"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCAmelCase__  :  Tuple						     =	datasets.utils.logging.get_logger(__name__)

lowerCAmelCase__  :  Any						     =	['names', 'prefix']
lowerCAmelCase__  :  List[Any]						     =	['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCAmelCase__  :  List[str]						     =	['encoding_errors', 'on_bad_lines']
lowerCAmelCase__  :  Optional[Any]						     =	['date_format']
@dataclass
class 				snake_case       ( datasets.BuilderConfig				):




					"""simple docstring"""

					snake_case__				    = ","
					snake_case__				    = None
					snake_case__				    = "infer"
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = True
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = False
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = True
					snake_case__				    = True
					snake_case__				    = False
					snake_case__				    = True
					snake_case__				    = None
					snake_case__				    = "."
					snake_case__				    = None
					snake_case__				    = '"'
					snake_case__				    = 0
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = None
					snake_case__				    = True
					snake_case__				    = True
					snake_case__				    = 0
					snake_case__				    = True
					snake_case__				    = False
					snake_case__				    = None
					snake_case__				    = 1_00_00
					snake_case__				    = None
					snake_case__				    = "strict"
					snake_case__				    = "error"
					snake_case__				    = None







					def 		__lowerCAmelCase						(				self   :	int			):
									if self.delimiter is not None:
													UpperCAmelCase__             =							self.delimiter
									if self.column_names is not None:
													UpperCAmelCase__             =							self.column_names







					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							{
									    'sep': self.sep,
									    'header': self.header,
									    'names': self.names,
									    'index_col': self.index_col,
									    'usecols': self.usecols,
									    'prefix': self.prefix,
									    'mangle_dupe_cols': self.mangle_dupe_cols,
									    'engine': self.engine,
									    'converters': self.converters,
									    'true_values': self.true_values,
									    'false_values': self.false_values,
									    'skipinitialspace': self.skipinitialspace,
									    'skiprows': self.skiprows,
									    'nrows': self.nrows,
									    'na_values': self.na_values,
									    'keep_default_na': self.keep_default_na,
									    'na_filter': self.na_filter,
									    'verbose': self.verbose,
									    'skip_blank_lines': self.skip_blank_lines,
									    'thousands': self.thousands,
									    'decimal': self.decimal,
									    'lineterminator': self.lineterminator,
									    'quotechar': self.quotechar,
									    'quoting': self.quoting,
									    'escapechar': self.escapechar,
									    'comment': self.comment,
									    'encoding': self.encoding,
									    'dialect': self.dialect,
									    'error_bad_lines': self.error_bad_lines,
									    'warn_bad_lines': self.warn_bad_lines,
									    'skipfooter': self.skipfooter,
									    'doublequote': self.doublequote,
									    'memory_map': self.memory_map,
									    'float_precision': self.float_precision,
									    'chunksize': self.chunksize,
									    'encoding_errors': self.encoding_errors,
									    'on_bad_lines': self.on_bad_lines,
									    'date_format': self.date_format,
									}

									# some kwargs must not be passed if they don't have a default value
									# some others are deprecated and we can also not pass them if they are the default value
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()    ,lowerCamelCase__			):
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
									if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
									if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																	del pd_read_csv_kwargs[pd_read_csv_parameter]

									return pd_read_csv_kwargs






class 				snake_case       ( datasets.ArrowBasedBuilder				):




					"""simple docstring"""

					snake_case__				    = CsvConfig







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return datasets.DatasetInfo(features=self.config.features			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	Optional[Any]			):
									if not self.config.data_files:
													raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''			)
									UpperCAmelCase__             =							dl_manager.download_and_extract(self.config.data_files			)
									if isinstance(lowerCamelCase__    ,(str, list, tuple)			):
													UpperCAmelCase__             =							data_files
													if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							[files]
													UpperCAmelCase__             =							[dl_manager.iter_files(lowerCamelCase__			) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,gen_kwargs={'files': files}			)]
									UpperCAmelCase__             =							[]
									for split_name, files in data_files.items():
													if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							[files]
													UpperCAmelCase__             =							[dl_manager.iter_files(lowerCamelCase__			) for file in files]
													splits.append(datasets.SplitGenerator(name=lowerCamelCase__    ,gen_kwargs={'files': files}			)			)
									return splits







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	pa.Table			):
									if self.config.features is not None:
													UpperCAmelCase__             =							self.config.features.arrow_schema
													if all(not require_storage_cast(lowerCamelCase__			) for feature in self.config.features.values()			):
																	# cheaper cast
																	UpperCAmelCase__             =							pa.Table.from_arrays([pa_table[field.name] for field in schema]    ,schema=lowerCamelCase__			)
													else:
																	# more expensive cast; allows str <-> int/float or str to Audio for example
																	UpperCAmelCase__             =							table_cast(lowerCamelCase__    ,lowerCamelCase__			)
									return pa_table







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							self.config.features.arrow_schema if self.config.features else None
									# dtype allows reading an int column as str
									UpperCAmelCase__             =							(
									    {
									        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCamelCase__			) else object
									        for name, dtype, feature in zip(schema.names    ,schema.types    ,self.config.features.values()			)
									    }
									    if schema is not None
									    else None
									)
									for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__			)			):
													UpperCAmelCase__             =							pd.read_csv(lowerCamelCase__    ,iterator=lowerCamelCase__    ,dtype=lowerCamelCase__    ,**self.config.pd_read_csv_kwargs			)
													try:
																	for batch_idx, df in enumerate(lowerCamelCase__			):
																					UpperCAmelCase__             =							pa.Table.from_pandas(lowerCamelCase__			)
																					# Uncomment for debugging (will print the Arrow table size and elements)
																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																					yield (file_idx, batch_idx), self._cast_table(lowerCamelCase__			)
													except ValueError as e:
																	logger.error(f'''Failed to read file \'{file}\' with error {type(lowerCamelCase__			)}: {e}'''			)
																	raise









import argparse

import torch

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


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["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 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """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							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

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


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

