
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_a      :			str					=							logging.get_logger(__name__)

_a      :			Optional[Any]					=							[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]
def 				a__   (   a   :				str		):

  """simple docstring"""







  if k == "embeddings.weight":
    return "shared.weight"

  for parlai_name, hf_name in PATTERNS:
    _snake_case     :						Optional[int]						=   k.replace(lowerCAmelCase__       ,     lowerCAmelCase__		)

  if k.startswith("encoder"		):
    _snake_case     :						List[Any]						=   k.replace(".attn"       ,     ".self_attn"		)
    _snake_case     :						str						=   k.replace("norm1"       ,     "self_attn_layer_norm"		)
    _snake_case     :						Dict						=   k.replace("norm2"       ,     "final_layer_norm"		)
  elif k.startswith("decoder"		):
    _snake_case     :						Any						=   k.replace("norm1"       ,     "self_attn_layer_norm"		)
    _snake_case     :						List[Any]						=   k.replace("norm2"       ,     "encoder_attn_layer_norm"		)
    _snake_case     :						List[str]						=   k.replace("norm3"       ,     "final_layer_norm"		)
  return k
def 				a__   (   a   :				int		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   [
      "model.encoder.layernorm_embedding.weight",
      "model.encoder.layernorm_embedding.bias",
      "model.decoder.layernorm_embedding.weight",
      "model.decoder.layernorm_embedding.bias",
  ]
  for k in keys:
    _snake_case     :						Union[str, Any]						=   sd.pop(lowerCAmelCase__		)
    _snake_case     :						List[str]						=   k.replace("layernorm_embedding"       ,     "layer_norm"		)
    assert new_k not in sd
    _snake_case     :						Optional[int]						=   v


_a      :			Tuple					=							["""START"""]
@torch.no_grad()
def 				a__   (   a   :				Dict       ,     a   :				List[Any]       ,     a   :				str		):

  """simple docstring"""







  _snake_case     :						str						=   torch.load(lowerCAmelCase__       ,     map_location="cpu"		)
  _snake_case     :						int						=   model["model"]
  _snake_case     :						Any						=   BlenderbotConfig.from_json_file(lowerCAmelCase__		)
  _snake_case     :						str						=   BlenderbotForConditionalGeneration(lowerCAmelCase__		)
  _snake_case     :						List[Any]						=   m.model.state_dict().keys()
  _snake_case     :						List[str]						=   []
  _snake_case     :						List[Any]						=   {}
  for k, v in sd.items():
    if k in IGNORE_KEYS:
      continue

    _snake_case     :						List[str]						=   rename_state_dict_key(lowerCAmelCase__		)
    if new_k not in valid_keys:
      failures.append([k, new_k]		)
    else:
      _snake_case     :						Tuple						=   v
  if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
    rename_layernorm_keys(lowerCAmelCase__		)
  m.model.load_state_dict(lowerCAmelCase__       ,     strict=lowerCAmelCase__		)
  m.half()
  m.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
    _a      :			List[Any]					=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
    parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
    parser.add_argument(
        """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
    )
    _a      :			str					=							parser.parse_args()
    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



"""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 				a__   (   a   :				List[str]       ,     a   :				Any		):

  """simple docstring"""







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

  return flax_key_tuple, flax_tensor
def 				a__   (   a   :				List[Any]       ,     a   :				Union[str, Any]       ,     a   :				List[str]		):

  """simple docstring"""







  if "metadata" in layer:
    _snake_case     :						Optional[int]						=   layer.split("metadata"		)
    _snake_case     :						Optional[int]						=   "".join(split_layer[0]		)[:-1]
    _snake_case     :						int						=   [tuple(("metadata" + split_layer[1]).split("/"		)		)]
  elif "kvstore" in layer:
    _snake_case     :						Any						=   layer.split("kvstore"		)
    _snake_case     :						str						=   "".join(split_layer[0]		)[:-1]
    _snake_case     :						Any						=   [tuple(("kvstore" + split_layer[1]).split("/"		)		)]

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

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

  return curr_real_layer_name, split_layer, content
def 				a__   (   a   :				List[Any]       ,     a   :				List[Any]		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   rename_keys(a		)
  _snake_case     :						int						=   {}
  for k, v in current_block.items():
    _snake_case     :						Optional[int]						=   v
  _snake_case     :						Optional[int]						=   new_current_block
  torch.save(a       ,     a		)
def 				a__   (   a   :				Dict       ,     a   :				Tuple       ,     a   :				List[str]       ,     a   :				int       ,     a   :				str = WEIGHTS_NAME		):

  """simple docstring"""







  _snake_case     :						Any						=   convert_file_size_to_int(a		)
  _snake_case     :						Tuple						=   []
  _snake_case     :						Optional[int]						=   {}
  _snake_case     :						Tuple						=   0
  _snake_case     :						Optional[Any]						=   0

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

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

  for key in all_layers.keys():
    # open tensorstore file
    _snake_case     :						List[str]						=   ts.open(unflatten_dict(all_layers[key]		)		).result().read().result()
    _snake_case     :						Dict						=   torch.tensor(a		)
    _snake_case     :						Dict						=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype		)

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

    _snake_case     :						List[str]						=   raw_weights.to(getattr(a       ,     a		)		)
    current_block_size += weight_size
    total_size += weight_size

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

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

  with open(os.path.join(a       ,     a		)       ,     "w"       ,     encoding="utf-8"		) as f:
    _snake_case     :						Union[str, Any]						=   json.dumps(a       ,     indent=2       ,     sort_keys=a		) + "\n"
    f.write(a		)

  return metadata, index


if __name__ == "__main__":
    _a      :			Dict					=							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.""",
    )
    _a      :			Optional[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 				a__   (   ):

  """simple docstring"""







  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

  _snake_case     :						List[Any]						=   TaTokenizer.from_pretrained("t5-small"		)
  _snake_case     :						Optional[Any]						=   "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

  _snake_case     :						Dict						=   tokenizer(a       ,     return_tensors="pt"		).input_ids
  _snake_case     :						List[Any]						=   model.generate(a       ,     decoder_start_token_id=0		)
  print(tokenizer.decode(out[0]		)		)

