

import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


_a:    int 							=			"""<<<<<<< This should probably be modified because it mentions: """

_a:    Union[str, Any] 							=			"""=======
>>>>>>>
"""

_a:    int 							=			[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_a:    int 							=			[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r"""tfds\.core""", r"""datasets"""),
    (r"""tf\.io\.gfile\.GFile""", r"""open"""),
    (r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
    (r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
    (r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
    (r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
    (r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
    (r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (r"""tfds\.""", r"""datasets."""),
    (r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
    (r"""self\.builder_config""", r"""self.config"""),
]



def       __lowerCAmelCase      (						A    ):
	return ConvertCommand(args.tfds_path       ,     args.datasets_directory    )




class 						__UpperCamelCase    (					lowercase						):




	@staticmethod
	def       __A					(		lowerCAmelCase	:						ArgumentParser							):


		'''simple docstring'''







		UpperCAmelCase_       				=			parser.add_parser(
		    "convert"		,	help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."		,	)
		train_parser.add_argument(
		    "--tfds_path"		,	type=lowerCAmelCase		,	required=lowerCAmelCase		,	help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."		,	)
		train_parser.add_argument(
		    "--datasets_directory"		,	type=lowerCAmelCase		,	required=lowerCAmelCase		,	help="Path to the HuggingFace Datasets folder."							)
		train_parser.set_defaults(func=lowerCAmelCase							)




	def __init__(		self	:						Any		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	*lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			get_logger("datasets-cli/converting"							)

		UpperCAmelCase_       				=			tfds_path
		UpperCAmelCase_       				=			datasets_directory




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		if os.path.isdir(self._tfds_path							):
			UpperCAmelCase_       				=			os.path.abspath(self._tfds_path							)
		elif os.path.isfile(self._tfds_path							):
			UpperCAmelCase_       				=			os.path.dirname(self._tfds_path							)
		else:
			raise ValueError("--tfds_path is neither a directory nor a file. Please check path."							)

		UpperCAmelCase_       				=			os.path.abspath(self._datasets_directory							)

		self._logger.info(F"Converting datasets from {abs_tfds_path} to {abs_datasets_path}"							)

		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			{}

		if os.path.isdir(self._tfds_path							):
			UpperCAmelCase_       				=			os.listdir(lowerCAmelCase							)
		else:
			UpperCAmelCase_       				=			[os.path.basename(self._tfds_path							)]

		for f_name in file_names:
			self._logger.info(F"Looking at file {f_name}"							)
			UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	lowerCAmelCase							)

			if not os.path.isfile(lowerCAmelCase							) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
				self._logger.info("Skipping file"							)
				continue

			with open(lowerCAmelCase		,	encoding="utf-8"							) as f:
				UpperCAmelCase_       				=			f.readlines()

			UpperCAmelCase_       				=			[]
			UpperCAmelCase_       				=			False
			UpperCAmelCase_       				=			False
			UpperCAmelCase_       				=			[]
			for line in lines:
				UpperCAmelCase_       				=			line

				# Convert imports
				if "import tensorflow.compat.v2 as tf" in out_line:
					continue
				elif "@tfds.core" in out_line:
					continue
				elif "builder=self" in out_line:
					continue
				elif "import tensorflow_datasets.public_api as tfds" in out_line:
					UpperCAmelCase_       				=			"import datasets\n"
				elif "import tensorflow" in out_line:
					# order is important here
					UpperCAmelCase_       				=			""
					continue
				elif "from absl import logging" in out_line:
					UpperCAmelCase_       				=			"from datasets import logging\n"
				elif "getLogger" in out_line:
					UpperCAmelCase_       				=			out_line.replace("getLogger"		,	"get_logger"							)
				elif any(expression in out_line for expression in TO_HIGHLIGHT							):
					UpperCAmelCase_       				=			True
					UpperCAmelCase_       				=			list(filter(lambda lowerCAmelCase							: e in out_line		,	lowerCAmelCase							)							)
					out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCAmelCase							) + "\n"							)
					out_lines.append(lowerCAmelCase							)
					out_lines.append(lowerCAmelCase							)
					continue
				else:
					for pattern, replacement in TO_CONVERT:
						UpperCAmelCase_       				=			re.sub(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)

                # Take care of saving utilities (to later move them together with main script)
				if "tensorflow_datasets" in out_line:
					UpperCAmelCase_       				=			re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"		,	lowerCAmelCase							)
					tfds_imports.extend(imp.strip() for imp in match.group(1							).split(","							)							)
					UpperCAmelCase_       				=			"from . import " + match.group(1							)

				# Check we have not forget anything
				if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
					raise ValueError(F"Error converting {out_line.strip()}"							)

				if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
					UpperCAmelCase_       				=			True
				out_lines.append(lowerCAmelCase							)

			if is_builder or "wmt" in f_name:
				# We create a new directory for each dataset
				UpperCAmelCase_       				=			f_name.replace(".py"		,	""							)
				UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	lowerCAmelCase							)
				UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	lowerCAmelCase							)
				os.makedirs(lowerCAmelCase		,	exist_ok=lowerCAmelCase							)
				self._logger.info(F"Adding directory {output_dir}"							)
				imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}							)
			else:
				# Utilities will be moved at the end
				utils_files.append(lowerCAmelCase							)

			if needs_manual_update:
				with_manual_update.append(lowerCAmelCase							)

			with open(lowerCAmelCase		,	"w"		,	encoding="utf-8"							) as f:
				f.writelines(lowerCAmelCase							)
			self._logger.info(F"Converted in {output_file}"							)

		for utils_file in utils_files:
			try:
				UpperCAmelCase_       				=			os.path.basename(lowerCAmelCase							)
				UpperCAmelCase_       				=			imports_to_builder_map[f_name.replace(".py"		,	""							)]
				self._logger.info(F"Moving {dest_folder} to {utils_file}"							)
				shutil.copy(lowerCAmelCase		,	lowerCAmelCase							)
			except KeyError:
				self._logger.error(F"Cannot find destination folder for {utils_file}. Please copy manually."							)

		if with_manual_update:
			for file_path in with_manual_update:
				self._logger.warning(
				    F"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."							)


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a:    List[Any] 							=			logging.get_logger(__name__)

_a:    List[str] 							=			"""▁"""

_a:    Union[str, Any] 							=			{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

_a:    Tuple 							=			{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

_a:    Optional[int] 							=			{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

_a:    List[Any] 							=			["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

_a:    Tuple 							=			{"""mustc""": MUSTC_LANGS}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     MAX_MODEL_INPUT_SIZES
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']

	SCREAMING_SNAKE_CASE__    				=     []




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Tuple="<s>"		,	lowerCAmelCase	:						Optional[int]="</s>"		,	lowerCAmelCase	:						Any="<pad>"		,	lowerCAmelCase	:						Union[str, Any]="<unk>"		,	lowerCAmelCase	:						Tuple=False		,	lowerCAmelCase	:						List[Any]=False		,	lowerCAmelCase	:						Optional[int]=None		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						Optional[Dict[str, Any]] = None		,	**lowerCAmelCase	:						Any		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=lowerCAmelCase		,	eos_token=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	do_upper_case=lowerCAmelCase		,	do_lower_case=lowerCAmelCase		,	tgt_lang=lowerCAmelCase		,	lang_codes=lowerCAmelCase		,	sp_model_kwargs=self.sp_model_kwargs		,	**lowerCAmelCase		,	)
		UpperCAmelCase_       				=			do_upper_case
		UpperCAmelCase_       				=			do_lower_case

		UpperCAmelCase_       				=			load_json(lowerCAmelCase							)
		UpperCAmelCase_       				=			{v: k for k, v in self.encoder.items()}
		UpperCAmelCase_       				=			spm_file
		UpperCAmelCase_       				=			load_spm(lowerCAmelCase		,	self.sp_model_kwargs							)

		if lang_codes is not None:
			UpperCAmelCase_       				=			lang_codes
			UpperCAmelCase_       				=			LANGUAGES[lang_codes]
			UpperCAmelCase_       				=			[F"<lang:{lang}>" for lang in self.langs]
			UpperCAmelCase_       				=			{lang: self.sp_model.PieceToId(F"<lang:{lang}>"							) for lang in self.langs}

			UpperCAmelCase_       				=			self.lang_tokens
			UpperCAmelCase_       				=			tgt_lang if tgt_lang is not None else self.langs[0]

			self.set_tgt_lang_special_tokens(self._tgt_lang							)
		else:
			UpperCAmelCase_       				=			{}




	@property
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return len(self.encoder							)




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return self._tgt_lang




	@tgt_lang.setter
	def       __A					(		self	:						int		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			new_tgt_lang
		self.set_tgt_lang_special_tokens(lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.lang_code_to_id[tgt_lang]
		UpperCAmelCase_       				=			[lang_code_id]




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		return self.sp_model.encode(lowerCAmelCase		,	out_type=lowerCAmelCase							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		return self.encoder.get(lowerCAmelCase		,	self.encoder[self.unk_token]							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		return self.decoder.get(lowerCAmelCase		,	self.unk_token							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			""
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				UpperCAmelCase_       				=			self.sp_model.decode(lowerCAmelCase							)
				out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
				UpperCAmelCase_       				=			[]
			else:
				current_sub_tokens.append(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.sp_model.decode(lowerCAmelCase							)
		out_string += decoded.upper() if self.do_upper_case else decoded
		return out_string.strip()




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Optional[int]=None							):


		'''simple docstring'''







		if token_ids_a is None:
			return self.prefix_tokens + token_ids_a + [self.eos_token_id]
		# We don't expect to process pairs, but leave the pair logic for API consistency
		return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = False							):


		'''simple docstring'''







		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=lowerCAmelCase		,	token_ids_a=lowerCAmelCase		,	already_has_special_tokens=lowerCAmelCase							)

		UpperCAmelCase_       				=			[1] * len(self.prefix_tokens							)
		UpperCAmelCase_       				=			[1]
		if token_ids_a is None:
			return prefix_ones + ([0] * len(lowerCAmelCase							)) + suffix_ones
		return prefix_ones + ([0] * len(lowerCAmelCase							)) + ([0] * len(lowerCAmelCase							)) + suffix_ones




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.encoder.copy()
		vocab.update(self.added_tokens_encoder							)
		return vocab




	def __getstate__(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.__dict__.copy()
		UpperCAmelCase_       				=			None
		return state




	def __setstate__(		self	:						Tuple		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			d

		# for backward compatibility
		if not hasattr(self		,	"sp_model_kwargs"							):
			UpperCAmelCase_       				=			{}

		UpperCAmelCase_       				=			load_spm(self.spm_file		,	self.sp_model_kwargs							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			Path(lowerCAmelCase							)
		assert save_dir.is_dir(), F"{save_directory} should be a directory"
		UpperCAmelCase_       				=			save_dir / (
		    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
		)
		UpperCAmelCase_       				=			save_dir / (
		    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
		)

		save_json(self.encoder		,	lowerCAmelCase							)

		if os.path.abspath(self.spm_file							) != os.path.abspath(lowerCAmelCase							) and os.path.isfile(self.spm_file							):
			copyfile(self.spm_file		,	lowerCAmelCase							)
		elif not os.path.isfile(self.spm_file							):
			with open(lowerCAmelCase		,	"wb"							) as fi:
				UpperCAmelCase_       				=			self.sp_model.serialized_model_proto()
				fi.write(lowerCAmelCase							)

		return (str(lowerCAmelCase							), str(lowerCAmelCase							))



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			sentencepiece.SentencePieceProcessor(**A    )
	spm.Load(str(A    )    )
	return spm



def       __lowerCAmelCase      (						A    ):
	with open(A       ,     "r"    ) as f:
		return json.load(A    )




def       __lowerCAmelCase      (						A       ,     A    ):
	with open(A       ,     "w"    ) as f:
		json.dump(A       ,     A       ,     indent=2    )