




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

UpperCAmelCase_							:	Optional[int]	     =		'''=======
>>>>>>>
'''

UpperCAmelCase_							:	Tuple	     =		[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

UpperCAmelCase_							:	Optional[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       _UpperCamelCase			(_lowerCamelCase				:  Namespace						)-> int:




     '''simple docstring'''





     return ConvertCommand(args.tfds_path ,						args.datasets_directory						)







class      lowerCAmelCase (			__lowerCAmelCase):


       @staticmethod
       def    lowerCAmelCase   ( __SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	parser.add_parser(
                '''convert'''     ,	help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''     ,	)
            train_parser.add_argument(
                '''--tfds_path'''     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_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=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help='''Path to the HuggingFace Datasets folder.'''	)
            train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE	)


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	*__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	get_logger('''datasets-cli/converting'''	)

            __snake_case            =	tfds_path
            __snake_case            =	datasets_directory




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




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

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

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

            __snake_case            =	[]
            __snake_case            =	[]
            __snake_case            =	{}

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

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

                 if not os.path.isfile(__SCREAMING_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(__SCREAMING_SNAKE_CASE     ,	encoding='''utf-8'''	) as f:
                      __snake_case            =	f.readlines()

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

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

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

                 if needs_manual_update:
                      with_manual_update.append(__SCREAMING_SNAKE_CASE	)

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

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












'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






