





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase = None					,  lowercase = None					,  lowercase = None					,  lowercase = False					,  lowercase = False					,  lowercase = None					,  lowercase = None					,  **lowercase					,  )			->						Optional[int]:







          '''simple docstring'''

          super().__init__(
              lowercase					,  split=lowercase					,  features=lowercase					,  cache_dir=lowercase					,  keep_in_memory=lowercase					,  streaming=lowercase					,  num_proc=lowercase					,  **lowercase					,  )
          a__						:	List[str] =				field
          a__						:	List[Any] =				path_or_paths if isinstance(lowercase					,  lowercase) else {self.split: path_or_paths}
          a__						:	Dict =				Json(
              cache_dir=lowercase					,  data_files=lowercase					,  features=lowercase					,  field=lowercase					,  **lowercase					,  )

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          if self.streaming:
                 a__						:	Any =				self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
                 a__						:	Tuple =				None
                 a__						:	List[str] =				None
                 a__						:	Union[str, Any] =				None
                 a__						:	Any =				None

                 self.builder.download_and_prepare(
                     download_config=lowercase					,  download_mode=lowercase					,  verification_mode=lowercase					,  base_path=lowercase					,  num_proc=self.num_proc					,  )
                 a__						:	str =				self.builder.as_dataset(
                     split=self.split					,  verification_mode=lowercase					,  in_memory=self.keep_in_memory)
          return dataset





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase					,  lowercase = None					,  lowercase = None					,  **lowercase					,  )			->						Optional[int]:







          '''simple docstring'''

          if num_proc is not None and num_proc <= 0:
                 raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

          a__						:	List[Any] =				dataset
          a__						:	Optional[Any] =				path_or_buf
          a__						:	Optional[int] =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          a__						:	int =				num_proc
          a__						:	Optional[Any] =				'utf-8'
          a__						:	Tuple =				to_json_kwargs

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	int =				self.to_json_kwargs.pop('path_or_buf'					,  lowercase)
          a__						:	List[Any] =				self.to_json_kwargs.pop('orient'					,  'records')
          a__						:	str =				self.to_json_kwargs.pop('lines'					,  True if orient == 'records' else False)
          a__						:	List[Any] =				self.to_json_kwargs.pop('index'					,  False if orient in ['split', 'table'] else True)
          a__						:	str =				self.to_json_kwargs.pop('compression'					,  lowercase)

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                 raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

          if isinstance(self.path_or_buf					,  (str, bytes, os.PathLike)):
                 with fsspec.open(self.path_or_buf					,  'wb'					,  compression=lowercase) as buffer:
                        a__						:	str =				self._write(file_obj=lowercase					,  orient=lowercase					,  lines=lowercase					,  index=lowercase					,  **self.to_json_kwargs)
          else:
                 if compression:
                        raise NotImplementedError(
                            F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                            ' was passed. Please provide a local path instead.')
                 a__						:	str =				self._write(
                     file_obj=self.path_or_buf					,  orient=lowercase					,  lines=lowercase					,  index=lowercase					,  **self.to_json_kwargs)
          return written

   def 							__lowercase		(					self					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Optional[int] =				args

          a__						:	Tuple =				query_table(
              table=self.dataset.data					,  key=slice(lowercase					,  offset + self.batch_size)					,  indices=self.dataset._indices					,  )
          a__						:	List[Any] =				batch.to_pandas().to_json(
              path_or_buf=lowercase					,  orient=lowercase					,  lines=lowercase					,  index=lowercase					,  **lowercase)
          if not json_str.endswith('\n'):
                 json_str += "\n"
          return json_str.encode(self.encoding)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  **lowercase					,  )			->						int:







          '''simple docstring'''

          a__						:	Optional[Any] =				0

          if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0					,  len(self.dataset)					,  self.batch_size)					,  unit='ba'					,  disable=not logging.is_progress_bar_enabled()					,  desc='Creating json from Arrow format'					,  ):
                        a__						:	Any =				self._batch_json((offset, orient, lines, index, to_json_kwargs))
                        written += file_obj.write(lowercase)
          else:
                 a__ ,							a__						:	List[Any] =				len(self.dataset), self.batch_size
                 with multiprocessing.Pool(self.num_proc) as pool:
                        for json_str in logging.tqdm(
                            pool.imap(
                                self._batch_json					,  [(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,  lowercase					,  lowercase)]					,  )					,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,  unit='ba'					,  disable=not logging.is_progress_bar_enabled()					,  desc='Creating json from Arrow format'					,  ):
                               written += file_obj.write(lowercase)

          return written



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,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



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



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

        _A        =  [Image.fromarray(np.moveaxis(lowerCAmelCase_		,				0		,				-1							)							) for x in image_inputs]

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_image_processor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

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

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				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      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

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

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)

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



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)




