
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   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[Any]  ):






 """simple docstring"""


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

 return flax_key_tuple, flax_tensor






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Dict  ):






 """simple docstring"""


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

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

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

 return curr_real_layer_name, split_layer, content






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Union[str, Any]  ):






 """simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     Any			,							UpperCamelCase:     str = WEIGHTS_NAME  ):






 """simple docstring"""


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

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

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

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

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

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

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

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

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

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

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

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

 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   (	):






 """simple docstring"""


 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							attention_head_dim
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							conv_glu_dim
								__lowerCAmelCase         =							conv_dropout
								__lowerCAmelCase         =							num_conv_layers
								__lowerCAmelCase         =							input_feat_per_channel
								__lowerCAmelCase         =							input_channels
								__lowerCAmelCase         =							conv_channels
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )

								if len(self.conv_kernel ) != self.num_conv_layers:
									raise ValueError(
									    "Configuration for convolutional module is incorrect. "
									    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
									    F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
									    F"`config.num_conv_layers = {self.num_conv_layers}`." )






