



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_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          a_       		=flax_key_tuple[:-1] + ("weight",)
          a_       		=torch.permute(__lowerCAmelCase       ,	(0, 2, 1)       )
     elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase       ):
          # 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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__ = WEIGHTS_NAME       ):


     '''simple docstring'''







     a_       		=convert_file_size_to_int(__lowerCAmelCase       )
     a_       		=[]
     a_       		={}
     a_       		=0
     a_       		=0

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

     a_       		={}
     for layer in checkpoint_info.keys():
          a_		,						a_		,						a_       		=get_key_and_tensorstore_dict(
              __lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       )
          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(__lowerCAmelCase       )
          a_       		=raw_weights.numel() * dtype_byte_size(raw_weights.dtype       )

          # use the renaming pattern from the small conversion scripts
          a_		,						a_       		=rename_base_flax_keys(tuple(key.split("/"       )       )       ,	__lowerCAmelCase       )
          a_       		="/".join(__lowerCAmelCase       )

          # 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(
                   __lowerCAmelCase       ,	weights_name.replace(".bin"       ,	F"""-{len(__lowerCAmelCase       )+1:05d}-of-???.bin"""       )       )
               rename_and_save_block(__lowerCAmelCase       ,	__lowerCAmelCase       )
               sharded_state_dicts.append(current_block.keys()       )
               del current_block
               a_       		={}
               a_       		=0

          a_       		=raw_weights.to(getattr(__lowerCAmelCase       ,	__lowerCAmelCase       )       )
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     a_       		={}
     a_       		={}
     for idx, shard in enumerate(__lowerCAmelCase       ):
          a_       		=weights_name.replace(
              ".bin"       ,	F"""-{idx+1:05d}-of-{len(__lowerCAmelCase       ):05d}.bin"""       )  # len(sharded_state_dicts):05d}
          a_       		=os.path.join(__lowerCAmelCase       ,	weights_name.replace(".bin"       ,	F"""-{idx+1:05d}-of-???.bin"""       )       )
          os.rename(__lowerCAmelCase       ,	os.path.join(__lowerCAmelCase       ,	__lowerCAmelCase       )       )
          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(__lowerCAmelCase       ,	__lowerCAmelCase       )       ,	"w"       ,	encoding="utf-8"       ) as f:
          a_       		=json.dumps(__lowerCAmelCase       ,	indent=2       ,	sort_keys=__lowerCAmelCase       ) + "\n"
          f.write(__lowerCAmelCase       )

     return metadata, index


if __name__ == "__main__":
  lowercase							       =			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							       =			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_       (		):


     '''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(__lowerCAmelCase       ,	return_tensors="pt"       ).input_ids
     a_       		=model.generate(__lowerCAmelCase       ,	decoder_start_token_id=0       )
     print(tokenizer.decode(out[0]       )       )







'''simple docstring'''


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


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      "switch_transformers"
 __magic_name__					: List[Any]  =      ["past_key_values"]
 __magic_name__					: Union[str, Any]  =      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




 def __init__(   self							,  lowerCAmelCase_=3_2_1_2_8							,  lowerCAmelCase_=7_6_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=2_0_4_8							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=3							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=8							,  lowerCAmelCase_=False							,  lowerCAmelCase_=0.0_1							,  lowerCAmelCase_="float32"							,  lowerCAmelCase_=False							,  lowerCAmelCase_=3_2							,  lowerCAmelCase_=1_2_8							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_=1e-6							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=0.0_0_1							,  lowerCAmelCase_=1.0							,  lowerCAmelCase_="relu"							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True							,  lowerCAmelCase_=0							,  lowerCAmelCase_=1							,  **lowerCAmelCase_							,  ) ->      Optional[int]:



      """simple docstring"""







      a_       		=vocab_size
      a_       		=d_model
      a_       		=d_kv
      a_       		=d_ff

      a_       		=num_sparse_encoder_layers

      a_       		=num_layers
      a_       		=(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      a_       		=num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
           a_       		=self.num_layers // self.num_sparse_encoder_layers
      else:
           a_       		=self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
           a_       		=self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
           a_       		=self.num_decoder_layers  # HACK: this will create 0 sparse layers

      a_       		=num_heads
      a_       		=num_experts
      a_       		=expert_capacity
      a_       		=router_bias
      a_       		=router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
      a_       		=router_dtype

      a_       		=router_ignore_padding_tokens
      a_       		=relative_attention_num_buckets
      a_       		=relative_attention_max_distance

      a_       		=dropout_rate
      a_       		=layer_norm_epsilon
      a_       		=initializer_factor
      a_       		=feed_forward_proj
      a_       		=use_cache
      a_       		=add_router_probs

      a_       		=router_z_loss_coef
      a_       		=router_aux_loss_coef

      a_       		=self.feed_forward_proj.split("-")
      a_       		=act_info[-1]
      a_       		=act_info[0] == "gated"

      if len(lowerCAmelCase_) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_) > 2:
           raise ValueError(
               f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'")

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           a_       		="gelu_new"

      super().__init__(
          pad_token_id=lowerCAmelCase_							,  eos_token_id=lowerCAmelCase_							,  is_encoder_decoder=lowerCAmelCase_							,  **lowerCAmelCase_							,  )


