


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				tempfile.mkdtemp()

          UpperCAmelCase__			:		Any													=				[
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          UpperCAmelCase__			:		List[Any]													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''vocab_file'''] )
          with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8''' ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

          UpperCAmelCase__			:		Tuple													=				{
              '''do_resize''': True,
              '''size''': 20,
              '''do_center_crop''': True,
              '''crop_size''': 18,
              '''do_normalize''': True,
              '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
          }
          UpperCAmelCase__			:		Dict													=				os.path.join(self.tmpdirname						,						_A )
          with open(self.image_processor_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              json.dump(_A						,						_A )






      def lowercase_							(					self				:    int						,						**_A				:    Dict ):


          '''simple docstring'''


          return BertTokenizer.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    int						,						**_A				:    int ):


          '''simple docstring'''


          return BertTokenizerFast.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    List[Any]						,						**_A				:    Optional[int] ):


          '''simple docstring'''


          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				[np.random.randint(255						,						size=(3, 30, 400)						,						dtype=np.uinta )]
          UpperCAmelCase__			:		Union[str, Any]													=				[Image.fromarray(np.moveaxis(_A						,						0						,						-1 ) ) for x in image_inputs]
          return image_inputs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_tokenizer()
          UpperCAmelCase__			:		int													=				self.get_rust_tokenizer()
          UpperCAmelCase__			:		List[Any]													=				self.get_image_processor()

          UpperCAmelCase__			:		Dict													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )
          processor_slow.save_pretrained(self.tmpdirname )
          UpperCAmelCase__			:		str													=				AlignProcessor.from_pretrained(self.tmpdirname						,						use_fast=_A )

          UpperCAmelCase__			:		str													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )
          processor_fast.save_pretrained(self.tmpdirname )
          UpperCAmelCase__			:		Optional[int]													=				AlignProcessor.from_pretrained(self.tmpdirname )

          self.assertEqual(processor_slow.tokenizer.get_vocab()						,						tokenizer_slow.get_vocab() )
          self.assertEqual(processor_fast.tokenizer.get_vocab()						,						tokenizer_fast.get_vocab() )
          self.assertEqual(tokenizer_slow.get_vocab()						,						tokenizer_fast.get_vocab() )
          self.assertIsInstance(processor_slow.tokenizer						,						_A )
          self.assertIsInstance(processor_fast.tokenizer						,						_A )

          self.assertEqual(processor_slow.image_processor.to_json_string()						,						image_processor.to_json_string() )
          self.assertEqual(processor_fast.image_processor.to_json_string()						,						image_processor.to_json_string() )
          self.assertIsInstance(processor_slow.image_processor						,						_A )
          self.assertIsInstance(processor_fast.image_processor						,						_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				AlignProcessor(tokenizer=self.get_tokenizer()						,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          UpperCAmelCase__			:		Optional[int]													=				self.get_tokenizer(bos_token='''(BOS)'''						,						eos_token='''(EOS)''' )
          UpperCAmelCase__			:		Tuple													=				self.get_image_processor(do_normalize=_A						,						padding_value=1.0 )

          UpperCAmelCase__			:		int													=				AlignProcessor.from_pretrained(
              self.tmpdirname						,						bos_token='''(BOS)'''						,						eos_token='''(EOS)'''						,						do_normalize=_A						,						padding_value=1.0 )

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

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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self.get_image_processor()
          UpperCAmelCase__			:		Any													=				self.get_tokenizer()

          UpperCAmelCase__			:		List[Any]													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )

          UpperCAmelCase__			:		List[Any]													=				self.prepare_image_inputs()

          UpperCAmelCase__			:		Dict													=				image_processor(_A						,						return_tensors='''np''' )
          UpperCAmelCase__			:		int													=				processor(images=_A						,						return_tensors='''np''' )

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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_image_processor()
          UpperCAmelCase__			:		List[str]													=				self.get_tokenizer()

          UpperCAmelCase__			:		Union[str, Any]													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )

          UpperCAmelCase__			:		Any													=				'''lower newer'''

          UpperCAmelCase__			:		Union[str, Any]													=				processor(text=_A )

          UpperCAmelCase__			:		List[Any]													=				tokenizer(_A						,						padding='''max_length'''						,						max_length=64 )

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






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				self.get_image_processor()
          UpperCAmelCase__			:		List[str]													=				self.get_tokenizer()

          UpperCAmelCase__			:		Any													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )

          UpperCAmelCase__			:		int													=				'''lower newer'''
          UpperCAmelCase__			:		Optional[Any]													=				self.prepare_image_inputs()

          UpperCAmelCase__			:		Union[str, Any]													=				processor(text=_A						,						images=_A )

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

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






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.get_image_processor()
          UpperCAmelCase__			:		List[str]													=				self.get_tokenizer()

          UpperCAmelCase__			:		Any													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )

          UpperCAmelCase__			:		Optional[int]													=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCAmelCase__			:		Union[str, Any]													=				processor.batch_decode(_A )
          UpperCAmelCase__			:		str													=				tokenizer.batch_decode(_A )

          self.assertListEqual(_A						,						_A )






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.get_image_processor()
          UpperCAmelCase__			:		List[Any]													=				self.get_tokenizer()

          UpperCAmelCase__			:		List[Any]													=				AlignProcessor(tokenizer=_A						,						image_processor=_A )

          UpperCAmelCase__			:		Optional[Any]													=				'''lower newer'''
          UpperCAmelCase__			:		List[Any]													=				self.prepare_image_inputs()

          UpperCAmelCase__			:		str													=				processor(text=_A						,						images=_A )

          self.assertListEqual(list(inputs.keys() )						,						processor.model_input_names )




'''simple docstring'''
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 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

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




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


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

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






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


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

          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=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**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.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		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'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				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						,						_A						,						_A )]						,						)						,						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(_A )

          return written
