






"""simple docstring"""



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class 			a	(  tf.keras.layers.Layer      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = "longest"				,						_lowerCamelCase = True				,						_lowerCamelCase = 5_1_2				,						_lowerCamelCase = None				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						**_lowerCamelCase				,						):
      super().__init__()
      if use_fast_bert_tokenizer:
        lowercase				       =			FastBertTokenizer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case_nfd_strip_accents=_lowerCamelCase				,						**_lowerCamelCase			)
      else:
        lowercase				       =			tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=_lowerCamelCase				,						key_dtype=tf.string				,						values=tf.range(tf.size(_lowerCamelCase				,						out_type=tf.intaa			)				,						dtype=tf.intaa			)				,						value_dtype=tf.intaa				,						)				,						num_oov_buckets=1				,						)
        lowercase				       =			BertTokenizerLayer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case=_lowerCamelCase				,						**_lowerCamelCase			)

      lowercase				       =			vocab_list
      lowercase				       =			do_lower_case
      lowercase				       =			cls_token_id or vocab_list.index('[CLS]'			)
      lowercase				       =			sep_token_id or vocab_list.index('[SEP]'			)
      lowercase				       =			pad_token_id or vocab_list.index('[PAD]'			)
      lowercase				       =			ShrinkLongestTrimmer(max_length - 3				,						axis=1			)  # Allow room for special tokens
      lowercase				       =			max_length
      lowercase				       =			padding
      lowercase				       =			truncation
      lowercase				       =			pad_to_multiple_of
      lowercase				       =			return_token_type_ids
      lowercase				       =			return_attention_mask







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						**_lowerCamelCase			):  # noqa: F821
      lowercase				       =			kwargs.pop('do_lower_case'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      lowercase				       =			kwargs.pop('cls_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      lowercase				       =			kwargs.pop('sep_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      lowercase				       =			kwargs.pop('pad_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.pad_token_id if pad_token_id is None else pad_token_id

      lowercase				       =			tokenizer.get_vocab()
      lowercase				       =			sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,						key=lambda _lowerCamelCase			: x[1]			)
      lowercase				       =			[entry[0] for entry in vocab]
      return cls(
          vocab_list=_lowerCamelCase				,						do_lower_case=_lowerCamelCase				,						cls_token_id=_lowerCamelCase				,						sep_token_id=_lowerCamelCase				,						pad_token_id=_lowerCamelCase				,						**_lowerCamelCase				,						)







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      try:
        lowercase				       =			BertTokenizer.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        lowercase				       =			BertTokenizerFast.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      return cls.from_tokenizer(_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if self.do_lower_case:
        lowercase				       =			case_fold_utfa(_lowerCamelCase			)
      lowercase				       =			self.tf_tokenizer.tokenize(_lowerCamelCase			)
      return tokens.merge_dims(1				,						-1			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						):
      if padding is None:
        lowercase				       =			self.padding
      if padding not in ("longest", "max_length"):
        raise ValueError('Padding must be either \'longest\' or \'max_length\'!'			)
      if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'			)
      if max_length is None:
        lowercase				       =			self.max_length
      if truncation is None:
        lowercase				       =			self.truncation
      if pad_to_multiple_of is None:
        lowercase				       =			self.pad_to_multiple_of
      if return_token_type_ids is None:
        lowercase				       =			self.return_token_type_ids
      if return_attention_mask is None:
        lowercase				       =			self.return_attention_mask
      if not isinstance(_lowerCamelCase				,						tf.Tensor			):
        lowercase				       =			tf.convert_to_tensor(_lowerCamelCase			)
      if text_pair is not None and not isinstance(_lowerCamelCase				,						tf.Tensor			):
        lowercase				       =			tf.convert_to_tensor(_lowerCamelCase			)
      if text_pair is not None:
        if text.shape.rank > 1:
          raise ValueError('text argument should not be multidimensional when a text pair is supplied!'			)
        if text_pair.shape.rank > 1:
          raise ValueError('text_pair should not be multidimensional!'			)
      if text.shape.rank == 2:
        lowercase       ,	lowercase				       =			text[:, 0], text[:, 1]
      lowercase				       =			self.unpaired_tokenize(_lowerCamelCase			)
      if text_pair is None:  # Unpaired text
        if truncation:
          lowercase				       =			text[:, : max_length - 2]  # Allow room for special tokens
        lowercase       ,	lowercase				       =			combine_segments(
            (text,)				,						start_of_sequence_id=self.cls_token_id				,						end_of_segment_id=self.sep_token_id			)
      else:  # Paired text
        lowercase				       =			self.unpaired_tokenize(_lowerCamelCase			)
        if truncation:
          lowercase       ,	lowercase				       =			self.paired_trimmer.trim([text, text_pair]			)
        lowercase       ,	lowercase				       =			combine_segments(
            (text, text_pair)				,						start_of_sequence_id=self.cls_token_id				,						end_of_segment_id=self.sep_token_id			)
      if padding == "longest":
        lowercase				       =			input_ids.bounding_shape(axis=1			)
        if pad_to_multiple_of is not None:
          # No ceiling division in tensorflow, so we negate floordiv instead
          lowercase				       =			pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,						_lowerCamelCase			))
      else:
        lowercase				       =			max_length

      lowercase       ,	lowercase				       =			pad_model_inputs(_lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						pad_value=self.pad_token_id			)
      lowercase				       =			{'input_ids': input_ids}
      if return_attention_mask:
        lowercase				       =			attention_mask
      if return_token_type_ids:
        lowercase       ,	lowercase				       =			pad_model_inputs(
            _lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						pad_value=self.pad_token_id			)
        lowercase				       =			token_type_ids
      return output







    def      UpperCamelCase_ (     self			):
      return {
          "vocab_list": self.vocab_list,
          "do_lower_case": self.do_lower_case,
          "cls_token_id": self.cls_token_id,
          "sep_token_id": self.sep_token_id,
          "pad_token_id": self.pad_token_id,
      }







import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=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(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf 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.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

