
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


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

@dataclass
class   __a(					datasets.BuilderConfig       ):






    """simple docstring"""





    lowerCAmelCase        =				1_0000
    lowerCAmelCase        =				None
    lowerCAmelCase        =				None


class   __a(					datasets.ArrowBasedBuilder       ):






    """simple docstring"""





    lowerCAmelCase        =				ParquetConfig





    def 							a__				(				self  )			->		Union[str, Any]:
         return datasets.DatasetInfo(features=self.config.features  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         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}'''  )
         UpperCAmelCase_      :							List[Any]       =      dl_manager.download_and_extract(self.config.data_files  )
         if isinstance(_SCREAMING_SNAKE_CASE       ,(str, list, tuple)  ):
              UpperCAmelCase_      :							List[str]       =      data_files
              if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							Optional[Any]       =      [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              UpperCAmelCase_      :							Dict       =      [dl_manager.iter_files(_SCREAMING_SNAKE_CASE  ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,gen_kwargs={'''files''': files}  )]
         UpperCAmelCase_      :							Optional[Any]       =      []
         for split_name, files in data_files.items():
              if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							Any       =      [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              UpperCAmelCase_      :							List[str]       =      [dl_manager.iter_files(_SCREAMING_SNAKE_CASE  ) 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(_SCREAMING_SNAKE_CASE  ):
                        with open(_SCREAMING_SNAKE_CASE       ,'''rb'''  ) as f:
                             UpperCAmelCase_      :							Any       =      datasets.Features.from_arrow_schema(pq.read_schema(_SCREAMING_SNAKE_CASE  )  )
                        break
              splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE       ,gen_kwargs={'''files''': files}  )  )
         return splits





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		pa.Table:
         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
              UpperCAmelCase_      :							int       =      table_cast(_SCREAMING_SNAKE_CASE       ,self.info.features.arrow_schema  )
         return pa_table





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							List[Any]       =      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(_SCREAMING_SNAKE_CASE  )  ):
              with open(_SCREAMING_SNAKE_CASE       ,'''rb'''  ) as f:
                   UpperCAmelCase_      :							int       =      pq.ParquetFile(_SCREAMING_SNAKE_CASE  )
                   try:
                        for batch_idx, record_batch in enumerate(
                            parquet_file.iter_batches(batch_size=self.config.batch_size       ,columns=self.config.columns  )  ):
                             UpperCAmelCase_      :							str       =      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(_SCREAMING_SNAKE_CASE  )
                   except ValueError as e:
                        logger.error(f'''Failed to read file \'{file}\' with error {type(_SCREAMING_SNAKE_CASE  )}: {e}'''  )
                        raise





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      tempfile.mkdtemp()

         UpperCAmelCase_      :							str       =      BlipImageProcessor()
         UpperCAmelCase_      :							Dict       =      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )

         UpperCAmelCase_      :							Optional[Any]       =      BlipaProcessor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         processor.save_pretrained(self.tmpdirname  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).tokenizer





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).image_processor





    def 							a__				(				self  )			->		List[Any]:
         shutil.rmtree(self.tmpdirname  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Tuple       =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta  )]

         UpperCAmelCase_      :							Optional[Any]       =      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,0       ,-1  )  ) for x in image_inputs]

         return image_inputs





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      BlipaProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

         UpperCAmelCase_      :							str       =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''  )
         UpperCAmelCase_      :							int       =      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor.from_pretrained(
             self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()  )
         self.assertIsInstance(processor.tokenizer       ,_SCREAMING_SNAKE_CASE  )

         self.assertEqual(processor.image_processor.to_json_string()       ,image_processor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.image_processor       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.get_image_processor()
         UpperCAmelCase_      :							Any       =      self.get_tokenizer()

         UpperCAmelCase_      :							str       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Optional[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							int       =      processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         UpperCAmelCase_      :							Optional[int]       =      processor(text=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_token_type_ids=_SCREAMING_SNAKE_CASE  )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Tuple       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							List[str]       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )

         # test if it raises when no input is passed
         with pytest.raises(_SCREAMING_SNAKE_CASE  ):
              processor()





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.get_image_processor()
         UpperCAmelCase_      :							Dict       =      self.get_tokenizer()

         UpperCAmelCase_      :							List[str]       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_      :							List[str]       =      processor.batch_decode(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Union[str, Any]       =      self.get_image_processor()
         UpperCAmelCase_      :							int       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Any       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )