




"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			__UpperCAmelCase						:			Optional[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(UpperCamelCase				):
						os.makedirs(UpperCamelCase				)

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

			def to_tf_var_name(UpperCamelCase				):
						for patt, repl in iter(UpperCamelCase				):
									__UpperCAmelCase						:			Any												=       name.replace(UpperCamelCase					,							UpperCamelCase				)
						return f"bert/{name}"

			def create_tf_var(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				):
						__UpperCAmelCase						:			str												=       tf.dtypes.as_dtype(tensor.dtype				)
						__UpperCAmelCase						:			Tuple												=       tf.get_variable(dtype=UpperCamelCase					,							shape=tensor.shape					,							name=UpperCamelCase					,							initializer=tf.zeros_initializer()				)
						session.run(tf.variables_initializer([tf_var]				)				)
						session.run(UpperCamelCase				)
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									__UpperCAmelCase						:			Union[str, Any]												=       to_tf_var_name(UpperCamelCase				)
									__UpperCAmelCase						:			List[str]												=       state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose				):
												__UpperCAmelCase						:			Tuple												=       torch_tensor.T
									__UpperCAmelCase						:			str												=       create_tf_var(tensor=UpperCamelCase					,							name=UpperCamelCase					,							session=UpperCamelCase				)
									tf.keras.backend.set_value(UpperCamelCase					,							UpperCamelCase				)
									__UpperCAmelCase						:			Dict												=       session.run(UpperCamelCase				)
									print(f"Successfully created {tf_name}: {np.allclose(UpperCamelCase					,							UpperCamelCase				)}"				)

						__UpperCAmelCase						:			Optional[int]												=       tf.train.Saver(tf.trainable_variables()				)
						saver.save(UpperCamelCase					,							os.path.join(UpperCamelCase					,							model_name.replace("-"					,							"_"				) + ".ckpt"				)				)

def        _UpperCamelCase     (      UpperCamelCase=None				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       argparse.ArgumentParser()
			parser.add_argument("--model_name"					,							type=UpperCamelCase					,							required=UpperCamelCase					,							help="model name e.g. bert-base-uncased"				)
			parser.add_argument(
			    "--cache_dir"					,							type=UpperCamelCase					,							default=UpperCamelCase					,							required=UpperCamelCase					,							help="Directory containing pytorch model"				)
			parser.add_argument("--pytorch_model_path"					,							type=UpperCamelCase					,							required=UpperCamelCase					,							help="/path/to/<pytorch-model-name>.bin"				)
			parser.add_argument("--tf_cache_dir"					,							type=UpperCamelCase					,							required=UpperCamelCase					,							help="Directory in which to save tensorflow model"				)
			__UpperCAmelCase						:			List[Any]												=       parser.parse_args(UpperCamelCase				)

			__UpperCAmelCase						:			List[str]												=       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=UpperCamelCase					,							ckpt_dir=args.tf_cache_dir					,							model_name=args.model_name				)


if __name__ == "__main__":
					main()





'''simple docstring'''

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


UpperCAmelCase					 =	logging.get_logger(__name__)

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

UpperCAmelCase					 =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   __lowercase    ,   tokenizer_file=__lowercase    ,   unk_token=__lowercase    ,   bos_token=__lowercase    ,   eos_token=__lowercase    ,   add_prefix_space=__lowercase    ,   **__lowercase    ,   )

            __lowercase      		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
            if pre_tok_state.get('add_prefix_space'    ,   __lowercase   ) != add_prefix_space:
                 __lowercase      		=getattr(__lowercase    ,   pre_tok_state.pop('type'   )   )
                 __lowercase      		=add_prefix_space
                 __lowercase      		=pre_tok_class(**__lowercase   )

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







            __lowercase      		=[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(__lowercase    ,   add_special_tokens=__lowercase   ) + [self.eos_token_id]   )

            if len(__lowercase   ) > self.model_max_length:
                 __lowercase      		=input_ids[-self.model_max_length :]
            return input_ids


