






'''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


UpperCamelCase     :   int	      =		datasets.utils.logging.get_logger(__name__)

UpperCamelCase     :   List[str]	      =		['names', 'prefix']
UpperCamelCase     :   List[str]	      =		['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCamelCase     :   Optional[int]	      =		['encoding_errors', 'on_bad_lines']
UpperCamelCase     :   Optional[int]	      =		['date_format']




@dataclass
class 		UpperCamelCase__     (datasets.BuilderConfig      ):




		'''simple docstring'''





		_UpperCamelCase						=       ","
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "infer"
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       False
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       True
		_UpperCamelCase						=       False
		_UpperCamelCase						=       True
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "."
		_UpperCamelCase						=       None
		_UpperCamelCase						=       '"'
		_UpperCamelCase						=       0
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       True
		_UpperCamelCase						=       0
		_UpperCamelCase						=       True
		_UpperCamelCase						=       False
		_UpperCamelCase						=       None
		_UpperCamelCase						=       10000
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "strict"
		_UpperCamelCase						=       "error"
		_UpperCamelCase						=       None






		def       UpperCamelCase_       (   self							):
								if self.delimiter is not None:
														lowerCamelCase__								=	self.delimiter
								if self.column_names is not None:
														lowerCamelCase__								=	self.column_names






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """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 		UpperCamelCase__     (datasets.ArrowBasedBuilder      ):




		'''simple docstring'''





		_UpperCamelCase						=       CsvConfig






		def       UpperCamelCase_       (   self							):
								return datasets.DatasetInfo(features=self.config.features							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								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}'''							)
								lowerCamelCase__								=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(_lowerCAmelCase ,(str, list, tuple)							):
														lowerCamelCase__								=	data_files
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files}							)]
								lowerCamelCase__								=	[]
								for split_name, files in data_files.items():
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														splits.append(datasets.SplitGenerator(name=_lowerCAmelCase ,gen_kwargs={"""files""": files}							)							)
								return splits






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if self.config.features is not None:
														lowerCamelCase__								=	self.config.features.arrow_schema
														if all(not require_storage_cast(_lowerCAmelCase							) for feature in self.config.features.values()							):
																				# cheaper cast
																				lowerCamelCase__								=	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
																				lowerCamelCase__								=	table_cast(_lowerCAmelCase ,_lowerCAmelCase							)
								return pa_table






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								lowerCamelCase__								=	(
								    {
								        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							)							):
														lowerCamelCase__								=	pd.read_csv(_lowerCAmelCase ,iterator=_lowerCAmelCase ,dtype=_lowerCAmelCase ,**self.config.pd_read_csv_kwargs							)
														try:
																				for batch_idx, df in enumerate(_lowerCAmelCase							):
																										lowerCamelCase__								=	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 os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE:     Optional[int]									= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]


def 					_a							(					lowerCAmelCase )->	str:
		for pegasus_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE_					      =							k.replace(lowerCAmelCase    ,						lowerCAmelCase )
		return k


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	PegasusForConditionalGeneration:
		SCREAMING_SNAKE_CASE_					      =							DEFAULTS.copy()
		cfg_kwargs.update(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusConfig(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusForConditionalGeneration(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.model.state_dict()
		SCREAMING_SNAKE_CASE_					      =							{}
		for k, v in tf_weights.items():
				SCREAMING_SNAKE_CASE_					      =							rename_state_dict_key(lowerCAmelCase )
				if new_k not in sd:
						raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )

				if "dense" in k or "proj" in new_k:
						SCREAMING_SNAKE_CASE_					      =							v.T
				SCREAMING_SNAKE_CASE_					      =							torch.tensor(lowerCAmelCase    ,						dtype=sd[new_k].dtype )
				assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
		# make sure embedding.padding_idx is respected
		SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							{k: torch.zeros_like(lowerCAmelCase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
		mapping.update(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							torch_model.model.load_state_dict(lowerCAmelCase    ,						strict=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[
		    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
		]
		assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
		assert extra == [], F'''no matches found for the following tf keys {extra}'''
		return torch_model


def 					_a							(					lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							tf.train.list_variables(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							['Adafactor', 'global_step']
		for name, shape in tqdm(lowerCAmelCase    ,						desc='converting tf checkpoint to dict' ):
				SCREAMING_SNAKE_CASE_					      =							any(pat in name for pat in ignore_name )
				if skip_key:
						continue
				SCREAMING_SNAKE_CASE_					      =							tf.train.load_variable(lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							array
		return tf_weights


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:
		# save tokenizer first
		SCREAMING_SNAKE_CASE_					      =							Path(lowerCAmelCase ).parent.name
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
		SCREAMING_SNAKE_CASE_					      =							PegasusTokenizer.from_pretrained('sshleifer/pegasus'    ,						model_max_length=lowerCAmelCase )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(lowerCAmelCase )

		# convert model
		SCREAMING_SNAKE_CASE_					      =							get_tf_weights_as_numpy(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']
		if dataset == "large":
				SCREAMING_SNAKE_CASE_					      =							task_specific_params
		SCREAMING_SNAKE_CASE_					      =							convert_pegasus(lowerCAmelCase    ,						lowerCAmelCase )
		torch_model.save_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.state_dict()
		sd.pop('model.decoder.embed_positions.weight' )
		sd.pop('model.encoder.embed_positions.weight' )
		torch.save(lowerCAmelCase    ,						Path(lowerCAmelCase ) / 'pytorch_model.bin' )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
						parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
						SCREAMING_SNAKE_CASE:     int									= parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE:     List[Any]									= Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE:     Tuple									= os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)