





'''simple docstring'''



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]				):
			__lowercase										=							os.path.join(args.tf_model_dir		,       '''parameters.json'''				)
			__lowercase										=							json.loads(open(lowerCamelCase_				).read()				)
			if not params:
						raise ValueError(
						    f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."				)
			if not args.output.endswith('''.pt'''				):
						__lowercase										=							args.output + '''.pt'''
			__lowercase										=							OrderedDict()
			with tf.device('''/CPU:0'''				):
						__lowercase										=							tf.train.load_checkpoint(args.tf_model_dir				)
						__lowercase										=							reader.get_variable_to_shape_map()
						for key_name in shapes.keys():
									__lowercase										=							reader.get_tensor(lowerCamelCase_				).astype(np.floataa				)
									if key_name.endswith('''/adam_m'''				) or key_name.endswith('''/adam_v'''				):
												continue
									if key_name.startswith('''pasts/'''				):
												if key_name.startswith('''pasts/mlp'''				):
															__lowercase										=							int(key_name[9]				)
												elif key_name.startswith('''pasts/out'''				):
															__lowercase										=							8
												__lowercase										=							'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
												__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
												__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/moe'''				):
												__lowercase										=							int(key_name[9:].split('''/'''				)[0]				)
												if key_name.endswith('''/switch_gating/kernel'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
															__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/softmlp/kernel'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
															__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/wo/kernel'''				) or key_name.endswith('''/wi/kernel'''				):
															__lowercase										=							key_name[-9:-7]
															for i in range(1_6				):
																		__lowercase										=							'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																		__lowercase										=							(
																		    vnp[i].transpose([1, 0]				).copy()
																		)  # In Mesh-Tensorflow, it is one array, so it is divided
																		__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/mlp'''				):
												__lowercase										=							int(key_name[9:].split('''/'''				)[0]				)
												if key_name.endswith('''/p1/kernel'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
															__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/p1/bias'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/p2/kernel'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
															__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/p2/bias'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/ln'''				):
												__lowercase										=							int(key_name[8:].split('''/'''				)[0]				)
												if key_name.endswith('''/b'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.norm.bias''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/g'''				):
															__lowercase										=							'''model.blocks.%d.feed_forward.norm.weight''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/att'''				):
												__lowercase										=							int(key_name[9:].split('''/'''				)[0]				)
												if key_name.endswith('''/qkv/kernel'''				):
															__lowercase										=							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
															__lowercase										=							state[:, 0, :, :]
															__lowercase										=							state[:, 1, :, :]
															__lowercase										=							state[:, 2, :, :]
															__lowercase										=							(
															    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]				)
															    .transpose([1, 0]				)
															    .copy()
															)  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							(
															    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]				)
															    .transpose([1, 0]				)
															    .copy()
															)  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							(
															    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]				)
															    .transpose([1, 0]				)
															    .copy()
															)  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
															__lowercase										=							torch.tensor(lowerCamelCase_				)
															__lowercase										=							'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
															__lowercase										=							torch.tensor(lowerCamelCase_				)
															__lowercase										=							'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/o/kernel'''				):
															__lowercase										=							'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
															__lowercase										=							(
															    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]				).transpose([1, 0]				).copy()
															)  # Mesh-Tensorflow is a diagonal matrix
															__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/an'''				):
												__lowercase										=							int(key_name[8:].split('''/'''				)[0]				)
												if key_name.endswith('''/b'''				):
															__lowercase										=							'''model.blocks.%d.self_attn.norm.bias''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
												elif key_name.endswith('''/g'''				):
															__lowercase										=							'''model.blocks.%d.self_attn.norm.weight''' % player
															__lowercase										=							vnp.copy()  # same because it is one dimensional
															__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif (
									    key_name.startswith('''model/wte'''				)
									    or key_name.startswith('''model/wpe'''				)
									    or key_name.startswith('''model/ete'''				)
									):
												__lowercase										=							{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
												    key_name[-3:]
												]
												__lowercase										=							'''model.%s.weight''' % nlayer
												__lowercase										=							vnp.copy()  # same in embedded
												__lowercase										=							torch.tensor(lowerCamelCase_				)
												if key_name.startswith('''model/wte'''				):
															__lowercase										=							'''lm_head.weight'''
															__lowercase										=							vnp.copy()  # same in embedded
															__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name.startswith('''model/wob'''				):
												__lowercase										=							'''final_logits_bias'''
												__lowercase										=							vnp.copy()  # same in embedded
												__lowercase										=							state.reshape((1, -1)				)
												__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name == "model/dense/kernel":
												__lowercase										=							'''model.last_project.weight'''
												__lowercase										=							vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
												__lowercase										=							torch.tensor(lowerCamelCase_				)
									elif key_name == "model/dense_1/bias":
												__lowercase										=							'''model.last_project.bias'''
												__lowercase										=							vnp.copy()  # same because it is one dimensional
												__lowercase										=							torch.tensor(lowerCamelCase_				)
			torch.save(lowerCamelCase_		,       args.output				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser(
	    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
	)
	parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
	parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_tf_gptsan_to_pt(args)












'''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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

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

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

_SCREAMING_SNAKE_CASE					=    {
    '''gpt-neox-20b''': 2_0_4_8,
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[Any]											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Optional[Any]:







				'''simple docstring'''





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

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

				__lowercase										=							add_prefix_space


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[int]:







				'''simple docstring'''





				__lowercase										=							[]
				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:
							__lowercase										=							input_ids[-self.model_max_length :]
				return input_ids





