


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

   return flax_key_tuple, flax_tensor


def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: Dict						,						A__						: Tuple					)     ->    Dict:
   if "metadata" in layer:
      lowerCamelCase_						:   Tuple		= layer.split("""metadata"""					)
      lowerCamelCase_						:   List[Any]		= """""".join(split_layer[0]					)[:-1]
      lowerCamelCase_						:   Union[str, Any]		= [tuple(("""metadata""" + split_layer[1]).split("""/"""					)					)]
   elif "kvstore" in layer:
      lowerCamelCase_						:   Optional[Any]		= layer.split("""kvstore"""					)
      lowerCamelCase_						:   str		= """""".join(split_layer[0]					)[:-1]
      lowerCamelCase_						:   Optional[int]		= [tuple(("""kvstore""" + split_layer[1]).split("""/"""					)					)]

   else:
      lowerCamelCase_						:   int		= layer.split("""/"""					)
      lowerCamelCase_						:   List[str]		= """/""".join(split_layer[:-1]					)
      lowerCamelCase_						:   Any		= (split_layer[-1],)

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

   return curr_real_layer_name, split_layer, content


def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: Optional[int]					)     ->    int:
   lowerCamelCase_						:   List[Any]		= rename_keys(A__					)
   lowerCamelCase_						:   Dict		= {}
   for k, v in current_block.items():
      lowerCamelCase_						:   str		= v
   lowerCamelCase_						:   str		= new_current_block
   torch.save(A__						,						A__					)


def    __lowerCamelCase   (      A__						: str						,						A__						: List[Any]						,						A__						: Any						,						A__						: str						,						A__						: str = WEIGHTS_NAME					)     ->    Optional[Any]:
   lowerCamelCase_						:   Any		= convert_file_size_to_int(A__					)
   lowerCamelCase_						:   Union[str, Any]		= []
   lowerCamelCase_						:   Tuple		= {}
   lowerCamelCase_						:   Dict		= 0
   lowerCamelCase_						:   List[Any]		= 0

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

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

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

      # use the renaming pattern from the small conversion scripts
      lowerCamelCase_,	lowerCamelCase_						:   str		= rename_base_flax_keys(tuple(key.split("""/"""					)					)						,						A__					)
      lowerCamelCase_						:   Optional[int]		= """/""".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:
         lowerCamelCase_						:   Tuple		= 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
         lowerCamelCase_						:   Optional[int]		= {}
         lowerCamelCase_						:   List[str]		= 0

      lowerCamelCase_						:   Optional[Any]		= raw_weights.to(getattr(A__						,						A__					)					)
      current_block_size += weight_size
      total_size += weight_size

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

    # Add the metadata
   lowerCamelCase_						:   List[Any]		= {"""total_size""": total_size}
   lowerCamelCase_						:   Optional[int]		= {"""metadata""": metadata, """weight_map""": weight_map}

   with open(os.path.join(A__						,						A__					)						,						"""w"""						,						encoding="""utf-8"""					) as f:
      lowerCamelCase_						:   Tuple		= 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

   lowerCamelCase_						:   Dict		= SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""					)
   config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""					)
   lowerCamelCase_						:   List[str]		= SwitchTransformersForConditionalGeneration.from_pretrained(
       """/home/arthur_huggingface_co/transformers/switch_converted"""						,						device_map="""auto"""					)

   lowerCamelCase_						:   Tuple		= TaTokenizer.from_pretrained("""t5-small"""					)
   lowerCamelCase_						:   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>."""

   lowerCamelCase_						:   Optional[Any]		= tokenizer(A__						,						return_tensors="""pt"""					).input_ids
   lowerCamelCase_						:   Optional[int]		= 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





