



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_ = None						,  ):
			lowercase__					  =				{}

			if train_file is not None:
						lowercase__					  =				[train_file]
			if eval_file is not None:
						lowercase__					  =				[eval_file]
			if test_file is not None:
						lowercase__					  =				[test_file]

			lowercase__					  =				datasets.load_dataset("csv"						,  data_files=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				list(ds[list(files.keys()  )[0]].features.keys()  )
			lowercase__					  =				features_name.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				list(set(ds[list(files.keys()  )[0]][label_name]  )  )
			lowercase__					  =				{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_  )}
			lowercase__					  =				tokenizer.model_input_names
			lowercase__					  =				{}

			if len(SCREAMING_SNAKE_CASE_  ) == 1:
						for k in files.keys():
									lowercase__					  =				ds[k].map(
									    lambda SCREAMING_SNAKE_CASE_  : tokenizer.batch_encode_plus(
									        example[features_name[0]]						,  truncation=SCREAMING_SNAKE_CASE_						,  max_length=SCREAMING_SNAKE_CASE_						,  padding="max_length"  )						,  batched=SCREAMING_SNAKE_CASE_						,  )
			elif len(SCREAMING_SNAKE_CASE_  ) == 2:
						for k in files.keys():
									lowercase__					  =				ds[k].map(
									    lambda SCREAMING_SNAKE_CASE_  : tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])						,  truncation=SCREAMING_SNAKE_CASE_						,  max_length=SCREAMING_SNAKE_CASE_						,  padding="max_length"						,  )						,  batched=SCREAMING_SNAKE_CASE_						,  )

			def gen_train():
						for ex in transformed_ds[datasets.Split.TRAIN]:
									lowercase__					  =				{k: v for k, v in ex.items() if k in input_names}
									lowercase__					  =				labelaid[ex[label_name]]
									yield (d, label)

			def gen_val():
						for ex in transformed_ds[datasets.Split.VALIDATION]:
									lowercase__					  =				{k: v for k, v in ex.items() if k in input_names}
									lowercase__					  =				labelaid[ex[label_name]]
									yield (d, label)

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									lowercase__					  =				{k: v for k, v in ex.items() if k in input_names}
									lowercase__					  =				labelaid[ex[label_name]]
									yield (d, label)

			lowercase__					  =				(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE_						,  ({k: tf.intaa for k in input_names}, tf.intaa)						,  ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))						,  )
			    if datasets.Split.TRAIN in transformed_ds
			    else None
			)

			if train_ds is not None:
						lowercase__					  =				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

			lowercase__					  =				(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE_						,  ({k: tf.intaa for k in input_names}, tf.intaa)						,  ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))						,  )
			    if datasets.Split.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						lowercase__					  =				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

			lowercase__					  =				(
			    tf.data.Dataset.from_generator(
			        SCREAMING_SNAKE_CASE_						,  ({k: tf.intaa for k in input_names}, tf.intaa)						,  ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))						,  )
			    if datasets.Split.TEST in transformed_ds
			    else None
			)

			if test_ds is not None:
						lowercase__					  =				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

			return train_ds, val_ds, test_ds, labelaid


lowercase_								=     logging.getLogger(__name__)
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					int     		=field(metadata={"""help""": """Which column contains the label"""})
				UpperCamelCase__				:					str     		=field(default=lowercase__    ,	metadata={"""help""": """The path of the training file"""})
				UpperCamelCase__				:					Optional[str]     		=field(default=lowercase__    ,	metadata={"""help""": """The path of the development file"""})
				UpperCamelCase__				:					Optional[str]     		=field(default=lowercase__    ,	metadata={"""help""": """The path of the test file"""})
				UpperCamelCase__				:					int     		=field(
				    default=1_2_8    ,	metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }    ,	)
				UpperCamelCase__				:					bool     		=field(
				    default=lowercase__    ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					str     		=field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
				UpperCamelCase__				:					Optional[str]     		=field(
				    default=lowercase__    ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
				UpperCamelCase__				:					Optional[str]     		=field(
				    default=lowercase__    ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
				UpperCamelCase__				:					bool     		=field(default=lowercase__    ,	metadata={"""help""": """Set this flag to use fast tokenization."""})
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				UpperCamelCase__				:					Optional[str]     		=field(
				    default=lowercase__    ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,	)



def __lowerCAmelCase (     ):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.
			lowercase__					  =				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
			lowercase__  ,       lowercase__  ,       lowercase__					  =				parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir  )
			    and os.listdir(training_args.output_dir  )
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
						    " --overwrite_output_dir to overcome."  )

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,  datefmt="%m/%d/%Y %H:%M:%S"						,  level=logging.INFO						,  )
			logger.info(
			    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1  )}, '''
			    f'''16-bits training: {training_args.fpaa}'''  )
			logger.info(f'''Training/evaluation parameters {training_args}'''  )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			lowercase__					  =				AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,  cache_dir=model_args.cache_dir						,  )

			lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				get_tfds(
			    train_file=data_args.train_file						,  eval_file=data_args.dev_file						,  test_file=data_args.test_file						,  tokenizer=SCREAMING_SNAKE_CASE_						,  label_column_id=data_args.label_column_id						,  max_seq_length=data_args.max_seq_length						,  )

			lowercase__					  =				AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,  num_labels=len(SCREAMING_SNAKE_CASE_  )						,  labelaid=SCREAMING_SNAKE_CASE_						,  idalabel={id: label for label, id in labelaid.items()}						,  finetuning_task="text-classification"						,  cache_dir=model_args.cache_dir						,  )

			with training_args.strategy.scope():
						lowercase__					  =				TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path						,  from_pt=bool(".bin" in model_args.model_name_or_path  )						,  config=SCREAMING_SNAKE_CASE_						,  cache_dir=model_args.cache_dir						,  )

			def compute_metrics(SCREAMING_SNAKE_CASE_  ) -> Dict:
						lowercase__					  =				np.argmax(p.predictions						,  axis=1  )

						return {"acc": (preds == p.label_ids).mean()}

			# Initialize our Trainer
			lowercase__					  =				TFTrainer(
			    model=SCREAMING_SNAKE_CASE_						,  args=SCREAMING_SNAKE_CASE_						,  train_dataset=SCREAMING_SNAKE_CASE_						,  eval_dataset=SCREAMING_SNAKE_CASE_						,  compute_metrics=SCREAMING_SNAKE_CASE_						,  )

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir  )

			# Evaluation
			lowercase__					  =				{}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"  )
						lowercase__					  =				trainer.evaluate()
						lowercase__					  =				os.path.join(training_args.output_dir						,  "eval_results.txt"  )

						with open(SCREAMING_SNAKE_CASE_						,  "w"  ) as writer:
									logger.info("***** Eval results *****"  )

									for key, value in result.items():
												logger.info(f'''  {key} = {value}'''  )
												writer.write(f'''{key} = {value}\n'''  )

									results.update(SCREAMING_SNAKE_CASE_  )

			return results


if __name__ == "__main__":
							main()









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

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


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

lowercase_								=     """=======
>>>>>>>
"""

lowercase_								=     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_								=     [
    # (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 (     SCREAMING_SNAKE_CASE_  ):
			return ConvertCommand(args.tfds_path						,  args.datasets_directory  )




class 				_snake_case			(					lowercase__):




				@staticmethod
				def 							A__      (						__lowercase					:						ArgumentParser						):
							lowercase__					  =				parser.add_parser(
							    "convert",					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",					)
							train_parser.add_argument(
							    "--tfds_path",					type=__lowercase,					required=__lowercase,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",					)
							train_parser.add_argument(
							    "--datasets_directory",					type=__lowercase,					required=__lowercase,					help="Path to the HuggingFace Datasets folder."						)
							train_parser.set_defaults(func=__lowercase						)




				def __init__(						self					:						Tuple,					__lowercase					:						str,					__lowercase					:						str,					*__lowercase					:						Tuple						):
							lowercase__					  =				get_logger("datasets-cli/converting"						)

							lowercase__					  =				tfds_path
							lowercase__					  =				datasets_directory




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

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

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

							lowercase__					  =				[]
							lowercase__					  =				[]
							lowercase__					  =				{}

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

							for f_name in file_names:
										self._logger.info(F'''Looking at file {f_name}'''						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)

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

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

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

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

										if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__					  =				f_name.replace(".py",					""						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													os.makedirs(__lowercase,					exist_ok=__lowercase						)
													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(__lowercase						)

										if needs_manual_update:
													with_manual_update.append(__lowercase						)

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

							for utils_file in utils_files:
										try:
													lowercase__					  =				os.path.basename(__lowercase						)
													lowercase__					  =				imports_to_builder_map[f_name.replace(".py",					""						)]
													self._logger.info(F'''Moving {dest_folder} to {utils_file}'''						)
													shutil.copy(__lowercase,					__lowercase						)
										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\'.'''						)




