


"""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 lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: Any ):






 """simple docstring"""







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

 return flax_key_tuple, flax_tensor


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Tuple ):






 """simple docstring"""







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

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

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

 return curr_real_layer_name, split_layer, content


def lowerCAmelCase_      (				UpperCamelCase__	: List[str]				,   UpperCamelCase__	: List[Any] ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: str = WEIGHTS_NAME ):






 """simple docstring"""







 __lowercase									=	convert_file_size_to_int(UpperCamelCase__ )
 __lowercase									=	[]
 __lowercase									=	{}
 __lowercase									=	0
 __lowercase									=	0

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

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

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

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

  __lowercase									=	raw_weights.to(getattr(UpperCamelCase__				,   UpperCamelCase__ ) )
  current_block_size += weight_size
  total_size += weight_size

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

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

 with open(os.path.join(UpperCamelCase__				,   UpperCamelCase__ )				,   """w"""				,   encoding="""utf-8""" ) as f:
  __lowercase									=	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 lowerCAmelCase_      (				):






 """simple docstring"""







 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







 for i, input_name in enumerate(node_proto.input ):
  if input_name == name:
   node_proto.input.insert(UpperCamelCase__				,   UpperCamelCase__ )
   node_proto.input.pop(i + 1 )

 if node_proto.op_type == "If":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )
  _graph_replace_input_with(node_proto.attribute[1].g				,   UpperCamelCase__				,   UpperCamelCase__ )
 if node_proto.op_type == "Loop":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 for n in graph_proto.node:
  _node_replace_input_with(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	list(model.graph.initializer )
 __lowercase									=	list(model_without_ext.graph.initializer )
 for i, ref_i in ind_to_replace:
  assert inits_with_data[i].name == inits[i].name
  assert inits_with_data[ref_i].name == inits[ref_i].name
  assert i > ref_i

  __lowercase									=	inits[i].name
  __lowercase									=	inits[ref_i].name

  model_without_ext.graph.initializer.remove(inits[i] )

  # for n in model.graph.node:
  _graph_replace_input_with(model_without_ext.graph				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	os.path.dirname(UpperCamelCase__ )
 __lowercase									=	os.path.basename(UpperCamelCase__ )

 __lowercase									=	onnx.load(os.path.join(UpperCamelCase__				,   UpperCamelCase__ ) )

 __lowercase									=	list(model.graph.initializer )

 __lowercase									=	set()
 __lowercase									=	{}
 __lowercase									=	[]

 __lowercase									=	0

 for i in range(len(UpperCamelCase__ ) ):
  if i in dup_set:
   continue

  for j in range(i + 1				,   len(UpperCamelCase__ ) ):
   if j in dup_set:
    continue
   if _is_equal_tensor_proto(inits[i]				,   inits[j] ):
    dup_set.add(UpperCamelCase__ )
    dup_set.add(UpperCamelCase__ )

    __lowercase									=	inits[j].data_type
    __lowercase									=	numpy.prod(inits[j].dims )
    if dtype == 1:
     mem_size *= 4
    elif dtype == 6:
     mem_size *= 4
    elif dtype == 7 or dtype == 11:
     mem_size *= 8
    else:
     print("""unexpected data type: """				,   UpperCamelCase__ )
    total_reduced_size += mem_size

    __lowercase									=	inits[i].name
    __lowercase									=	inits[j].name

    if name_i in dup_map:
     dup_map[name_i].append(UpperCamelCase__ )
    else:
     __lowercase									=	[name_j]
    ind_to_replace.append((j, i) )

 print("""total reduced size: """				,   total_reduced_size / 1024 / 1024 / 1024				,   """GB""" )

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
