
"""simple docstring"""

import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:

      class __lowerCAmelCase		:





           @staticmethod
           def 						UpperCAmelCase (     *__UpperCAmelCase     ,					**__UpperCAmelCase				):

            '''simple docstring'''




            pass

@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('visual-question-answering'     ,					model='hf-internal-testing/tiny-vilt-random-vqa'				)
      __UpperCamelCase      =	[
          {
              'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				),
              'question': 'How many cats are there?',
          },
          {
              'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
              'question': 'How many cats are there?',
          },
      ]
      return vqa_pipeline, examples





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	vqa_pipeline(__UpperCAmelCase     ,					top_k=1				)
      self.assertEqual(
          __UpperCAmelCase     ,					[
              [{'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}],
              [{'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}],
          ]     ,					)





     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('visual-question-answering'     ,					model='hf-internal-testing/tiny-vilt-random-vqa'				)
      __UpperCamelCase      =	'./tests/fixtures/tests_samples/COCO/000000039769.png'
      __UpperCamelCase      =	'How many cats are there?'

      __UpperCamelCase      =	vqa_pipeline(image=__UpperCAmelCase     ,					question='How many cats are there?'     ,					top_k=2				)
      self.assertEqual(
          __UpperCAmelCase     ,					[{'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}, {'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}]				)

      __UpperCamelCase      =	vqa_pipeline({'image': image, 'question': question}     ,					top_k=2				)
      self.assertEqual(
          __UpperCAmelCase     ,					[{'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}, {'score': ANY(__UpperCAmelCase				), 'answer': ANY(__UpperCAmelCase				)}]				)





     @slow
     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('visual-question-answering'     ,					model='dandelin/vilt-b32-finetuned-vqa'				)
      __UpperCamelCase      =	'./tests/fixtures/tests_samples/COCO/000000039769.png'
      __UpperCamelCase      =	'How many cats are there?'

      __UpperCamelCase      =	vqa_pipeline(image=__UpperCAmelCase     ,					question=__UpperCAmelCase     ,					top_k=2				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase     ,					decimals=4				)     ,					[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]				)

      __UpperCamelCase      =	vqa_pipeline({'image': image, 'question': question}     ,					top_k=2				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase     ,					decimals=4				)     ,					[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]				)

      __UpperCamelCase      =	vqa_pipeline(
          [{'image': image, 'question': question}, {'image': image, 'question': question}]     ,					top_k=2				)
      self.assertEqual(
          nested_simplify(__UpperCAmelCase     ,					decimals=4				)     ,					[[{'score': 0.8_7_9_9, 'answer': '2'}, {'score': 0.2_9_6, 'answer': '1'}]] * 2     ,					)







     @require_tf
     @unittest.skip('Visual question answering not implemented in TF'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





"""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		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					split=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					cache_dir=__UpperCAmelCase     ,					keep_in_memory=__UpperCAmelCase     ,					streaming=__UpperCAmelCase     ,					num_proc=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      __UpperCamelCase      =	field
      __UpperCamelCase      =	path_or_paths if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else {self.split: path_or_paths}
      __UpperCamelCase      =	Json(
          cache_dir=__UpperCAmelCase     ,					data_files=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					field=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.streaming:
       __UpperCamelCase      =	self.builder.as_streaming_dataset(split=self.split				)
      # Build regular (map-style) dataset
      else:
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase     ,					download_mode=__UpperCAmelCase     ,					verification_mode=__UpperCAmelCase     ,					base_path=__UpperCAmelCase     ,					num_proc=self.num_proc     ,					)
       __UpperCamelCase      =	self.builder.as_dataset(
           split=self.split     ,					verification_mode=__UpperCAmelCase     ,					in_memory=self.keep_in_memory				)
      return dataset


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''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      =	dataset
      __UpperCamelCase      =	path_or_buf
      __UpperCamelCase      =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      __UpperCamelCase      =	num_proc
      __UpperCamelCase      =	'utf-8'
      __UpperCamelCase      =	to_json_kwargs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.to_json_kwargs.pop('path_or_buf'     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('orient'     ,					'records'				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('lines'     ,					True if orient == 'records' else False				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('index'     ,					False if orient in ['split', 'table'] else True				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('compression'     ,					__UpperCAmelCase				)

      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=__UpperCAmelCase				) as buffer:
        __UpperCamelCase      =	self._write(file_obj=__UpperCAmelCase     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**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      =	self._write(
           file_obj=self.path_or_buf     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**self.to_json_kwargs				)
      return written





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	args

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







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	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      =	self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
        written += file_obj.write(__UpperCAmelCase				)
      else:
       __UpperCamelCase  ,     __UpperCamelCase      =	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     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)]     ,					)     ,					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(__UpperCAmelCase				)

      return written



