






'''simple docstring'''







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A	:			Tuple         =logging.get_logger(__name__)

_A	:			Any         ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A	:			Optional[int]         ={
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

_A	:			Dict         ={
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       PRETRAINED_VOCAB_FILES_MAP
							a				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       ["""input_ids""", """attention_mask"""]
							a				      =       GPTaTokenizer




							def __init__(							self:				Any			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				Tuple=None			,							UpperCamelCase__:				Optional[int]=None			,							UpperCamelCase__:				Optional[Any]="<|endoftext|>"			,							UpperCamelCase__:				Tuple="<|endoftext|>"			,							UpperCamelCase__:				int="<|endoftext|>"			,							UpperCamelCase__:				str=False			,							**UpperCamelCase__:				Any			,							):
														super().__init__(
														    UpperCamelCase__			,							UpperCamelCase__			,							tokenizer_file=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							bos_token=UpperCamelCase__			,							eos_token=UpperCamelCase__			,							add_prefix_space=UpperCamelCase__			,							**UpperCamelCase__			,							)

														lowerCamelCase__					:	Union[str, Any]         =       kwargs.pop("""add_bos_token"""			,							UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
														if pre_tok_state.get("""add_prefix_space"""			,							UpperCamelCase__     ) != add_prefix_space:
																					lowerCamelCase__					:	Optional[Any]         =       getattr(UpperCamelCase__			,							pre_tok_state.pop("""type"""     )     )
																					lowerCamelCase__					:	Optional[int]         =       add_prefix_space
																					lowerCamelCase__					:	Dict         =       pre_tok_class(**UpperCamelCase__     )

														lowerCamelCase__					:	Union[str, Any]         =       add_prefix_space




							def 		lowerCamelCase_				(							self:				str			,							*UpperCamelCase__:				str			,							**UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	List[str]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )
														assert self.add_prefix_space or not is_split_into_words, (
														    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
														    "to use it with pretokenized inputs."
														)

														return super()._batch_encode_plus(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							*UpperCamelCase__:				List[str]			,							**UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )

														assert self.add_prefix_space or not is_split_into_words, (
														    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
														    "to use it with pretokenized inputs."
														)

														return super()._encode_plus(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														lowerCamelCase__					:	Tuple         =       self._tokenizer.model.save(UpperCamelCase__			,							name=UpperCamelCase__     )
														return tuple(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				"Conversation"     ):
														lowerCamelCase__					:	Tuple         =       []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     ) + [self.eos_token_id]     )

														if len(UpperCamelCase__     ) > self.model_max_length:
																					lowerCamelCase__					:	Optional[Any]         =       input_ids[-self.model_max_length :]
														return input_ids




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



