






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		__magic_name__:					Optional[Any]							=     argparse.ArgumentParser(
		    description=(
		        "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
		        " Distillation"
		    )
		)
		parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
		parser.add_argument("--model_name", default="roberta-large", type=str)
		parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
		parser.add_argument("--vocab_transform", action="store_true")
		__magic_name__:					Optional[Any]							=     parser.parse_args()

		if args.model_type == "roberta":
				__magic_name__:					int							=     RobertaForMaskedLM.from_pretrained(args.model_name)
				__magic_name__:					List[Any]							=     "roberta"
		elif args.model_type == "gpt2":
				__magic_name__:					List[Any]							=     GPTaLMHeadModel.from_pretrained(args.model_name)
				__magic_name__:					List[Any]							=     "transformer"

		__magic_name__:					Dict							=     model.state_dict()
		__magic_name__:					List[Any]							=     {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						__magic_name__:					int							=     state_dict[F"""{prefix}.{param_name}"""]
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						__magic_name__:					Optional[int]							=     F"""{prefix}.embeddings.{w}.weight"""
						__magic_name__:					Optional[Any]							=     state_dict[param_name]
				for w in ["weight", "bias"]:
						__magic_name__:					Dict							=     F"""{prefix}.embeddings.LayerNorm.{w}"""
						__magic_name__:					Dict							=     state_dict[param_name]

    # Transformer Blocks #
		__magic_name__:					Tuple							=     0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				if args.model_type == "gpt2":
						for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
								for w in ["weight", "bias"]:
										__magic_name__:					Dict							=     state_dict[
										    F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
										]
						__magic_name__:					List[Any]							=     state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
				else:
						for layer in [
						    "attention.self.query",
						    "attention.self.key",
						    "attention.self.value",
						    "attention.output.dense",
						    "attention.output.LayerNorm",
						    "intermediate.dense",
						    "output.dense",
						    "output.LayerNorm",
						]:
								for w in ["weight", "bias"]:
										__magic_name__:					str							=     state_dict[
										    F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
										]
				std_idx += 1

		# Language Modeling Head ###s
		if args.model_type == "roberta":
				for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
						__magic_name__:					int							=     state_dict[F"""{layer}"""]
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								__magic_name__:					List[str]							=     state_dict[F"""lm_head.dense.{w}"""]
								__magic_name__:					Optional[Any]							=     state_dict[F"""lm_head.layer_norm.{w}"""]
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						__magic_name__:					Union[str, Any]							=     state_dict[F"""{prefix}.ln_f.{w}"""]
				__magic_name__:					List[Any]							=     state_dict["lm_head.weight"]

		print(F"""N layers selected for distillation: {std_idx}""")
		print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

		print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
		torch.save(compressed_sd, args.dump_checkpoint)








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlnet import XLNetTokenizer
else:
		__magic_name__:					Any							=     None


__magic_name__:					Dict							=     logging.get_logger(__name__)

__magic_name__:					Any							=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__magic_name__:					str							=     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

__magic_name__:					Optional[Any]							=     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

__magic_name__:					Optional[Any]							=     "▁"

# Segments (not really needed)
__magic_name__:					List[Any]							=     0
__magic_name__:					Dict							=     1
__magic_name__:					List[str]							=     2
__magic_name__:					List[Any]							=     3
__magic_name__:					Optional[int]							=     4





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Dict											=   VOCAB_FILES_NAMES
	lowercase__						:					Union[str, Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Tuple											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					List[str]											=   '''left'''
	lowercase__						:					List[str]											=   XLNetTokenizer
	def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<sep>"      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__="<cls>"      ,       lowerCAmelCase__="<mask>"      ,       lowerCAmelCase__=["<eop>", "<eod>"]      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							# Mask token behave like a normal word, i.e. include the space before it
							__magic_name__      :       Optional[int]       						= AddedToken(lowerCAmelCase__      ,       lstrip=lowerCAmelCase__      ,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			) else mask_token

							super().__init__(
							    vocab_file=lowerCAmelCase__      ,       tokenizer_file=lowerCAmelCase__      ,       do_lower_case=lowerCAmelCase__      ,       remove_space=lowerCAmelCase__      ,       keep_accents=lowerCAmelCase__      ,       bos_token=lowerCAmelCase__      ,       eos_token=lowerCAmelCase__      ,       unk_token=lowerCAmelCase__      ,       sep_token=lowerCAmelCase__      ,       pad_token=lowerCAmelCase__      ,       cls_token=lowerCAmelCase__      ,       mask_token=lowerCAmelCase__      ,       additional_special_tokens=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )

							__magic_name__      :       List[str]       						= 3
							__magic_name__      :       str       						= do_lower_case
							__magic_name__      :       Union[str, Any]       						= remove_space
							__magic_name__      :       str       						= keep_accents
							__magic_name__      :       Tuple       						= vocab_file
							__magic_name__      :       List[Any]       						= False if not self.vocab_file else True
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       Any       						= [self.sep_token_id]
							__magic_name__      :       Any       						= [self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       List[str]       						= [self.sep_token_id]
							__magic_name__      :       Optional[Any]       						= [2]

							if token_ids_a is None:
													return len(token_ids_a + sep			) * [0] + cls_segment_id
							return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""			)

							if not os.path.isdir(lowerCAmelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							__magic_name__      :       List[str]       						= os.path.join(
							    lowerCAmelCase__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

							if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			):
													copyfile(self.vocab_file      ,       lowerCAmelCase__			)

							return (out_vocab_file,)
