


'''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 		_A (lowerCAmelCase__     :List[str] ,  lowerCAmelCase__     :List[str]						)      ->     List[str]:




     '''simple docstring'''



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

     return flax_key_tuple, flax_tensor






def 		_A (lowerCAmelCase__     :List[str] ,  lowerCAmelCase__     :Any ,  lowerCAmelCase__     :Optional[Any]						)      ->     Optional[Any]:




     '''simple docstring'''



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

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

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

     return curr_real_layer_name, split_layer, content






def 		_A (lowerCAmelCase__     :List[Any] ,  lowerCAmelCase__     :Union[str, Any]						)      ->     Any:




     '''simple docstring'''



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






def 		_A (lowerCAmelCase__     :Optional[Any] ,  lowerCAmelCase__     :Optional[int] ,  lowerCAmelCase__     :Tuple ,  lowerCAmelCase__     :str ,  lowerCAmelCase__     :str = WEIGHTS_NAME						)      ->     List[str]:




     '''simple docstring'''



     _a						=							convert_file_size_to_int(__SCREAMING_SNAKE_CASE						)
     _a						=							[]
     _a						=							{}
     _a						=							0
     _a						=							0

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

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

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

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

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

          _a						=							raw_weights.to(getattr(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE						)						)
          current_block_size += weight_size
          total_size += weight_size

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

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

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

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

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

     return metadata, index


if __name__ == "__main__":
 a_    :			List[str]      				=			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_    :			Optional[Any]      				=			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 ()      ->     str:




     '''simple docstring'''



     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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



'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





											return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

											del W_supports["sizes"]
											del W_supports["start_token_id"]
											del W_supports["end_token_id"]

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



