




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowercase__				:      Optional[int]	   = namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
     return (abs(source - target) / target) < 0.01





@pytest.mark.integration
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				_TestCommandArgs(dataset=__UpperCamelCase		, all_configs=__UpperCamelCase		, save_infos=__UpperCamelCase)
     a     =				TestCommand(*__UpperCamelCase)
     test_command.run()
     a     =				os.path.join(__UpperCamelCase		, "README.md")
     assert os.path.exists(__UpperCamelCase)
     a     =				DatasetInfosDict.from_directory(__UpperCamelCase)
     a     =				DatasetInfosDict(
         {
             "default": DatasetInfo(
                 features=Features(
                     {
                         "tokens": Sequence(Value("string")),
                         "ner_tags": Sequence(
                             ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"])),
                         "langs": Sequence(Value("string")),
                         "spans": Sequence(Value("string")),
                     })		, splits=[
                     {
                         "name": "train",
                         "num_bytes": 2_35_15_63,
                         "num_examples": 1_00_00,
                     },
                     {
                         "name": "validation",
                         "num_bytes": 23_84_18,
                         "num_examples": 10_00,
                     },
                 ]		, download_size=3_94_06_80		, dataset_size=2_58_99_81		, )
         })
     assert dataset_infos.keys() == expected_dataset_infos.keys()
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          a  ,				a     =				getattr(dataset_infos["default"]		, __UpperCamelCase), getattr(expected_dataset_infos["default"]		, __UpperCamelCase)
          if key == "num_bytes":
               assert is_apercent_close(__UpperCamelCase		, __UpperCamelCase)
          elif key == "splits":
               assert list(__UpperCamelCase) == list(__UpperCamelCase)
               for split in result:
                    assert result[split].name == expected[split].name
                    assert result[split].num_examples == expected[split].num_examples
                    assert is_apercent_close(result[split].num_bytes		, expected[split].num_bytes)
          else:
               result == expected







import argparse

import torch

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


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     if k.startswith("encoder"):
          a     =				k.replace(".attn"		, ".self_attn")
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "final_layer_norm")
     elif k.startswith("decoder"):
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "encoder_attn_layer_norm")
          a     =				k.replace("norm3"		, "final_layer_norm")
     return k

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          a     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
   lowercase__				:      int	   = 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"
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

