



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

   return flax_key_tuple, flax_tensor
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]     )     ->					Tuple:
   if "metadata" in layer:
      UpperCamelCase   :  List[str]						     =     layer.split('metadata'     )
      UpperCamelCase   :  Union[str, Any]						     =     ''.join(split_layer[0]     )[:-1]
      UpperCamelCase   :  Dict						     =     [tuple(('metadata' + split_layer[1]).split('/'     )     )]
   elif "kvstore" in layer:
      UpperCamelCase   :  Optional[int]						     =     layer.split('kvstore'     )
      UpperCamelCase   :  Tuple						     =     ''.join(split_layer[0]     )[:-1]
      UpperCamelCase   :  Tuple						     =     [tuple(('kvstore' + split_layer[1]).split('/'     )     )]

   else:
      UpperCamelCase   :  List[Any]						     =     layer.split('/'     )
      UpperCamelCase   :  int						     =     '/'.join(split_layer[:-1]     )
      UpperCamelCase   :  List[Any]						     =     (split_layer[-1],)

   if "kvstore/path" in layer:
      UpperCamelCase   :  List[Any]						     =     F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
   elif "kvstore/driver" in layer:
      UpperCamelCase   :  Union[str, Any]						     =     'file'
   else:
      UpperCamelCase   :  Tuple						     =     checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Dict     )     ->					List[Any]:
   UpperCamelCase   :  Any						     =     rename_keys(_A     )
   UpperCamelCase   :  Tuple						     =     {}
   for k, v in current_block.items():
      UpperCamelCase   :  Dict						     =     v
   UpperCamelCase   :  Tuple						     =     new_current_block
   torch.save(_A	,					_A     )
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Any	,					snake_case__	:   List[Any]	,					snake_case__	:   Optional[Any]	,					snake_case__	:   str = WEIGHTS_NAME     )     ->					Union[str, Any]:
   UpperCamelCase   :  List[str]						     =     convert_file_size_to_int(_A     )
   UpperCamelCase   :  int						     =     []
   UpperCamelCase   :  Optional[int]						     =     {}
   UpperCamelCase   :  List[str]						     =     0
   UpperCamelCase   :  List[Any]						     =     0

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

   UpperCamelCase   :  List[str]						     =     {}
   for layer in checkpoint_info.keys():
      UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Dict						     =     get_key_and_tensorstore_dict(
          _A	,					_A	,					_A     )
      if curr_real_layer_name in all_layers:
         UpperCamelCase   :  Tuple						     =     content
      else:
         UpperCamelCase   :  Union[str, Any]						     =     {split_layer[-1]: content}

   for key in all_layers.keys():
      # open tensorstore file
      UpperCamelCase   :  Tuple						     =     ts.open(unflatten_dict(all_layers[key]     )     ).result().read().result()
      UpperCamelCase   :  Union[str, Any]						     =     torch.tensor(_A     )
      UpperCamelCase   :  List[str]						     =     raw_weights.numel() * dtype_byte_size(raw_weights.dtype     )

      # use the renaming pattern from the small conversion scripts
      UpperCamelCase     ,					UpperCamelCase   :  Dict						     =     rename_base_flax_keys(tuple(key.split('/'     )     )	,					_A     )
      UpperCamelCase   :  Union[str, Any]						     =     '/'.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:
         UpperCamelCase   :  List[str]						     =     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
         UpperCamelCase   :  List[str]						     =     {}
         UpperCamelCase   :  Any						     =     0

      UpperCamelCase   :  Union[str, Any]						     =     raw_weights.to(getattr(_A	,					_A     )     )
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   UpperCamelCase   :  str						     =     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
   UpperCamelCase   :  str						     =     {}
   UpperCamelCase   :  Optional[int]						     =     {}
   for idx, shard in enumerate(_A     ):
      UpperCamelCase   :  Union[str, Any]						     =     weights_name.replace(
          '.bin'	,					F"""-{idx+1:05d}-of-{len(_A     ):05d}.bin"""     )  # len(sharded_state_dicts):05d}
      UpperCamelCase   :  Any						     =     os.path.join(_A	,					weights_name.replace('.bin'	,					F"""-{idx+1:05d}-of-???.bin"""     )     )
      os.rename(_A	,					os.path.join(_A	,					_A     )     )
      UpperCamelCase   :  Union[str, Any]						     =     shard
      for key in shard:
         UpperCamelCase   :  Tuple						     =     shard_file

    # Add the metadata
   UpperCamelCase   :  Any						     =     {'total_size': total_size}
   UpperCamelCase   :  List[Any]						     =     {'metadata': metadata, 'weight_map': weight_map}

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

   return metadata, index


if __name__ == "__main__":
    __UpperCAmelCase	       =	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.''',
    )
    __UpperCAmelCase	       =	parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )




def 		UpperCamelCase     (       )     ->					int:
   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

   UpperCamelCase   :  List[str]						     =     TaTokenizer.from_pretrained('t5-small'     )
   UpperCamelCase   :  Union[str, Any]						     =     'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

   UpperCamelCase   :  Tuple						     =     tokenizer(_A	,					return_tensors='pt'     ).input_ids
   UpperCamelCase   :  Optional[Any]						     =     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	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  "data2vec-text"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  List[str]						     =     type_vocab_size
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[str]						     =     position_embedding_type
          UpperCamelCase   :  Any						     =     use_cache
          UpperCamelCase   :  Any						     =     classifier_dropout






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



