


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

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


__UpperCamelCase				:  Optional[Any]      					=							'<<<<<<< This should probably be modified because it mentions: '

__UpperCamelCase				:  Union[str, Any]      					=							'=======\n>>>>>>>\n'

__UpperCamelCase				:  int      					=							[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

__UpperCamelCase				:  Union[str, 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_						( __lowercase       ):
   return ConvertCommand(args.tfds_path			,       args.datasets_directory       )





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    @staticmethod
    def 					_lowerCamelCase (	__snake_case				:   ArgumentParser	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[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				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   str       ,   *__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   get_logger('''datasets-cli/converting'''	)

       UpperCAmelCase_							:      Tuple						=   tfds_path
       UpperCAmelCase_							:      Tuple						=   datasets_directory





    def 					_lowerCamelCase (	self				:   Any	):





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

       UpperCAmelCase_							:      Tuple						=   os.path.abspath(self._datasets_directory	)

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

       UpperCAmelCase_							:      Optional[int]						=   []
       UpperCAmelCase_							:      Any						=   []
       UpperCAmelCase_							:      Optional[Any]						=   {}

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

       for f_name in file_names:
          self._logger.info(f'''Looking at file {f_name}'''	)
          UpperCAmelCase_							:      int						=   os.path.join(__snake_case       ,   __snake_case	)
          UpperCAmelCase_							:      Tuple						=   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:
             UpperCAmelCase_							:      Optional[Any]						=   f.readlines()

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

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



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase				:  str      					=							'true'
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=1_6       ):
   set_seed(4_2       )
   UpperCAmelCase_							:      Optional[int]						=   RegressionModel()
   UpperCAmelCase_							:      Optional[int]						=   deepcopy(__lowercase       )
   UpperCAmelCase_							:      Union[str, Any]						=   RegressionDataset(length=__lowercase       )
   UpperCAmelCase_							:      Any						=   DataLoader(__lowercase			,       batch_size=__lowercase       )
   model.to(accelerator.device       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return model, ddp_model, dataloader
def      snake_case_						( __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      Optional[int]						=   AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''       )
   UpperCAmelCase_							:      List[Any]						=   load_dataset('''glue'''			,       '''mrpc'''			,       split='''validation'''       )

   def tokenize_function(__lowercase       ):
      UpperCAmelCase_							:      int						=   tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=__lowercase			,       max_length=__lowercase       )
      return outputs

   with accelerator.main_process_first():
      UpperCAmelCase_							:      List[str]						=   dataset.map(
          __lowercase			,       batched=__lowercase			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       )

   UpperCAmelCase_							:      Any						=   tokenized_datasets.rename_column('''label'''			,       '''labels'''       )

   def collate_fn(__lowercase       ):
      if use_longest:
         return tokenizer.pad(__lowercase			,       padding='''longest'''			,       return_tensors='''pt'''       )
      return tokenizer.pad(__lowercase			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''       )

   return DataLoader(__lowercase			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=1_6       )
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[int]						=   Accelerator(dispatch_batches=__lowercase			,       split_batches=__lowercase       )
   UpperCAmelCase_							:      int						=   get_dataloader(__lowercase			,       not dispatch_batches       )
   UpperCAmelCase_							:      Optional[int]						=   AutoModelForSequenceClassification.from_pretrained(
       '''hf-internal-testing/mrpc-bert-base-cased'''			,       return_dict=__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Dict						=   []
   for batch in dataloader:
      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   batch.values()
      with torch.no_grad():
         UpperCAmelCase_							:      List[Any]						=   model(__lowercase       )
         UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.gather_for_metrics((logit, target)       )
         logits_and_targets.append((logit, target)       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   [], []
   for logit, targ in logits_and_targets:
      logits.append(__lowercase       )
      targs.append(__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   torch.cat(__lowercase       ), torch.cat(__lowercase       )
   return logits, targs
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=False			,       __lowercase=False			,       __lowercase=1_6       ):
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   get_basic_setup(__lowercase			,       __lowercase			,       __lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   generate_predictions(__lowercase			,       __lowercase			,       __lowercase       )
   assert (
       len(__lowercase       ) == num_samples
   ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowercase       )}'''
def      snake_case_						( __lowercase = False			,       __lowercase = False       ):
   UpperCAmelCase_							:      Optional[Any]						=   evaluate.load('''glue'''			,       '''mrpc'''       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   get_mrpc_setup(__lowercase			,       __lowercase       )
   # First do baseline
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   setup['''no''']
   model.to(__lowercase       )
   model.eval()
   for batch in dataloader:
      batch.to(__lowercase       )
      with torch.inference_mode():
         UpperCAmelCase_							:      str						=   model(**__lowercase       )
      UpperCAmelCase_							:      Dict						=   outputs.logits.argmax(dim=-1       )
      metric.add_batch(predictions=__lowercase			,       references=batch['''labels''']       )
   UpperCAmelCase_							:      Optional[int]						=   metric.compute()

   # Then do distributed
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         UpperCAmelCase_							:      Optional[int]						=   model(**__lowercase       )
      UpperCAmelCase_							:      int						=   outputs.logits.argmax(dim=-1       )
      UpperCAmelCase_							:      Optional[int]						=   batch['''labels''']
      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   accelerator.gather_for_metrics((preds, references)       )
      metric.add_batch(predictions=__lowercase			,       references=__lowercase       )
   UpperCAmelCase_							:      Dict						=   metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]			,       distributed[key]       ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print('''**Testing gather_for_metrics**'''       )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''       )
            test_mrpc(__lowercase			,       __lowercase       )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test torch metrics**'''       )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         UpperCAmelCase_							:      Optional[Any]						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
         if accelerator.is_local_main_process:
            print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''       )
         test_torch_metrics(__lowercase			,       9_9       )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''       )
   UpperCAmelCase_							:      List[Any]						=   Accelerator()
   test_torch_metrics(__lowercase			,       5_1_2       )
   accelerator.state._reset_state()
def      snake_case_						( __lowercase       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()