



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    ,  lowercase_    ,  lowercase_    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = "longest"    ,  lowercase_ = True    ,  lowercase_ = 512    ,  lowercase_ = None    ,  lowercase_ = True    ,  lowercase_ = True    ,  lowercase_ = True    ,  **lowercase_    ,  )	->					Tuple:
									super().__init__()
									if use_fast_bert_tokenizer:
															lowerCAmelCase	:   List[str]					     =					FastBertTokenizer(
															    lowercase_    ,  token_out_type=tf.intaa    ,  lower_case_nfd_strip_accents=lowercase_    ,  **lowercase_ )
									else:
															lowerCAmelCase	:   Optional[int]					     =					tf.lookup.StaticVocabularyTable(
															    tf.lookup.KeyValueTensorInitializer(
															        keys=lowercase_    ,  key_dtype=tf.string    ,  values=tf.range(tf.size(lowercase_    ,  out_type=tf.intaa )    ,  dtype=tf.intaa )    ,  value_dtype=tf.intaa    ,  )    ,  num_oov_buckets=1    ,  )
															lowerCAmelCase	:   List[str]					     =					BertTokenizerLayer(
															    lowercase_    ,  token_out_type=tf.intaa    ,  lower_case=lowercase_    ,  **lowercase_ )

									lowerCAmelCase	:   List[Any]					     =					vocab_list
									lowerCAmelCase	:   Any					     =					do_lower_case
									lowerCAmelCase	:   Dict					     =					cls_token_id or vocab_list.index("""[CLS]""" )
									lowerCAmelCase	:   Any					     =					sep_token_id or vocab_list.index("""[SEP]""" )
									lowerCAmelCase	:   List[str]					     =					pad_token_id or vocab_list.index("""[PAD]""" )
									lowerCAmelCase	:   str					     =					ShrinkLongestTrimmer(max_length - 3    ,  axis=1 )  # Allow room for special tokens
									lowerCAmelCase	:   List[str]					     =					max_length
									lowerCAmelCase	:   Any					     =					padding
									lowerCAmelCase	:   List[Any]					     =					truncation
									lowerCAmelCase	:   List[Any]					     =					pad_to_multiple_of
									lowerCAmelCase	:   Any					     =					return_token_type_ids
									lowerCAmelCase	:   Dict					     =					return_attention_mask






			@classmethod
			def 							_snake_case   (    cls    ,  lowercase_    ,  **lowercase_ )	->					List[Any]:  # noqa: F821
									lowerCAmelCase	:   Tuple					     =					kwargs.pop("""do_lower_case"""    ,  lowercase_ )
									lowerCAmelCase	:   Optional[Any]					     =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									lowerCAmelCase	:   Any					     =					kwargs.pop("""cls_token_id"""    ,  lowercase_ )
									lowerCAmelCase	:   List[str]					     =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									lowerCAmelCase	:   Dict					     =					kwargs.pop("""sep_token_id"""    ,  lowercase_ )
									lowerCAmelCase	:   Any					     =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									lowerCAmelCase	:   Optional[Any]					     =					kwargs.pop("""pad_token_id"""    ,  lowercase_ )
									lowerCAmelCase	:   Any					     =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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






			@classmethod
			def 							_snake_case   (    cls    ,  lowercase_    ,  *lowercase_    ,  **lowercase_ )	->					Union[str, Any]:
									try:
															lowerCAmelCase	:   List[Any]					     =					BertTokenizer.from_pretrained(lowercase_    ,  *lowercase_    ,  **lowercase_ )
									except:  # noqa: E722
															from .tokenization_bert_fast import BertTokenizerFast

															lowerCAmelCase	:   Optional[Any]					     =					BertTokenizerFast.from_pretrained(lowercase_    ,  *lowercase_    ,  **lowercase_ )
									return cls.from_tokenizer(lowercase_    ,  **lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ )	->					Dict:
									if self.do_lower_case:
															lowerCAmelCase	:   List[Any]					     =					case_fold_utfa(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.tf_tokenizer.tokenize(lowercase_ )
									return tokens.merge_dims(1    ,  -1 )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  lowercase_=None    ,  )	->					Tuple:
									if padding is None:
															lowerCAmelCase	:   List[Any]					     =					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:
															lowerCAmelCase	:   Optional[Any]					     =					self.max_length
									if truncation is None:
															lowerCAmelCase	:   Dict					     =					self.truncation
									if pad_to_multiple_of is None:
															lowerCAmelCase	:   int					     =					self.pad_to_multiple_of
									if return_token_type_ids is None:
															lowerCAmelCase	:   Dict					     =					self.return_token_type_ids
									if return_attention_mask is None:
															lowerCAmelCase	:   int					     =					self.return_attention_mask
									if not isinstance(lowercase_    ,  tf.Tensor ):
															lowerCAmelCase	:   Any					     =					tf.convert_to_tensor(lowercase_ )
									if text_pair is not None and not isinstance(lowercase_    ,  tf.Tensor ):
															lowerCAmelCase	:   Optional[int]					     =					tf.convert_to_tensor(lowercase_ )
									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:
															lowerCAmelCase     ,	lowerCAmelCase	:   List[str]					     =					text[:, 0], text[:, 1]
									lowerCAmelCase	:   Optional[int]					     =					self.unpaired_tokenize(lowercase_ )
									if text_pair is None:  # Unpaired text
															if truncation:
																					lowerCAmelCase	:   str					     =					text[:, : max_length - 2]  # Allow room for special tokens
															lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					combine_segments(
															    (text,)    ,  start_of_sequence_id=self.cls_token_id    ,  end_of_segment_id=self.sep_token_id )
									else:  # Paired text
															lowerCAmelCase	:   str					     =					self.unpaired_tokenize(lowercase_ )
															if truncation:
																					lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					self.paired_trimmer.trim([text, text_pair] )
															lowerCAmelCase     ,	lowerCAmelCase	:   Optional[Any]					     =					combine_segments(
															    (text, text_pair)    ,  start_of_sequence_id=self.cls_token_id    ,  end_of_segment_id=self.sep_token_id )
									if padding == "longest":
															lowerCAmelCase	:   str					     =					input_ids.bounding_shape(axis=1 )
															if pad_to_multiple_of is not None:
																					# No ceiling division in tensorflow, so we negate floordiv instead
																					lowerCAmelCase	:   List[str]					     =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,  lowercase_ ))
									else:
															lowerCAmelCase	:   int					     =					max_length

									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					pad_model_inputs(lowercase_    ,  max_seq_length=lowercase_    ,  pad_value=self.pad_token_id )
									lowerCAmelCase	:   Tuple					     =					{"""input_ids""": input_ids}
									if return_attention_mask:
															lowerCAmelCase	:   List[str]					     =					attention_mask
									if return_token_type_ids:
															lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					pad_model_inputs(
															    lowercase_    ,  max_seq_length=lowercase_    ,  pad_value=self.pad_token_id )
															lowerCAmelCase	:   Optional[Any]					     =					token_type_ids
									return output






			def 							_snake_case   (    self )	->					Optional[Any]:
									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)

