





'''simple docstring'''



import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  int				):
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)
						__lowercase										=							torch.permute(lowerCamelCase_		,       (0, 2, 1)				)
			elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_				):
						# linear layer
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)
						__lowercase										=							flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						__lowercase										=							flax_key_tuple[:-1] + ('''weight''',)

			return flax_key_tuple, flax_tensor




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Union[str, Any]				):
			if "metadata" in layer:
						__lowercase										=							layer.split('''metadata'''				)
						__lowercase										=							''''''.join(split_layer[0]				)[:-1]
						__lowercase										=							[tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
			elif "kvstore" in layer:
						__lowercase										=							layer.split('''kvstore'''				)
						__lowercase										=							''''''.join(split_layer[0]				)[:-1]
						__lowercase										=							[tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

			else:
						__lowercase										=							layer.split('''/'''				)
						__lowercase										=							'''/'''.join(split_layer[:-1]				)
						__lowercase										=							(split_layer[-1],)

			if "kvstore/path" in layer:
						__lowercase										=							f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
			elif "kvstore/driver" in layer:
						__lowercase										=							'''file'''
			else:
						__lowercase										=							checkpoint_info[layer]

			return curr_real_layer_name, split_layer, content




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							rename_keys(lowerCamelCase_				)
			__lowercase										=							{}
			for k, v in current_block.items():
						__lowercase										=							v
			__lowercase										=							new_current_block
			torch.save(lowerCamelCase_		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  str = WEIGHTS_NAME				):
			__lowercase										=							convert_file_size_to_int(lowerCamelCase_				)
			__lowercase										=							[]
			__lowercase										=							{}
			__lowercase										=							0
			__lowercase										=							0

			os.makedirs(lowerCamelCase_		,       exist_ok=lowerCamelCase_				)
			with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''		,       '''rb'''				) as fp:
						__lowercase										=							serialization.msgpack_restore(fp.read()				)['''optimizer''']['''target''']
						__lowercase										=							flatten_dict(lowerCamelCase_		,       sep='''/'''				)

			__lowercase										=							{}
			for layer in checkpoint_info.keys():
						__lowercase						,		__lowercase						,		__lowercase										=							get_key_and_tensorstore_dict(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
						if curr_real_layer_name in all_layers:
									__lowercase										=							content
						else:
									__lowercase										=							{split_layer[-1]: content}

			for key in all_layers.keys():
						# open tensorstore file
						__lowercase										=							ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
						__lowercase										=							torch.tensor(lowerCamelCase_				)
						__lowercase										=							raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

						# use the renaming pattern from the small conversion scripts
						__lowercase						,		__lowercase										=							rename_base_flax_keys(tuple(key.split('''/'''				)				)		,       lowerCamelCase_				)
						__lowercase										=							'''/'''.join(lowerCamelCase_				)

						# If this weight is going to tip up over the maximal size, we split.
						if current_block_size + weight_size > max_shard_size:
									__lowercase										=							os.path.join(
									    lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{len(lowerCamelCase_				)+1:05d}-of-???.bin"				)				)
									rename_and_save_block(lowerCamelCase_		,       lowerCamelCase_				)
									sharded_state_dicts.append(current_block.keys()				)
									del current_block
									__lowercase										=							{}
									__lowercase										=							0

						__lowercase										=							raw_weights.to(getattr(lowerCamelCase_		,       lowerCamelCase_				)				)
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			__lowercase										=							os.path.join(lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{len(lowerCamelCase_				)+1:05d}-of-???.bin"				)				)
			rename_and_save_block(lowerCamelCase_		,       lowerCamelCase_				)
			sharded_state_dicts.append(current_block.keys()				)

			# If we only have one shard, we return it
			if len(lowerCamelCase_				) == 1:
						return {weights_name: sharded_state_dicts[0]}, None

			# Otherwise, let's build the index
			__lowercase										=							{}
			__lowercase										=							{}
			for idx, shard in enumerate(lowerCamelCase_				):
						__lowercase										=							weights_name.replace(
						    '''.bin'''		,       f"-{idx+1:05d}-of-{len(lowerCamelCase_				):05d}.bin"				)  # len(sharded_state_dicts):05d}
						__lowercase										=							os.path.join(lowerCamelCase_		,       weights_name.replace('''.bin'''		,       f"-{idx+1:05d}-of-???.bin"				)				)
						os.rename(lowerCamelCase_		,       os.path.join(lowerCamelCase_		,       lowerCamelCase_				)				)
						__lowercase										=							shard
						for key in shard:
									__lowercase										=							shard_file

    # Add the metadata
			__lowercase										=							{'''total_size''': total_size}
			__lowercase										=							{'''metadata''': metadata, '''weight_map''': weight_map}

			with open(os.path.join(lowerCamelCase_		,       lowerCamelCase_				)		,       '''w'''		,       encoding='''utf-8'''				) as f:
						__lowercase										=							json.dumps(lowerCamelCase_		,       indent=2		,       sort_keys=lowerCamelCase_				) + '''\n'''
						f.write(lowerCamelCase_				)

			return metadata, index


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--switch_t5x_checkpoint_path''',
	    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
	    type=str,
	    required=False,
	    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
	)
	parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
	parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
	    type=str,
	    required=False,
	    help='''Path to the output pytorch model.''',
	)
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)




def    _lowerCAmelCase						(				):
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			__lowercase										=							SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''				)
			config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''				)
			__lowercase										=							SwitchTransformersForConditionalGeneration.from_pretrained(
			    '''/home/arthur_huggingface_co/transformers/switch_converted'''		,       device_map='''auto'''				)

			__lowercase										=							TaTokenizer.from_pretrained('''t5-small'''				)
			__lowercase										=							'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

			__lowercase										=							tokenizer(lowerCamelCase_		,       return_tensors='''pt'''				).input_ids
			__lowercase										=							model.generate(lowerCamelCase_		,       decoder_start_token_id=0				)
			print(tokenizer.decode(out[0]				)				)












'''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





