




'''simple docstring'''





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil





@pytest.fixture
def       __a      (  _UpperCamelCase:       Any       )		->					List[str]:

     """simple docstring"""




     _snake_case				= tmp_path / "file.csv"
     _snake_case				= textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "       )
     with open(_UpperCamelCase   ,		"w"       ) as f:
          f.write(_UpperCamelCase       )
     return str(_UpperCamelCase       )





@pytest.fixture
def       __a      (  _UpperCamelCase:       Dict       )		->					Dict:

     """simple docstring"""




     _snake_case				= tmp_path / "malformed_file.csv"
     _snake_case				= textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "       )
     with open(_UpperCamelCase   ,		"w"       ) as f:
          f.write(_UpperCamelCase       )
     return str(_UpperCamelCase       )





@pytest.fixture
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Tuple       )		->					Tuple:

     """simple docstring"""




     _snake_case				= tmp_path / "csv_with_image.csv"
     _snake_case				= textwrap.dedent(
         F"""\
        image
        {image_file}
        """       )
     with open(_UpperCamelCase   ,		"w"       ) as f:
          f.write(_UpperCamelCase       )
     return str(_UpperCamelCase       )





@pytest.fixture
def       __a      (  _UpperCamelCase:       Tuple       )		->					Dict:

     """simple docstring"""




     _snake_case				= tmp_path / "csv_with_label.csv"
     _snake_case				= textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "       )
     with open(_UpperCamelCase   ,		"w"       ) as f:
          f.write(_UpperCamelCase       )
     return str(_UpperCamelCase       )





@pytest.fixture
def       __a      (  _UpperCamelCase:       str       )		->					Tuple:

     """simple docstring"""




     _snake_case				= tmp_path / "csv_with_int_list.csv"
     _snake_case				= textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "       )
     with open(_UpperCamelCase   ,		"w"       ) as f:
          f.write(_UpperCamelCase       )
     return str(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Union[str, Any]       )		->					Union[str, Any]:

     """simple docstring"""




     _snake_case				= Csv()
     _snake_case				= csv._generate_tables([[csv_file, malformed_csv_file]]       )
     with pytest.raises(_UpperCamelCase   ,		match="Error tokenizing data"       ):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(_UpperCamelCase       ) in record.message
         for record in caplog.records       )





@require_pil
def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Any:

     """simple docstring"""




     with open(_UpperCamelCase   ,		encoding="utf-8"       ) as f:
          _snake_case				= f.read().splitlines()[1]
     _snake_case				= Csv(encoding="utf-8"   ,		features=Features({"image": Image()}       )       )
     _snake_case				= csv._generate_tables([[csv_file_with_image]]       )
     _snake_case				= pa.concat_tables([table for _, table in generator]       )
     assert pa_table.schema.field("image"       ).type == Image()()
     _snake_case				= pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[Any]:

     """simple docstring"""




     with open(_UpperCamelCase   ,		encoding="utf-8"       ) as f:
          _snake_case				= f.read().splitlines()[1:]
     _snake_case				= Csv(encoding="utf-8"   ,		features=Features({"label": ClassLabel(names=["good", "bad"]       )}       )       )
     _snake_case				= csv._generate_tables([[csv_file_with_label]]       )
     _snake_case				= pa.concat_tables([table for _, table in generator]       )
     assert pa_table.schema.field("label"       ).type == ClassLabel(names=["good", "bad"]       )()
     _snake_case				= pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]       ).straint(_UpperCamelCase       ) for label in labels]





def       __a      (  _UpperCamelCase:       str       )		->					Tuple:

     """simple docstring"""




     _snake_case				= Csv(encoding="utf-8"   ,		sep=","   ,		converters={"int_list": lambda _UpperCamelCase       : [int(_UpperCamelCase       ) for i in x.split()]}       )
     _snake_case				= csv._generate_tables([[csv_file_with_int_list]]       )
     _snake_case				= pa.concat_tables([table for _, table in generator]       )
     assert pa.types.is_list(pa_table.schema.field("int_list"       ).type       )
     _snake_case				= pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]








'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''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      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




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

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

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





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "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				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




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

          _snake_case				= rename_state_dict_key(_UpperCamelCase       )
          if new_k not in valid_keys:
               failures.append([k, new_k]       )
          else:
               _snake_case				= 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__":
   UpperCamelCase_		:     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'''
   )
   UpperCamelCase_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


