



"""simple docstring"""




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Features							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE__      :		FeatureType							) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      List[Any]														=       min(SCREAMING_SNAKE_CASE__		,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
          elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      int														=       min(SCREAMING_SNAKE_CASE__		,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
          elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) and feature.dtype == "binary":
               _UpperCAmelCase     :      Tuple														=       min(SCREAMING_SNAKE_CASE__		,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS							)

     _visit(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     return None if batch_size is np.inf else batch_size






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __init__(	self	:      int	,      A	:      NestedDataStructureLike[PathLike]	,      A	:      Optional[NamedSplit] = None	,      A	:      Optional[Features] = None	,      A	:      str = None	,      A	:      bool = False	,      A	:      bool = False	,      A	:      Optional[int] = None	,      **A	:      Any	,      ):
           super().__init__(
               A	,      split=A	,      features=A	,      cache_dir=A	,      keep_in_memory=A	,      streaming=A	,      num_proc=A	,      **A	,      )
           _UpperCAmelCase     :      str														=       path_or_paths if isinstance(A	,      A		) else {self.split: path_or_paths}
           _UpperCAmelCase     :      int														=       _PACKAGED_DATASETS_MODULES["parquet"][1]
           _UpperCAmelCase     :      Any														=       Parquet(
               cache_dir=A	,      data_files=A	,      features=A	,      hash=A	,      **A	,      )



      def 		snake_case_   (	self	:      Optional[int]		):
           # Build iterable dataset
           if self.streaming:
                _UpperCAmelCase     :      Tuple														=       self.builder.as_streaming_dataset(split=self.split		)
           # Build regular (map-style) dataset
           else:
                _UpperCAmelCase     :      str														=       None
                _UpperCAmelCase     :      Dict														=       None
                _UpperCAmelCase     :      Dict														=       None
                _UpperCAmelCase     :      int														=       None

                self.builder.download_and_prepare(
                    download_config=A	,      download_mode=A	,      verification_mode=A	,      base_path=A	,      num_proc=self.num_proc	,      )
                _UpperCAmelCase     :      Optional[Any]														=       self.builder.as_dataset(
                    split=self.split	,      verification_mode=A	,      in_memory=self.keep_in_memory		)
           return dataset







class       UpperCAmelCase_    :



      def __init__(	self	:      List[Any]	,      A	:      Dataset	,      A	:      Union[PathLike, BinaryIO]	,      A	:      Optional[int] = None	,      **A	:      Optional[Any]	,      ):
           _UpperCAmelCase     :      Union[str, Any]														=       dataset
           _UpperCAmelCase     :      Tuple														=       path_or_buf
           _UpperCAmelCase     :      Optional[int]														=       batch_size or get_writer_batch_size(dataset.features		)
           _UpperCAmelCase     :      str														=       parquet_writer_kwargs



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[Any]														=       self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

           if isinstance(self.path_or_buf	,      (str, bytes, os.PathLike)		):
                with open(self.path_or_buf	,      "wb+"		) as buffer:
                     _UpperCAmelCase     :      Tuple														=       self._write(file_obj=A	,      batch_size=A	,      **self.parquet_writer_kwargs		)
           else:
                _UpperCAmelCase     :      Union[str, Any]														=       self._write(file_obj=self.path_or_buf	,      batch_size=A	,      **self.parquet_writer_kwargs		)
           return written



      def 		snake_case_   (	self	:      List[Any]	,      A	:      BinaryIO	,      A	:      int	,      **A	:      Tuple		):
           _UpperCAmelCase     :      str														=       0
           _UpperCAmelCase     :      List[Any]														=       parquet_writer_kwargs.pop("path_or_buf"	,      A		)
           _UpperCAmelCase     :      Tuple														=       self.dataset.features.arrow_schema

           _UpperCAmelCase     :      List[str]														=       pq.ParquetWriter(A	,      schema=A	,      **A		)

           for offset in logging.tqdm(
               range(0	,      len(self.dataset		)	,      A		)	,      unit="ba"	,      disable=not logging.is_progress_bar_enabled()	,      desc="Creating parquet from Arrow format"	,      ):
                _UpperCAmelCase     :      str														=       query_table(
                    table=self.dataset._data	,      key=slice(A	,      offset + batch_size		)	,      indices=self.dataset._indices if self.dataset._indices is not None else None	,      )
                writer.write_table(A		)
                written += batch.nbytes
           writer.close()
           return written





"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase     :      List[str]														=       ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase     :      Tuple														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Any														=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase     :      Optional[Any]														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)
           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

           _UpperCAmelCase     :      str														=       [Image.fromarray(np.moveaxis(A	,      0	,      -1		)		) for x in image_inputs]

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       CLIPSegProcessor.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 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.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 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=A	,      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 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

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

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["pixel_values", "conditional_pixel_values"]		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
