


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

						__SCREAMING_SNAKE_CASE						:     int					=		(
						    ('''layer.''', '''layer_'''),
						    ('''word_embeddings.weight''', '''word_embeddings'''),
						    ('''position_embeddings.weight''', '''position_embeddings'''),
						    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
						    ('''.''', '''/'''),
						    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
						    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
						    ('''weight''', '''kernel'''),
						)

						if not os.path.isdir(lowercase__      ):
												os.makedirs(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		model.state_dict()

						def to_tf_var_name(lowercase__      ):
												for patt, repl in iter(lowercase__      ):
																		__SCREAMING_SNAKE_CASE						:     Any					=		name.replace(lowercase__    ,       lowercase__      )
												return F'''bert/{name}'''

						def create_tf_var(lowercase__    ,       lowercase__    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tf.dtypes.as_dtype(tensor.dtype      )
												__SCREAMING_SNAKE_CASE						:     int					=		tf.get_variable(dtype=lowercase__    ,       shape=tensor.shape    ,       name=lowercase__    ,       initializer=tf.zeros_initializer()      )
												session.run(tf.variables_initializer([tf_var]      )      )
												session.run(lowercase__      )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		to_tf_var_name(lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose      ):
																								__SCREAMING_SNAKE_CASE						:     Dict					=		torch_tensor.T
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		create_tf_var(tensor=lowercase__    ,       name=lowercase__    ,       session=lowercase__      )
																		tf.keras.backend.set_value(lowercase__    ,       lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		session.run(lowercase__      )
																		print(F'''Successfully created {tf_name}: {np.allclose(lowercase__    ,       lowercase__      )}'''      )

												__SCREAMING_SNAKE_CASE						:     Dict					=		tf.train.Saver(tf.trainable_variables()      )
												saver.save(lowercase__    ,       os.path.join(lowercase__    ,       model_name.replace('''-'''    ,       '''_'''      ) + '''.ckpt'''      )      )


def        _UpperCamelCase							(      lowercase__=None      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		argparse.ArgumentParser()
						parser.add_argument('''--model_name'''    ,       type=lowercase__    ,       required=lowercase__    ,       help='''model name e.g. bert-base-uncased'''      )
						parser.add_argument(
						    '''--cache_dir'''    ,       type=lowercase__    ,       default=lowercase__    ,       required=lowercase__    ,       help='''Directory containing pytorch model'''      )
						parser.add_argument('''--pytorch_model_path'''    ,       type=lowercase__    ,       required=lowercase__    ,       help='''/path/to/<pytorch-model-name>.bin'''      )
						parser.add_argument('''--tf_cache_dir'''    ,       type=lowercase__    ,       required=lowercase__    ,       help='''Directory in which to save tensorflow model'''      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		parser.parse_args(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name    ,       state_dict=torch.load(args.pytorch_model_path      )    ,       cache_dir=args.cache_dir    ,       )

						convert_pytorch_checkpoint_to_tf(model=lowercase__    ,       ckpt_dir=args.tf_cache_dir    ,       model_name=args.model_name      )


if __name__ == "__main__":
						main()




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	: Optional[int]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


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

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
