
"""simple docstring"""




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				Tuple:
       lowercase    :				Optional[Any]       		=							[]
       embed.append(
           (
               f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
               f"""stage{idx}.patch_embed.proj.weight""",
           )  )
       embed.append(
           (
               f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
               f"""stage{idx}.patch_embed.proj.bias""",
           )  )
       embed.append(
           (
               f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
               f"""stage{idx}.patch_embed.norm.weight""",
           )  )
       embed.append(
           (
               f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
               f"""stage{idx}.patch_embed.norm.bias""",
           )  )
       return embed


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       List[str]  ) ->				Optional[int]:
       lowercase    :				Optional[Any]       		=							[]
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
               f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
               f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
           )  )
       attention_weights.append(
           (
               f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
               f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
           )  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")  )
       attention_weights.append(
           (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")  )
       return attention_weights


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Optional[int]:
       lowercase    :				int       		=							[]
       token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")  )
       return token


def   __magic_name__	(      ) ->				List[str]:
       lowercase    :				int       		=							[]
       head.append(("layernorm.weight", "norm.weight")  )
       head.append(("layernorm.bias", "norm.bias")  )
       head.append(("classifier.weight", "head.weight")  )
       head.append(("classifier.bias", "head.bias")  )
       return head


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Tuple     , __snake_case				:       List[Any]     , __snake_case				:       Dict  ) ->				Optional[Any]:
       lowercase    :				Optional[Any]       		=							"imagenet-1k-id2label.json"
       lowercase    :				Optional[Any]       		=							1000

       lowercase    :				Optional[Any]       		=							"huggingface/label-files"
       lowercase    :				str       		=							num_labels
       lowercase    :				Optional[Any]       		=							json.load(open(cached_download(hf_hub_url(__lowercase     , __lowercase     , repo_type="dataset"  )  )     , "r"  )  )
       lowercase    :				List[Any]       		=							{int(__lowercase  ): v for k, v in idalabel.items()}

       lowercase    :				Optional[Any]       		=							idalabel
       lowercase    :				List[str]       		=							{v: k for k, v in idalabel.items()}

       lowercase    :				List[str]       		=							CvtConfig(num_labels=__lowercase     , idalabel=__lowercase     , labelaid=__lowercase  )

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit("/"     , 1  )[-1][4:6] == "13":
              lowercase    :				Any       		=							[1, 2, 10]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit("/"     , 1  )[-1][4:6] == "21":
              lowercase    :				str       		=							[1, 4, 16]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase    :				Dict       		=							[2, 2, 20]
              lowercase    :				Optional[Any]       		=							[3, 12, 16]
              lowercase    :				Dict       		=							[192, 768, 1024]

       lowercase    :				Dict       		=							CvtForImageClassification(__lowercase  )
       lowercase    :				Any       		=							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  )
       lowercase    :				Any       		=							image_size
       lowercase    :				int       		=							torch.load(__lowercase     , map_location=torch.device("cpu"  )  )

       lowercase    :				Optional[Any]       		=							OrderedDict()
       lowercase    :				int       		=							[]

       for idx in range(len(config.depth  )  ):
              if config.cls_token[idx]:
                     lowercase    :				Optional[int]       		=							list_of_state_dict + cls_token(__lowercase  )
              lowercase    :				Union[str, Any]       		=							list_of_state_dict + embeddings(__lowercase  )
              for cnt in range(config.depth[idx]  ):
                     lowercase    :				int       		=							list_of_state_dict + attention(__lowercase     , __lowercase  )

       lowercase    :				str       		=							list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(__lowercase  )
       for i in range(len(__lowercase  )  ):
              lowercase    :				Optional[int]       		=							original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(__lowercase  )
       model.save_pretrained(__lowercase  )
       image_processor.save_pretrained(__lowercase  )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
   _A			:       int   		=       argparse.ArgumentParser()
   parser.add_argument(
       """--cvt_model""",
       default="""cvt-w24""",
       type=str,
       help="""Name of the cvt model you\'d like to convert.""",
   )
   parser.add_argument(
       """--image_size""",
       default=3_84,
       type=int,
       help="""Input Image Size""",
   )
   parser.add_argument(
       """--cvt_file_name""",
       default=r"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
       type=str,
       help="""Input Image Size""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   _A			:       int   		=       parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




import argparse
import os
import shutil

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






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

			_snake_case		=			args.model_name_or_path.rstrip('/' )
			_snake_case		=			args.target_model_path

			print(f'''Load fine-pruned model from {model_name_or_path}''' )
			_snake_case		=			torch.load(os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			_snake_case		=			{}

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

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

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

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


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			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)