
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


lowerCamelCase__	=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__	=							{
    "google/electra-small-generator": 512,
    "google/electra-base-generator": 512,
    "google/electra-large-generator": 512,
    "google/electra-small-discriminator": 512,
    "google/electra-base-discriminator": 512,
    "google/electra-large-discriminator": 512,
}

lowerCamelCase__	=							{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Any	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: List[Any]	=		PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_							: int	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Any	=		ElectraTokenizer

						def __init__(			self       ,							a=None       ,							a=None       ,							a=True       ,							a="[UNK]"       ,							a="[SEP]"       ,							a="[PAD]"       ,							a="[CLS]"       ,							a="[MASK]"       ,							a=True       ,							a=None       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								super().__init__(
								    a       ,							tokenizer_file=a       ,							do_lower_case=a       ,							unk_token=a       ,							sep_token=a       ,							pad_token=a       ,							cls_token=a       ,							mask_token=a       ,							tokenize_chinese_chars=a       ,							strip_accents=a       ,							**a       ,							)

								_UpperCamelCase						    =	json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase"""       ,							a				) != do_lower_case
								    or normalizer_state.get("""strip_accents"""       ,							a				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""       ,							a				) != tokenize_chinese_chars
								):
										_UpperCamelCase						    =	getattr(a       ,							normalizer_state.pop("""type"""				)				)
										_UpperCamelCase						    =	do_lower_case
										_UpperCamelCase						    =	strip_accents
										_UpperCamelCase						    =	tokenize_chinese_chars
										_UpperCamelCase						    =	normalizer_class(**a				)

								_UpperCamelCase						    =	do_lower_case

						def  A_				(			self       ,							a       ,							a=None				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
										output += token_ids_a + [self.sep_token_id]

								return output

						def  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[self.cls_token_id]
								if token_ids_a is None:
										return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







						def  A_				(			self       ,							a       ,							a = None				)					->      Tuple[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._tokenizer.model.save(a       ,							name=a				)
								return tuple(a				)





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  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						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							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__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



