
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			,		_lowerCamelCase  )						->Optional[int]:
 if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
  # expert layer
  _UpperCAmelCase 				=flax_key_tuple[:-1] + ("""weight""",)
  _UpperCAmelCase 				=torch.permute(A__			,		(0, 2, 1)  )
 elif flax_key_tuple[-1] == "kernel" and ".".join(A__  ):
  # linear layer
  _UpperCAmelCase 				=flax_key_tuple[:-1] + ("""weight""",)
  _UpperCAmelCase 				=flax_tensor.T
 elif flax_key_tuple[-1] in ["scale", "embedding"]:
  _UpperCAmelCase 				=flax_key_tuple[:-1] + ("""weight""",)

 return flax_key_tuple, flax_tensor
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase  )						->Dict:
 if "metadata" in layer:
  _UpperCAmelCase 				=layer.split("metadata"  )
  _UpperCAmelCase 				="""""".join(split_layer[0]  )[:-1]
  _UpperCAmelCase 				=[tuple(("metadata" + split_layer[1]).split("/"  )  )]
 elif "kvstore" in layer:
  _UpperCAmelCase 				=layer.split("kvstore"  )
  _UpperCAmelCase 				="""""".join(split_layer[0]  )[:-1]
  _UpperCAmelCase 				=[tuple(("kvstore" + split_layer[1]).split("/"  )  )]

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

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

 return curr_real_layer_name, split_layer, content
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase  )						->int:
 _UpperCAmelCase 				=rename_keys(A__  )
 _UpperCAmelCase 				={}
 for k, v in current_block.items():
  _UpperCAmelCase 				=v
 _UpperCAmelCase 				=new_current_block
 torch.save(A__			,		A__  )
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = WEIGHTS_NAME  )						->Optional[Any]:
 _UpperCAmelCase 				=convert_file_size_to_int(A__  )
 _UpperCAmelCase 				=[]
 _UpperCAmelCase 				={}
 _UpperCAmelCase 				=0
 _UpperCAmelCase 				=0

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

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

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

  # use the renaming pattern from the small conversion scripts
  _UpperCAmelCase 				=rename_base_flax_keys(tuple(key.split("/"  )  )			,		A__  )
  _UpperCAmelCase 				="""/""".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 				=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 				={}
   _UpperCAmelCase 				=0

  _UpperCAmelCase 				=raw_weights.to(getattr(A__			,		A__  )  )
  current_block_size += weight_size
  total_size += weight_size

 # Add the last block
 _UpperCAmelCase 				=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 				={}
 _UpperCAmelCase 				={}
 for idx, shard in enumerate(A__  ):
  _UpperCAmelCase 				=weights_name.replace(
      ".bin"			,		F"-{idx+1:05d}-of-{len(A__  ):05d}.bin"  )  # len(sharded_state_dicts):05d}
  _UpperCAmelCase 				=os.path.join(A__			,		weights_name.replace(".bin"			,		F"-{idx+1:05d}-of-???.bin"  )  )
  os.rename(A__			,		os.path.join(A__			,		A__  )  )
  _UpperCAmelCase 				=shard
  for key in shard:
   _UpperCAmelCase 				=shard_file

    # Add the metadata
 _UpperCAmelCase 				={"""total_size""": total_size}
 _UpperCAmelCase 				={"""metadata""": metadata, """weight_map""": weight_map}

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

 return metadata, index


if __name__ == "__main__":
       snake_case__      :		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.',
       )
       snake_case__      :		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 					lowerCamelCase__		(				)						->Union[str, Any]:
 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

 _UpperCAmelCase 				=tokenizer(A__			,		return_tensors="pt"  ).input_ids
 _UpperCAmelCase 				=model.generate(A__			,		decoder_start_token_id=0  )
 print(tokenizer.decode(out[0]  )  )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

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





