





"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:str     ,       __UpperCamelCase			:NestedDataStructureLike[PathLike]     ,       __UpperCamelCase			:Optional[NamedSplit] = None     ,       __UpperCamelCase			:Optional[Features] = None     ,       __UpperCamelCase			:str = None     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[str] = None     ,       __UpperCamelCase			:Optional[int] = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												super().__init__(
												    __UpperCamelCase     ,       split=__UpperCamelCase     ,       features=__UpperCamelCase     ,       cache_dir=__UpperCamelCase     ,       keep_in_memory=__UpperCamelCase     ,       streaming=__UpperCamelCase     ,       num_proc=__UpperCamelCase     ,       **__UpperCamelCase     ,       )
												A		=  field
												A		=  path_or_paths if isinstance(__UpperCamelCase     ,       __UpperCamelCase		) else {self.split: path_or_paths}
												A		=  Json(
												    cache_dir=__UpperCamelCase     ,       data_files=__UpperCamelCase     ,       features=__UpperCamelCase     ,       field=__UpperCamelCase     ,       **__UpperCamelCase     ,       )


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# Build iterable dataset
												if self.streaming:
																			A		=  self.builder.as_streaming_dataset(split=self.split		)
												# Build regular (map-style) dataset
												else:
																			A		=  None
																			A		=  None
																			A		=  None
																			A		=  None

																			self.builder.download_and_prepare(
																			    download_config=__UpperCamelCase     ,       download_mode=__UpperCamelCase     ,       verification_mode=__UpperCamelCase     ,       base_path=__UpperCamelCase     ,       num_proc=self.num_proc     ,       )
																			A		=  self.builder.as_dataset(
																			    split=self.split     ,       verification_mode=__UpperCamelCase     ,       in_memory=self.keep_in_memory		)
												return dataset







class 					_UpperCAmelCase   :


					def __init__(					self			:Any     ,       __UpperCamelCase			:Dataset     ,       __UpperCamelCase			:Union[PathLike, BinaryIO]     ,       __UpperCamelCase			:Optional[int] = None     ,       __UpperCamelCase			:Optional[int] = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												if num_proc is not None and num_proc <= 0:
																			raise ValueError(f"num_proc {num_proc} must be an integer > 0."		)

												A		=  dataset
												A		=  path_or_buf
												A		=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												A		=  num_proc
												A		=  "utf-8"
												A		=  to_json_kwargs


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  self.to_json_kwargs.pop("path_or_buf"     ,       __UpperCamelCase		)
												A		=  self.to_json_kwargs.pop("orient"     ,       "records"		)
												A		=  self.to_json_kwargs.pop("lines"     ,       True if orient == "records" else False		)
												A		=  self.to_json_kwargs.pop("index"     ,       False if orient in ["split", "table"] else True		)
												A		=  self.to_json_kwargs.pop("compression"     ,       __UpperCamelCase		)

												if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																			raise NotImplementedError(f"`datasets` currently does not support {compression} compression"		)

												if isinstance(self.path_or_buf     ,       (str, bytes, os.PathLike)		):
																			with fsspec.open(self.path_or_buf     ,       "wb"     ,       compression=__UpperCamelCase		) as buffer:
																										A		=  self._write(file_obj=__UpperCamelCase     ,       orient=__UpperCamelCase     ,       lines=__UpperCamelCase     ,       index=__UpperCamelCase     ,       **self.to_json_kwargs		)
												else:
																			if compression:
																										raise NotImplementedError(
																										    f"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																										    " was passed. Please provide a local path instead."		)
																			A		=  self._write(
																			    file_obj=self.path_or_buf     ,       orient=__UpperCamelCase     ,       lines=__UpperCamelCase     ,       index=__UpperCamelCase     ,       **self.to_json_kwargs		)
												return written


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A,	A,	A,	A,	A		=  args

												A		=  query_table(
												    table=self.dataset.data     ,       key=slice(__UpperCamelCase     ,       offset + self.batch_size		)     ,       indices=self.dataset._indices     ,       )
												A		=  batch.to_pandas().to_json(
												    path_or_buf=__UpperCamelCase     ,       orient=__UpperCamelCase     ,       lines=__UpperCamelCase     ,       index=__UpperCamelCase     ,       **__UpperCamelCase		)
												if not json_str.endswith("\n"		):
																			json_str += "\n"
												return json_str.encode(self.encoding		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:BinaryIO     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Dict     ,       **__UpperCamelCase			:int     ,       ):
												A		=  0

												if self.num_proc is None or self.num_proc == 1:
																			for offset in logging.tqdm(
																			    range(0     ,       len(self.dataset		)     ,       self.batch_size		)     ,       unit="ba"     ,       disable=not logging.is_progress_bar_enabled()     ,       desc="Creating json from Arrow format"     ,       ):
																										A		=  self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
																										written += file_obj.write(__UpperCamelCase		)
												else:
																			A,	A		=  len(self.dataset		), self.batch_size
																			with multiprocessing.Pool(self.num_proc		) as pool:
																										for json_str in logging.tqdm(
																										    pool.imap(
																										        self._batch_json     ,       [(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,       __UpperCamelCase     ,       __UpperCamelCase		)]     ,       )     ,       total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,       unit="ba"     ,       disable=not logging.is_progress_bar_enabled()     ,       desc="Creating json from Arrow format"     ,       ):
																																	written += file_obj.write(__UpperCamelCase		)

												return written









"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


