
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None ,     SCREAMING_SNAKE_CASE__=None    )			->   Optional[int]:
       if attention_mask is None:
              lowercase							:					int	     =				tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__ ,     config.pad_token_id    ) ,     tf.inta    )
       return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class __snake_case		:
    _a		:						int=    OPTConfig
    _a		:						List[Any]=    {}
    _a		:						Dict=    "gelu"
    def __init__(  self		,snake_case		,snake_case=13		,snake_case=7		,snake_case=True		,snake_case=False		,snake_case=99		,snake_case=16		,snake_case=2		,snake_case=4		,snake_case=4		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=20		,snake_case=2		,snake_case=1		,snake_case=0		,snake_case=16		,snake_case=16		,):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				parent
           lowercase							:					Optional[Any]	     =				batch_size
           lowercase							:					List[Any]	     =				seq_length
           lowercase							:					List[Any]	     =				is_training
           lowercase							:					List[Any]	     =				use_labels
           lowercase							:					Any	     =				vocab_size
           lowercase							:					Dict	     =				hidden_size
           lowercase							:					Dict	     =				num_hidden_layers
           lowercase							:					str	     =				num_attention_heads
           lowercase							:					Any	     =				intermediate_size
           lowercase							:					int	     =				hidden_act
           lowercase							:					Optional[Any]	     =				hidden_dropout_prob
           lowercase							:					Union[str, Any]	     =				attention_probs_dropout_prob
           lowercase							:					Optional[Any]	     =				max_position_embeddings
           lowercase							:					Optional[Any]	     =				eos_token_id
           lowercase							:					List[Any]	     =				pad_token_id
           lowercase							:					Tuple	     =				bos_token_id
           lowercase							:					Any	     =				embed_dim
           lowercase							:					Dict	     =				word_embed_proj_dim
           lowercase							:					List[str]	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				ids_tensor([self.batch_size, self.seq_length - 1]		,self.vocab_size				)
           lowercase							:					Optional[int]	     =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,1				)
           lowercase							:					str	     =				tf.concat([input_ids, eos_tensor]		,axis=1				)

           lowercase							:					List[str]	     =				self.config_cls(
               vocab_size=self.vocab_size		,hidden_size=self.hidden_size		,num_hidden_layers=self.num_hidden_layers		,num_attention_heads=self.num_attention_heads		,ffn_dim=self.intermediate_size		,dropout=self.hidden_dropout_prob		,attention_dropout=self.attention_probs_dropout_prob		,max_position_embeddings=self.max_position_embeddings		,eos_token_id=self.eos_token_id		,bos_token_id=self.bos_token_id		,pad_token_id=self.pad_token_id		,embed_dim=self.embed_dim		,word_embed_proj_dim=self.word_embed_proj_dim		,is_encoder_decoder=snake_case		,**self.config_updates		,)
           lowercase							:					int	     =				prepare_opt_inputs_dict(snake_case		,snake_case				)
           return config, inputs_dict
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				TFOPTModel(config=snake_case				)
           lowercase							:					str	     =				inputs_dict["""input_ids"""]

           lowercase							:					Optional[int]	     =				input_ids[:1, :]
           lowercase							:					List[str]	     =				inputs_dict["""attention_mask"""][:1, :]
           lowercase							:					List[str]	     =				1

           # first forward pass
           lowercase							:					Tuple	     =				model(snake_case		,attention_mask=snake_case		,use_cache=snake_case				)

           lowercase     ,      lowercase							:					int	     =				outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           lowercase							:					Dict	     =				ids_tensor((self.batch_size, 3)		,config.vocab_size				)
           lowercase							:					List[Any]	     =				tf.cast(ids_tensor((self.batch_size, 3)		,2				)		,tf.inta				)

           # append to next input_ids and
           lowercase							:					Union[str, Any]	     =				tf.concat([input_ids, next_tokens]		,axis=-1				)
           lowercase							:					Optional[int]	     =				tf.concat([attention_mask, next_attn_mask]		,axis=-1				)

           lowercase							:					int	     =				model(snake_case		,attention_mask=snake_case				)[0]
           lowercase							:					Any	     =				model(snake_case		,attention_mask=snake_case		,past_key_values=snake_case				)[0]

           self.parent.assertEqual(next_tokens.shape[1]		,output_from_past.shape[1]				)

           # select random slice
           lowercase							:					Union[str, Any]	     =				int(ids_tensor((1,)		,output_from_past.shape[-1]				)				)
           lowercase							:					str	     =				output_from_no_past[:, -3:, random_slice_idx]
           lowercase							:					int	     =				output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(snake_case		,snake_case		,rtol=1e-3				)






@require_tf
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						str=    (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
    _a		:						Tuple=    (TFOPTForCausalLM,) if is_tf_available() else ()
    _a		:						int=    (
        {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
    )
    _a		:						List[Any]=    False
    _a		:						int=    False
    _a		:						Optional[int]=    False
    _a		:						List[str]=    10
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				TFOPTModelTester(self				)
           lowercase							:					Dict	     =				ConfigTester(self		,config_class=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(snake_case		,snake_case				):
                  if hasattr(snake_case		,"""weight"""				):
                         return embedding_layer.weight
                  else:
                         # Here we build the word embeddings weights if not exists.
                         # And then we retry to get the attribute once built.
                         model.build()
                         if hasattr(snake_case		,"""weight"""				):
                                return embedding_layer.weight
                         else:
                                return None

           for model_class in self.all_model_classes:
                  for size in [config.vocab_size - 10, config.vocab_size + 10]:
                         # build the embeddings
                         lowercase							:					int	     =				model_class(config=snake_case				)
                         lowercase							:					Union[str, Any]	     =				_get_word_embedding_weight(snake_case		,model.get_input_embeddings()				)
                         lowercase							:					int	     =				_get_word_embedding_weight(snake_case		,model.get_output_embeddings()				)

                         # reshape the embeddings
                         model.resize_token_embeddings(snake_case				)
                         lowercase							:					Optional[Any]	     =				_get_word_embedding_weight(snake_case		,model.get_input_embeddings()				)
                         lowercase							:					str	     =				_get_word_embedding_weight(snake_case		,model.get_output_embeddings()				)

                         # check that the resized embeddings size matches the desired size.
                         lowercase							:					int	     =				size if size is not None else config.vocab_size

                         self.assertEqual(new_input_embeddings.shape[0]		,snake_case				)

                         # check that weights remain the same after resizing
                         lowercase							:					Optional[int]	     =				True
                         for pa, pa in zip(old_input_embeddings.value()		,new_input_embeddings.value()				):
                                if tf.math.reduce_sum(tf.math.abs(pa - pa				)				) > 0:
                                       lowercase							:					Any	     =				False
                         self.assertTrue(snake_case				)

                         if old_output_embeddings is not None and new_output_embeddings is not None:
                                self.assertEqual(new_output_embeddings.shape[0]		,snake_case				)

                                lowercase							:					Optional[Any]	     =				True
                                for pa, pa in zip(old_output_embeddings.value()		,new_output_embeddings.value()				):
                                       if tf.math.reduce_sum(tf.math.abs(pa - pa				)				) > 0:
                                              lowercase							:					Any	     =				False
                                self.assertTrue(snake_case				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Tuple:
       return tf.constant(SCREAMING_SNAKE_CASE__ ,     dtype=tf.intaa    )

@require_tf
class __snake_case		(				unittest.TestCase					):
    _a		:						Any=    99
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				tf.ones((4, 1)		,dtype=tf.intaa				) * 2
           lowercase							:					Union[str, Any]	     =				tf.concat([ids_tensor((4, 6)		,self.vocab_size - 3				) + 3, eos_column_vector]		,axis=1				)
           lowercase							:					str	     =				input_ids.shape[0]
           lowercase							:					Optional[int]	     =				OPTConfig(
               vocab_size=self.vocab_size		,hidden_size=24		,num_hidden_layers=2		,num_attention_heads=2		,ffn_dim=32		,max_position_embeddings=48		,eos_token_id=2		,pad_token_id=1		,bos_token_id=0		,)
           return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class __snake_case		(				unittest.TestCase					):
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				TFOPTModel.from_pretrained("""facebook/opt-350m"""				)
           lowercase							:					Union[str, Any]	     =				_long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
           lowercase							:					Dict	     =				tf.not_equal(snake_case		,model.config.pad_token_id				)
           with tf.GradientTape():
                  lowercase							:					List[str]	     =				model(input_ids=snake_case		,attention_mask=snake_case				).last_hidden_state
           lowercase							:					str	     =				(1, 11, 512)
           self.assertEqual(output.shape		,snake_case				)
           lowercase							:					Any	     =				tf.constant(
               [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]				)
           self.assertTrue(np.allclose(output[:, :3, :3]		,snake_case		,atol=4e-3				)				)

           lowercase							:					Any	     =				tf.function(snake_case		,jit_compile=snake_case				)
           lowercase							:					Optional[Any]	     =				xla_generate(snake_case		,snake_case				)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]		,snake_case		,atol=4e-2				)				)






@require_tf
@slow
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Union[str, Any]	     =				"""facebook/opt-350m"""
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				TFOPTForCausalLM.from_pretrained(self.path_model				)
           lowercase							:					str	     =				GPTaTokenizer.from_pretrained(self.path_model				)

           lowercase							:					str	     =				[
               """Today is a beautiful day and I want to""",
               """In the city of""",
               """Paris is the capital of France and""",
               """Computers and mobile phones have taken""",
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           lowercase							:					Union[str, Any]	     =				tokenizer(snake_case		,return_tensors="""tf"""		,padding=snake_case		,add_special_tokens=snake_case				)
           lowercase							:					List[Any]	     =				tf.math.reduce_mean(model(inputs.input_ids		,attention_mask=inputs.attention_mask				)[0]		,axis=-1				)
           lowercase							:					Dict	     =				tf.constant(
               [
                   [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                   [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                   [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                   [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
               ]				)
           self.assertTrue(np.allclose(snake_case		,snake_case		,atol=1e-4				)				)

           lowercase							:					Union[str, Any]	     =				tf.function(snake_case		,jit_compile=snake_case				)
           lowercase							:					List[Any]	     =				tf.math.reduce_mean(xla_generate(inputs.input_ids		,attention_mask=inputs.attention_mask				)[0]		,axis=-1				)
           self.assertTrue(np.allclose(snake_case		,snake_case		,atol=1e-4				)				)






@require_tf
@slow
class __snake_case		(				unittest.TestCase					):
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				"""facebook/opt-125m"""

           lowercase							:					Any	     =				[
               """Today is a beautiful day and I want to""",
               """In the city of New York, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowercase							:					Optional[int]	     =				[]
           lowercase							:					int	     =				GPTaTokenizer.from_pretrained(snake_case				)
           lowercase							:					Any	     =				TFOPTForCausalLM.from_pretrained(snake_case				)

           for prompt in self.prompts:
                  lowercase							:					Optional[Any]	     =				tokenizer(snake_case		,return_tensors="""tf"""				).input_ids

                  lowercase							:					List[str]	     =				model.generate(snake_case		,max_length=10				)

                  lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case		,skip_special_tokens=snake_case				)
                  predicted_outputs += generated_string

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				"""facebook/opt-350m"""

           lowercase							:					List[Any]	     =				GPTaTokenizer.from_pretrained(snake_case				)
           lowercase							:					Tuple	     =				TFOPTForCausalLM.from_pretrained(snake_case				)

           lowercase							:					Dict	     =				"""left"""

           # use different length sentences to test batching
           lowercase							:					Optional[int]	     =				[
               """Hello, my dog is a little""",
               """Today, I""",
           ]

           lowercase							:					int	     =				tokenizer(snake_case		,return_tensors="""tf"""		,padding=snake_case				)
           lowercase							:					Union[str, Any]	     =				inputs["""input_ids"""]

           lowercase							:					Tuple	     =				model.generate(input_ids=snake_case		,attention_mask=inputs["""attention_mask"""]				)

           lowercase							:					Any	     =				tokenizer(sentences[0]		,return_tensors="""tf"""				).input_ids
           lowercase							:					Optional[Any]	     =				model.generate(input_ids=snake_case				)

           lowercase							:					Optional[int]	     =				inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs["""attention_mask"""][-1]		,tf.intaa				)				)
           lowercase							:					Dict	     =				tokenizer(sentences[1]		,return_tensors="""tf"""				).input_ids
           lowercase							:					Dict	     =				model.generate(input_ids=snake_case		,max_length=model.config.max_length - num_paddings				)

           lowercase							:					Union[str, Any]	     =				tokenizer.batch_decode(snake_case		,skip_special_tokens=snake_case				)
           lowercase							:					Optional[Any]	     =				tokenizer.decode(output_non_padded[0]		,skip_special_tokens=snake_case				)
           lowercase							:					int	     =				tokenizer.decode(output_padded[0]		,skip_special_tokens=snake_case				)

           lowercase							:					Any	     =				[
               """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
               """Today, I was in the middle of a conversation with a friend about the""",
           ]
           self.assertListEqual(snake_case		,snake_case				)
           self.assertListEqual(snake_case		,[non_padded_sentence, padded_sentence]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				"""facebook/opt-350m"""

           lowercase							:					str	     =				[
               """Today is a beautiful day and I want to""",
               """In the city of San Francisco, the city""",
               """Paris is the capital of France and the capital""",
               """Computers and mobile phones have taken over the""",
           ]

           lowercase							:					int	     =				[]
           lowercase							:					int	     =				GPTaTokenizer.from_pretrained(snake_case				)
           lowercase							:					Union[str, Any]	     =				TFOPTForCausalLM.from_pretrained(snake_case				)

           for prompt in self.prompts:
                  lowercase							:					Optional[int]	     =				tokenizer(snake_case		,return_tensors="""tf"""				).input_ids

                  lowercase							:					int	     =				model.generate(snake_case		,max_length=10				)

                  lowercase							:					Optional[Any]	     =				tokenizer.batch_decode(snake_case		,skip_special_tokens=snake_case				)
                  predicted_outputs += generated_string

           self.assertListEqual(snake_case		,snake_case				)


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

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


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

lowercase  : Any						   =							"""=======
>>>>>>>
"""

lowercase  : List[str]						   =							[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase  : Any						   =							[
    # (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(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       return ConvertCommand(args.tfds_path ,     args.datasets_directory    )




class __snake_case		(				lowerCAmelCase					):
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				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		,snake_case		,snake_case		,*snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				get_logger("""datasets-cli/converting"""				)

           lowercase							:					Optional[int]	     =				tfds_path
           lowercase							:					Dict	     =				datasets_directory
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           lowercase							:					Optional[int]	     =				os.path.abspath(self._datasets_directory				)

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

           lowercase							:					List[Any]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Dict	     =				{}

           if os.path.isdir(self._tfds_path				):
                  lowercase							:					int	     =				os.listdir(snake_case				)
           else:
                  lowercase							:					List[Any]	     =				[os.path.basename(self._tfds_path				)]

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

                  lowercase							:					Union[str, Any]	     =				[]
                  lowercase							:					Optional[Any]	     =				False
                  lowercase							:					Optional[Any]	     =				False
                  lowercase							:					Optional[int]	     =				[]
                  for line in lines:
                         lowercase							:					int	     =				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							:					Union[str, Any]	     =				"""import datasets\n"""
                         elif "import tensorflow" in out_line:
                                # order is important here
                                lowercase							:					List[Any]	     =				""""""
                                continue
                         elif "from absl import logging" in out_line:
                                lowercase							:					Optional[int]	     =				"""from datasets import logging\n"""
                         elif "getLogger" in out_line:
                                lowercase							:					Any	     =				out_line.replace("""getLogger"""		,"""get_logger"""				)
                         elif any(expression in out_line for expression in TO_HIGHLIGHT				):
                                lowercase							:					Optional[Any]	     =				True
                                lowercase							:					Optional[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:
                                       lowercase							:					Union[str, Any]	     =				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:
                                lowercase							:					Dict	     =				re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""		,snake_case				)
                                tfds_imports.extend(imp.strip() for imp in match.group(1				).split(""","""				)				)
                                lowercase							:					Optional[int]	     =				"""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							:					Any	     =				True
                         out_lines.append(snake_case				)

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         lowercase							:					Union[str, Any]	     =				f_name.replace(""".py"""		,""""""				)
                         lowercase							:					Optional[Any]	     =				os.path.join(snake_case		,snake_case				)
                         lowercase							:					List[str]	     =				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:
                         lowercase							:					Optional[int]	     =				os.path.basename(snake_case				)
                         lowercase							:					int	     =				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'."				)
