

"""simple docstring"""


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCAmelCase													=datasets.utils.logging.get_logger(__name__)





@dataclass
class lowerCamelCase__   (				datasets.BuilderConfig    ):



    '''simple docstring'''




    _lowerCamelCase					=   10000
    _lowerCamelCase					=   None
    _lowerCamelCase					=   None





class lowerCamelCase__   (				datasets.ArrowBasedBuilder    ):



    '''simple docstring'''




    _lowerCamelCase					=   ParquetConfig




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[Any]:



          if not self.config.data_files:
                raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'    )
          A				  =		dl_manager.download_and_extract(self.config.data_files    )
          if isinstance(lowerCamelCase_ ,(str, list, tuple)    ):
                A				  =		data_files
                if isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                      A				  =		[files]
                # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                A				  =		[dl_manager.iter_files(lowerCamelCase_    ) for file in files]
                return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files}    )]
          A				  =		[]
          for split_name, files in data_files.items():
                if isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                      A				  =		[files]
                # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                A				  =		[dl_manager.iter_files(lowerCamelCase_    ) for file in files]
                # Infer features is they are stoed in the arrow schema
                if self.info.features is None:
                      for file in itertools.chain.from_iterable(lowerCamelCase_    ):
                            with open(lowerCamelCase_ ,"""rb"""    ) as f:
                                  A				  =		datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase_    )    )
                            break
                splits.append(datasets.SplitGenerator(name=lowerCamelCase_ ,gen_kwargs={"""files""": files}    )    )
          return splits




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Any:



          if self.info.features is not None:
                # more expensive cast to support nested features with keys in a different order
                # allows str <-> int/float or str to Audio for example
                A				  =		table_cast(lowerCamelCase_ ,self.info.features.arrow_schema    )
          return pa_table




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      int:
          A				  =		self.info.features.arrow_schema if self.info.features is not None else None
          if self.info.features is not None and self.config.columns is not None:
                if sorted(field.name for field in schema    ) != sorted(self.config.columns    ):
                      raise ValueError(
                          f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''    )
          for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_    )    ):
                with open(lowerCamelCase_ ,"""rb"""    ) as f:
                      A				  =		pq.ParquetFile(lowerCamelCase_    )
                      try:
                            for batch_idx, record_batch in enumerate(
                                parquet_file.iter_batches(batch_size=self.config.batch_size ,columns=self.config.columns    )    ):
                                  A				  =		pa.Table.from_batches([record_batch]    )
                                  # Uncomment for debugging (will print the Arrow table size and elements)
                                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                  yield f'{file_idx}_{batch_idx}', self._cast_table(lowerCamelCase_    )
                      except ValueError as e:
                            logger.error(f'Failed to read file \'{file}\' with error {type(lowerCamelCase_    )}: {e}'    )
                            raise











'''simple docstring'''




import os
import sys
import unittest


__UpperCAmelCase            =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase            =       os.path.join(git_repo_path, """src""", """transformers""")

__UpperCAmelCase            =       """
{0} = None
"""

__UpperCAmelCase            =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__UpperCAmelCase            =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""						)
							self.assertIsNone(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			find_backend("""    if not is_tokenizers_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend("""    if not is_tensorflow_text_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers_and_vision"""						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""      ,						lowerCamelCase_						)
							self.assertIn("""tensorflow_text"""      ,						lowerCamelCase_						)
							self.assertIn("""sentencepiece_and_tokenizers"""      ,						lowerCamelCase_						)

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertModel"""      ,						objects["""tf"""]						)
							self.assertIn("""FlaxBertModel"""      ,						objects["""flax"""]						)
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertTokenizer"""      ,						objects["""tensorflow_text"""]						)
							self.assertIn("""convert_slow_tokenizer"""      ,						objects["""sentencepiece_and_tokenizers"""]						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			create_dummy_object("""CONSTANT"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						"""\nCONSTANT = None\n"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_object("""function"""      ,						"""'torch'"""						)
							self.assertEqual(
							    lowerCamelCase_      ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""						)

							SCREAMING_SNAKE_CASE :				str		=			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			create_dummy_object("""FakeClass"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}						)
							self.assertEqual(dummy_files["""torch"""]      ,						lowerCamelCase_						)



