  fx_state

								self.check_pt_flax_equivalence(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Dict			,			snake_case_					:      Tuple			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  VisionTextDualEncoderConfig.from_vision_text_configs(snake_case_			,			snake_case_  )

								snake_case__						:       Dict       						=  VisionTextDualEncoderModel(snake_case_  )
								snake_case__						:       Dict       						=  FlaxVisionTextDualEncoderModel(snake_case_  )

								snake_case__						:       List[Any]       						=  load_flax_weights_in_pytorch_model(snake_case_			,			fx_model.params  )

								self.check_pt_flax_equivalence(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**snake_case_  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.prepare_config_and_inputs()
								self.check_save_load(**snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**snake_case_  )







						@is_pt_flax_cross_test
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.prepare_config_and_inputs()
								snake_case__						:       Any       						=  config_inputs_dict.pop('''vision_config'''  )
								snake_case__						:       Tuple       						=  config_inputs_dict.pop('''text_config'''  )

								snake_case__						:       Union[str, Any]       						=  config_inputs_dict

								self.check_equivalence_pt_to_flax(snake_case_			,			snake_case_			,			snake_case_  )
								self.check_equivalence_flax_to_pt(snake_case_			,			snake_case_			,			snake_case_  )







						@slow
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.get_pretrained_model_and_inputs()

								snake_case__						:       List[Any]       						=  model_a(**snake_case_  )
								snake_case__						:       Dict       						=  outputs[0]

								with tempfile.TemporaryDirectory() as tmp_dirname:
										model_a.save_pretrained(snake_case_  )
										snake_case__						:       Optional[Any]       						=  FlaxVisionTextDualEncoderModel.from_pretrained(snake_case_  )

										snake_case__						:       Dict       						=  model_a(**snake_case_  )
										snake_case__						:       Optional[Any]       						=  after_outputs[0]
										snake_case__						:       Optional[int]       						=  np.amax(np.abs(out_a - out_a  )  )
										self.assertLessEqual(snake_case_			,			1e-5  )





@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    '''hf-internal-testing/tiny-random-vit'''			,			'''hf-internal-testing/tiny-bert'''			,			vision_from_pt=snake_case_			,			text_from_pt=snake_case_			,			)
								snake_case__						:       List[Any]       						=  1_3
								snake_case__						:       List[Any]       						=  floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]  )
								snake_case__						:       str       						=  ids_tensor([batch_size, 4]			,			model.config.text_config.vocab_size  )
								snake_case__						:       Optional[Any]       						=  random_attention_mask([batch_size, 4]  )
								snake_case__						:       Optional[int]       						=  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

								return model, inputs







						def       __magic_name__							(							self					:      int			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  FlaxViTModel(snake_case_  )
								snake_case__						:       Any       						=  FlaxBertModel(snake_case_  )
								return vision_model, text_model







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  FlaxViTModelTester(self  )
								snake_case__						:       Dict       						=  FlaxBertModelTester(self  )
								snake_case__						:       List[Any]       						=  vit_model_tester.prepare_config_and_inputs()
								snake_case__						:       Optional[Any]       						=  bert_model_tester.prepare_config_and_inputs()

								snake_case__						:       str       						=  vision_config_and_inputs

								snake_case__						:       str       						=  text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       str       						=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    '''hf-internal-testing/tiny-random-clip'''			,			'''hf-internal-testing/tiny-bert'''			,			vision_from_pt=snake_case_			,			text_from_pt=snake_case_			,			)
								snake_case__						:       str       						=  1_3
								snake_case__						:       Any       						=  floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]  )
								snake_case__						:       Any       						=  ids_tensor([batch_size, 4]			,			model.config.text_config.vocab_size  )
								snake_case__						:       str       						=  random_attention_mask([batch_size, 4]  )
								snake_case__						:       Optional[Any]       						=  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

								return model, inputs







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      str			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  FlaxCLIPVisionModel(snake_case_  )
								snake_case__						:       Dict       						=  FlaxBertModel(snake_case_  )
								return vision_model, text_model







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  FlaxCLIPVisionModelTester(self  )
								snake_case__						:       str       						=  FlaxBertModelTester(self  )
								snake_case__						:       Optional[Any]       						=  clip_model_tester.prepare_config_and_inputs()
								snake_case__						:       Any       						=  bert_model_tester.prepare_config_and_inputs()

								snake_case__						:       Union[str, Any]       						=  vision_config_and_inputs

								snake_case__						:       Optional[int]       						=  text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}





@require_flax
@require_vision
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''			,			logit_scale_init_value=1.0  )
								snake_case__						:       List[Any]       						=  VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''  )

								snake_case__						:       Union[str, Any]       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								snake_case__						:       List[Any]       						=  processor(
								    text=['''una foto di un gatto''', '''una foto di un cane''']			,			images=snake_case_			,			padding=snake_case_			,			return_tensors='''np'''  )

								snake_case__						:       Dict       						=  model(**snake_case_  )

								# verify the logits
								self.assertEqual(outputs.logits_per_image.shape			,			(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])  )
								self.assertEqual(
								    outputs.logits_per_text.shape			,			(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,			)

								snake_case__						:       Optional[Any]       						=  np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]  )

								self.assertTrue(np.allclose(outputs.logits_per_image			,			snake_case_			,			atol=1e-3  )  )






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

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

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

					main(args)



