




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_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Tuple )	->			int:





				"""simple docstring"""


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

				return flax_key_tuple, flax_tensor







def      lowerCamelCase_						(			UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Tuple )	->			List[str]:





				"""simple docstring"""


				if "metadata" in layer:
								__lowerCamelCase									=       layer.split('metadata' )
								__lowerCamelCase									=       ''.join(split_layer[0] )[:-1]
								__lowerCamelCase									=       [tuple(('metadata' + split_layer[1]).split('/' ) )]
				elif "kvstore" in layer:
								__lowerCamelCase									=       layer.split('kvstore' )
								__lowerCamelCase									=       ''.join(split_layer[0] )[:-1]
								__lowerCamelCase									=       [tuple(('kvstore' + split_layer[1]).split('/' ) )]

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

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

				return curr_real_layer_name, split_layer, content







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Tuple )	->			Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       rename_keys(UpperCamelCase__ )
				__lowerCamelCase									=       {}
				for k, v in current_block.items():
								__lowerCamelCase									=       v
				__lowerCamelCase									=       new_current_block
				torch.save(UpperCamelCase__					,	UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict					,	UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: str = WEIGHTS_NAME )	->			List[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       convert_file_size_to_int(UpperCamelCase__ )
				__lowerCamelCase									=       []
				__lowerCamelCase									=       {}
				__lowerCamelCase									=       0
				__lowerCamelCase									=       0

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

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

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

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

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

								__lowerCamelCase									=       raw_weights.to(getattr(UpperCamelCase__					,	UpperCamelCase__ ) )
								current_block_size += weight_size
								total_size += weight_size

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

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

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

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

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

				return metadata, index


if __name__ == "__main__":
					__A								=       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.",
					)
					__A								=       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_						(			)	->			str:





				"""simple docstring"""


				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''xlm-roberta-xl'''


			def __init__( self     ,		lowerCamelCase__=250_880     ,		lowerCamelCase__=2_560     ,		lowerCamelCase__=36     ,		lowerCamelCase__=32     ,		lowerCamelCase__=10_240     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=514     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-05     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=2     ,		lowerCamelCase__="absolute"     ,		lowerCamelCase__=True     ,		lowerCamelCase__=None     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__     ,		bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		**lowerCamelCase__				)
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       position_embedding_type
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       classifier_dropout







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@property
			def  lowercase_       ( self				)       -> Mapping[str, Mapping[int, str]]:



							'''simple docstring'''
							if self.task == "multiple-choice":
											__lowerCamelCase									=       {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
											__lowerCamelCase									=       {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]				)


