

'''simple docstring'''

from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_						   =			{
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": (
            "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase_						   =			{
    "openai/clip-vit-base-patch32": 7_7,
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   VOCAB_FILES_NAMES
				snake_case_           =   PRETRAINED_VOCAB_FILES_MAP
				snake_case_           =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_           =   ['''input_ids''', '''attention_mask''']
				snake_case_           =   CLIPTokenizer

				def __init__(					self,	lowercase_=None,	lowercase_=None,	lowercase_=None,	lowercase_="<|endoftext|>",	lowercase_="<|startoftext|>",	lowercase_="<|endoftext|>",	lowercase_="<|endoftext|>",	**lowercase_,	)						->       Union[str, Any]:
					super().__init__(
					    lowercase_,	lowercase_,	tokenizer_file=lowercase_,	unk_token=lowercase_,	bos_token=lowercase_,	eos_token=lowercase_,	pad_token=lowercase_,	**lowercase_,	)

					if not isinstance(self.backend_tokenizer.pre_tokenizer,	pre_tokenizers.Sequence   ):
						raise ValueError(
						    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
						    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
						    ' to be compatible with this version.The easiest way to do so is'
						    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
						    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
						    ' transformers.'   )

					self._wrap_decode_method_backend_tokenizer()

				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				self.backend_tokenizer.decode

					def new_decode_method(*lowercase_,	**lowercase_   ):
						snake_case							 =				orig_decode_method(*lowercase_,	**lowercase_   )
						snake_case							 =				text.replace(self.backend_tokenizer.model.end_of_word_suffix,	' '   ).strip()
						return text

					snake_case							 =				new_decode_method

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None   )						->       List[int]:
					snake_case							 =				[self.bos_token_id]
					snake_case							 =				[self.eos_token_id]

					if token_ids_a is None:
						return bos_token + token_ids_a + eos_token
					return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None   )						->       List[int]:
					snake_case							 =				[self.bos_token_id]
					snake_case							 =				[self.eos_token_id]

					if token_ids_a is None:
						return len(bos_token + token_ids_a + eos_token   ) * [0]
					return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None   )						->       Tuple[str]:
					snake_case							 =				self._tokenizer.model.save(lowercase_,	name=lowercase_   )
					return tuple(lowercase_   )





'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def     __magic_name__       ( A							)			->		Dict:
	snake_case							 =				args.serialization_dir
	snake_case							 =				args.pruning_method
	snake_case							 =				args.threshold

	snake_case							 =				torch.load(os.path.join(A					,   'pytorch_model.bin'							)					,   map_location='cpu'							)

	snake_case							 =				0  # Number of remaining (not pruned) params in the encoder
	snake_case							 =				0  # Number of params in the encoder

	print('name'.ljust(6_0					,   ' '							)					,   '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							 =				TopKBinarizer.apply(A					,   A							).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							 =				ThresholdBinarizer.apply(A					,   A					,   A							).sum().item()
			elif pruning_method == "l0":
				snake_case , snake_case							 =				-0.1, 1.1
				snake_case							 =				torch.sigmoid(A							)
				snake_case							 =				s * (r - l) + l
				snake_case							 =				s_bar.clamp(min=0.0					,   max=1.0							)
				snake_case							 =				(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'							)
			remaining_count += mask_ones
			print(name.ljust(6_0					,   ' '							)					,   str(round(1_0_0 * mask_ones / param.numel()					,   3							)							).ljust(2_0					,   ' '							)					,   str(A							)							)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''							)
	print('Remaining Weights (global) %: '					,   1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
						lowerCAmelCase_						   =			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",
						)

						lowerCAmelCase_						   =			parser.parse_args()

						main(args)





