

from typing import Dict, List, Optional

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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)


_lowerCAmelCase					:			str  				=			{
    "nielsr/canine-s": 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCAmelCase					:			Dict  				=			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
_lowerCAmelCase					:			Union[str, Any]  				=			0

_lowerCAmelCase					:			List[str]  				=			0xE_0_0_0
_lowerCAmelCase					:			str  				=			0xE_0_0_1
_lowerCAmelCase					:			Union[str, Any]  				=			0xE_0_0_2
_lowerCAmelCase					:			List[str]  				=			0xE_0_0_3
_lowerCAmelCase					:			Optional[Any]  				=			0xE_0_0_4

# Maps special codepoints to human-readable names.
_lowerCAmelCase					:			Dict[int, str]  				=			{
    # 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.
_lowerCAmelCase					:			Dict[str, int]  				=			{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(		self					,  __snake_case=chr(__snake_case					)					,  __snake_case=chr(__snake_case					)					,  __snake_case=chr(__snake_case					)					,  __snake_case=chr(__snake_case					)					,  __snake_case=chr(__snake_case					)					,  __snake_case=chr(__snake_case					)					,  __snake_case=False					,  __snake_case=2048					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						__a        =AddedToken(__snake_case					,  lstrip=__snake_case					,  rstrip=__snake_case					) if isinstance(__snake_case					,  __snake_case					) else bos_token
						__a        =AddedToken(__snake_case					,  lstrip=__snake_case					,  rstrip=__snake_case					) if isinstance(__snake_case					,  __snake_case					) else eos_token
						__a        =AddedToken(__snake_case					,  lstrip=__snake_case					,  rstrip=__snake_case					) if isinstance(__snake_case					,  __snake_case					) else sep_token
						__a        =AddedToken(__snake_case					,  lstrip=__snake_case					,  rstrip=__snake_case					) if isinstance(__snake_case					,  __snake_case					) else cls_token
						__a        =AddedToken(__snake_case					,  lstrip=__snake_case					,  rstrip=__snake_case					) if isinstance(__snake_case					,  __snake_case					) else pad_token

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

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

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

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

						__a        =UNICODE_VOCAB_SIZE
						__a        =len(self._special_codepoints					)





			@property
			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						return self._unicode_vocab_size





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						return list(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						try:
									return ord(__snake_case					)
						except TypeError:
									raise ValueError(f'invalid token: \'{token}\''					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> str:




						'''simple docstring'''
						try:
									if index in SPECIAL_CODEPOINTS:
												return SPECIAL_CODEPOINTS[index]
									return chr(__snake_case					)
						except TypeError:
									raise ValueError(f'invalid id: {index}'					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						return "".join(__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					)   -> List[int]:




						'''simple docstring'''
						__a        =[self.sep_token_id]
						__a        =[self.cls_token_id]

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





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					,  __snake_case = False					)   -> List[int]:




						'''simple docstring'''
						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__snake_case					,  token_ids_a=__snake_case					,  already_has_special_tokens=__snake_case					)

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





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					)   -> List[int]:




						'''simple docstring'''
						__a        =[self.sep_token_id]
						__a        =[self.cls_token_id]

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





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					)   -> Dict:




						'''simple docstring'''
						return ()









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     max_position_embeddings=args.max_target_positions       ,     num_layers=args.decoder_layers       ,     attention_heads=args.decoder_attention_heads       ,     ffn_dim=args.decoder_ffn_embed_dim       ,     d_model=args.decoder_embed_dim       ,     layerdrop=args.decoder_layerdrop       ,     dropout=args.dropout       ,     attention_dropout=args.attention_dropout       ,     activation_dropout=args.activation_dropout       ,     activation_function='gelu'       ,     scale_embedding=not args.no_scale_embedding       ,     tie_word_embeddings=args.share_decoder_input_output_embed       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






