






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      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""

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

				return flax_key_tuple, flax_tensor
def      a_ (      _A      ,   _A      ,   _A  )			->			Tuple:







				"""simple docstring"""

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

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

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

				return curr_real_layer_name, split_layer, content
def      a_ (      _A      ,   _A  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     rename_keys(_A  )
				snake_case__									=     {}
				for k, v in current_block.items():
								snake_case__									=     v
				snake_case__									=     new_current_block
				torch.save(_A      ,   _A  )
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A = WEIGHTS_NAME  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     convert_file_size_to_int(_A  )
				snake_case__									=     []
				snake_case__									=     {}
				snake_case__									=     0
				snake_case__									=     0

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

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

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

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

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

								snake_case__									=     raw_weights.to(getattr(_A      ,   _A  )  )
								current_block_size += weight_size
								total_size += weight_size

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

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

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

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

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

				return metadata, index


if __name__ == "__main__":
							lowercase__					:		Optional[int]	  =						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.""",
							)
							lowercase__					:		int	  =						parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)


def      a_ (      )			->			Union[str, Any]:







				"""simple docstring"""
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				snake_case__									=     tokenizer(_A      ,   return_tensors='pt'  ).input_ids
				snake_case__									=     model.generate(_A      ,   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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




