




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

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


UpperCAmelCase_		:				str									=     "<<<<<<< This should probably be modified because it mentions: "

UpperCAmelCase_		:				List[str]									=     "=======\n>>>>>>>\n"

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

UpperCAmelCase_		:				Tuple									=     [
    # (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_   (	lowerCamelCase   ):
				return ConvertCommand(args.tfds_path	,     args.datasets_directory   )




class 		__A      (			UpperCamelCase__			):

						@staticmethod
						def     A__						(		__snake_case   :ArgumentParser ):
										'''simple docstring'''

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

						def __init__(		self   :List[Any]    , __snake_case   :str    , __snake_case   :str    , *__snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=get_logger("""datasets-cli/converting""" )

										__magic_name__	:	Dict								=tfds_path
										__magic_name__	:	Optional[Any]								=datasets_directory


						def     A__						(		self   :Dict ):
										'''simple docstring'''

										if os.path.isdir(self._tfds_path ):
														__magic_name__	:	List[str]								=os.path.abspath(self._tfds_path )
										elif os.path.isfile(self._tfds_path ):
														__magic_name__	:	Optional[Any]								=os.path.dirname(self._tfds_path )
										else:
														raise ValueError("""--tfds_path is neither a directory nor a file. Please check path.""" )

										__magic_name__	:	List[Any]								=os.path.abspath(self._datasets_directory )

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

										__magic_name__	:	Optional[Any]								=[]
										__magic_name__	:	Optional[Any]								=[]
										__magic_name__	:	List[Any]								={}

										if os.path.isdir(self._tfds_path ):
														__magic_name__	:	str								=os.listdir(__snake_case )
										else:
														__magic_name__	:	List[str]								=[os.path.basename(self._tfds_path )]

										for f_name in file_names:
														self._logger.info(f"Looking at file {f_name}" )
														__magic_name__	:	List[str]								=os.path.join(__snake_case    , __snake_case )
														__magic_name__	:	Optional[Any]								=os.path.join(__snake_case    , __snake_case )

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

														with open(__snake_case    , encoding="""utf-8""" ) as f:
																		__magic_name__	:	Dict								=f.readlines()

														__magic_name__	:	List[Any]								=[]
														__magic_name__	:	Dict								=False
														__magic_name__	:	List[str]								=False
														__magic_name__	:	str								=[]
														for line in lines:
																		__magic_name__	:	str								=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:
																						__magic_name__	:	List[str]								="""import datasets\n"""
																		elif "import tensorflow" in out_line:
																						# order is important here
																						__magic_name__	:	Optional[Any]								=""""""
																						continue
																		elif "from absl import logging" in out_line:
																						__magic_name__	:	List[str]								="""from datasets import logging\n"""
																		elif "getLogger" in out_line:
																						__magic_name__	:	Union[str, Any]								=out_line.replace("""getLogger"""    , """get_logger""" )
																		elif any(expression in out_line for expression in TO_HIGHLIGHT ):
																						__magic_name__	:	int								=True
																						__magic_name__	:	List[Any]								=list(filter(lambda __snake_case : e in out_line    , __snake_case ) )
																						out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__snake_case ) + """\n""" )
																						out_lines.append(__snake_case )
																						out_lines.append(__snake_case )
																						continue
																		else:
																						for pattern, replacement in TO_CONVERT:
																										__magic_name__	:	List[str]								=re.sub(__snake_case    , __snake_case    , __snake_case )

                # Take care of saving utilities (to later move them together with main script)
																		if "tensorflow_datasets" in out_line:
																						__magic_name__	:	Optional[Any]								=re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""    , __snake_case )
																						tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
																						__magic_name__	:	Optional[Any]								="""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:
																						__magic_name__	:	Union[str, Any]								=True
																		out_lines.append(__snake_case )

														if is_builder or "wmt" in f_name:
																		# We create a new directory for each dataset
																		__magic_name__	:	Optional[Any]								=f_name.replace(""".py"""    , """""" )
																		__magic_name__	:	List[str]								=os.path.join(__snake_case    , __snake_case )
																		__magic_name__	:	Dict								=os.path.join(__snake_case    , __snake_case )
																		os.makedirs(__snake_case    , exist_ok=__snake_case )
																		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(__snake_case )

														if needs_manual_update:
																		with_manual_update.append(__snake_case )

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

										for utils_file in utils_files:
														try:
																		__magic_name__	:	str								=os.path.basename(__snake_case )
																		__magic_name__	:	Union[str, Any]								=imports_to_builder_map[f_name.replace(""".py"""    , """""" )]
																		self._logger.info(f"Moving {dest_folder} to {utils_file}" )
																		shutil.copy(__snake_case    , __snake_case )
														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'." )










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=act_info[0] == """gated"""

										if len(__snake_case ) > 1 and act_info[0] != "gated" or len(__snake_case ) > 2:
														raise ValueError(
														    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
														    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
														    """'gated-gelu' or 'relu'""" )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														__magic_name__	:	str								="""gelu_new"""

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case    , )




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
														self.fill_with_past_key_values_(__snake_case    , direction="""inputs""" )

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




