
"""simple docstring"""






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

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


__UpperCAmelCase       =  '<<<<<<< This should probably be modified because it mentions: '

__UpperCAmelCase       =  '=======\n>>>>>>>\n'

__UpperCAmelCase       =  [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

__UpperCAmelCase       =  [
    # (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 						_snake_case       (	lowercase__		:  Union[str, Any]		)  ->				List[Any]:


			'''simple docstring'''





			return ConvertCommand(args.tfds_path      ,     args.datasets_directory		)




class 			_SCREAMING_SNAKE_CASE					(			UpperCamelCase_							):






			@staticmethod
			def  __lowerCAmelCase (					__A       )			->     Optional[int]:
						lowerCAmelCase_    :Dict			     =  parser.add_parser(
						    """convert"""       ,   help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""       ,   )
						train_parser.add_argument(
						    """--tfds_path"""       ,   type=UpperCamelCase__       ,   required=UpperCamelCase__       ,   help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""       ,   )
						train_parser.add_argument(
						    """--datasets_directory"""       ,   type=UpperCamelCase__       ,   required=UpperCamelCase__       ,   help="""Path to the HuggingFace Datasets folder."""       )
						train_parser.set_defaults(func=UpperCamelCase__       )






			def __init__(					self       ,   __A       ,   __A       ,   *__A       )			->     List[str]:
						lowerCAmelCase_    :Union[str, Any]			     =  get_logger("""datasets-cli/converting"""       )

						lowerCAmelCase_    :Tuple			     =  tfds_path
						lowerCAmelCase_    :List[Any]			     =  datasets_directory






			def  __lowerCAmelCase (					self       )			->     Any:





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

						lowerCAmelCase_    :str			     =  os.path.abspath(self._datasets_directory       )

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

						lowerCAmelCase_    :int			     =  []
						lowerCAmelCase_    :str			     =  []
						lowerCAmelCase_    :Tuple			     =  {}

						if os.path.isdir(self._tfds_path       ):
									lowerCAmelCase_    :Dict			     =  os.listdir(UpperCamelCase__       )
						else:
									lowerCAmelCase_    :Dict			     =  [os.path.basename(self._tfds_path       )]

						for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""       )
									lowerCAmelCase_    :Any			     =  os.path.join(UpperCamelCase__       ,   UpperCamelCase__       )
									lowerCAmelCase_    :Dict			     =  os.path.join(UpperCamelCase__       ,   UpperCamelCase__       )

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

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

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

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

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

									if needs_manual_update:
												with_manual_update.append(UpperCamelCase__       )

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

						for utils_file in utils_files:
									try:
												lowerCAmelCase_    :Dict			     =  os.path.basename(UpperCamelCase__       )
												lowerCAmelCase_    :Dict			     =  imports_to_builder_map[f_name.replace(""".py"""       ,   """"""       )]
												self._logger.info(f"""Moving {dest_folder} to {utils_file}"""       )
												shutil.copy(UpperCamelCase__       ,   UpperCamelCase__       )
									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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


__UpperCamelCase		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

																	SCREAMING_SNAKE_CASE							:    Any							=							tf.cast(input_ids_dense > 0    ,		tf.intaa						)
																	# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





@require_tf
@require_keras_nlp
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[GPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												SCREAMING_SNAKE_CASE							:    List[str]							=							[TFGPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers						) == len(self.tf_tokenizers						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							[
												    '''This is a straightforward English test sentence.''',
												    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
												    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
												    '''And some much more rare Chinese: 齉 堃 齉堃''',
												    '''Je vais aussi écrire en français pour tester les accents''',
												    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer([test_inputs]    ,		return_tensors='''tf'''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							tf_tokenizer([test_inputs]						)

																								for key in python_outputs.keys():
																														# convert them to numpy to avoid messing with ragged tensors
																														SCREAMING_SNAKE_CASE							:    int							=							python_outputs[key].numpy()
																														SCREAMING_SNAKE_CASE							:    int							=							tf_outputs[key].numpy()

																														self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
																														self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__    ,		tf.intaa						) == tf_outputs_values						)						)


						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

																								for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)


						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    str							=							ModelToSave(tokenizer=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.serving(UpperCamelCase__						)  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__    ,		UpperCamelCase__    ,		signatures={'''serving_default''': model.serving}						)
																								SCREAMING_SNAKE_CASE							:    str							=							tf.saved_model.load(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    int							=							loaded_model.signatures['''serving_default'''](UpperCamelCase__						)['''output_0''']
																		# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																		self.assertTrue(tf.reduce_all(out == loaded_output						)						)


						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__						)  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

																		for key in from_config_output.keys():
																								self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]						)						)



						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





