




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						_A     =			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''')
						_A     =			parser.parse_args()

						if args.model_type == "roberta":
												_A     =			RobertaForMaskedLM.from_pretrained(args.model_name)
												_A     =			'''roberta'''
						elif args.model_type == "gpt2":
												_A     =			GPTaLMHeadModel.from_pretrained(args.model_name)
												_A     =			'''transformer'''

						_A     =			model.state_dict()
						_A     =			{}

						# Embeddings #
						if args.model_type == "gpt2":
												for param_name in ["wte.weight", "wpe.weight"]:
																		_A     =			state_dict[f"{prefix}.{param_name}"]
						else:
												for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																		_A     =			f"{prefix}.embeddings.{w}.weight"
																		_A     =			state_dict[param_name]
												for w in ["weight", "bias"]:
																		_A     =			f"{prefix}.embeddings.LayerNorm.{w}"
																		_A     =			state_dict[param_name]

    # Transformer Blocks #
						_A     =			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"]:
																														_A     =			state_dict[
																														    f"{prefix}.h.{teacher_idx}.{layer}.{w}"
																														]
																		_A     =			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"]:
																														_A     =			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"]:
																		_A     =			state_dict[f"{layer}"]
												if args.vocab_transform:
																		for w in ["weight", "bias"]:
																								_A     =			state_dict[f"lm_head.dense.{w}"]
																								_A     =			state_dict[f"lm_head.layer_norm.{w}"]
						elif args.model_type == "gpt2":
												for w in ["weight", "bias"]:
																		_A     =			state_dict[f"{prefix}.ln_f.{w}"]
												_A     =			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)










from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A     =			logging.get_logger(__name__)


_A     =			{
    '''nielsr/canine-s''': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
_A     =			1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_A     =			0

_A     =			0xe0_00
_A     =			0xe0_01
_A     =			0xe0_02
_A     =			0xe0_03
_A     =			0xe0_04

# Maps special codepoints to human-readable names.
_A     =			{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_A     =			{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



		def __init__(     self,	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=False,	UpperCamelCase__=2048,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else bos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else eos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else sep_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else cls_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else mask_token

								super().__init__(
								    bos_token=UpperCamelCase__,	eos_token=UpperCamelCase__,	sep_token=UpperCamelCase__,	cls_token=UpperCamelCase__,	pad_token=UpperCamelCase__,	mask_token=UpperCamelCase__,	add_prefix_space=UpperCamelCase__,	model_max_length=UpperCamelCase__,	**UpperCamelCase__,	)

								# Creates a mapping for looking up the IDs of special symbols.
								lowerCAmelCase_      			=  {}
								for codepoint, name in SPECIAL_CODEPOINTS.items():
														lowerCAmelCase_      			=  codepoint

								# Creates a mapping for looking up the string forms of special symbol IDs.
								lowerCAmelCase_      			=  {
								    codepoint: name for name, codepoint in self._special_codepoints.items()
								}

								lowerCAmelCase_      			=  UNICODE_VOCAB_SIZE
								lowerCAmelCase_      			=  len(self._special_codepoints   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._unicode_vocab_size



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return list(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														return ord(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid token: '{token}'"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														if index in SPECIAL_CODEPOINTS:
																				return SPECIAL_CODEPOINTS[index]
														return chr(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid id: {index}"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return "".join(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  cls + token_ids_a + sep
								if token_ids_a is not None:
														result += token_ids_a + sep
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = False   ):







								"""simple docstring"""



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__,	token_ids_a=UpperCamelCase__,	already_has_special_tokens=UpperCamelCase__   )

								lowerCAmelCase_      			=  [1] + ([0] * len(UpperCamelCase__   )) + [1]
								if token_ids_a is not None:
														result += ([0] * len(UpperCamelCase__   )) + [1]
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  len(cls + token_ids_a + sep   ) * [0]
								if token_ids_a is not None:
														result += len(token_ids_a + sep   ) * [1]
								return result







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								return ()




