'''simple docstring'''






import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )







@require_vision
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: Dict    )     -> List[str]:
        _A								=     tempfile.mkdtemp()

        # fmt: off
        _A								=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
        # fmt: on
        _A								=     os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES['vocab_file']    )

        with open(self.vocab_file,   'w',   encoding='utf-8'    ) as fp:
             fp.write(''.join([x + '\n' for x in vocab_tokens]    )    )

        _A								=     {
            'image_mean': FLAVA_IMAGE_MEAN,
            'image_std': FLAVA_IMAGE_STD,
            'do_normalize': True,
            'do_resize': True,
            'size': 2_24,
            'do_center_crop': True,
            'crop_size': 2_24,
            'input_size_patches': 14,
            'total_mask_patches': 75,
            'mask_group_max_patches': None,
            'mask_group_min_patches': 16,
            'mask_group_min_aspect_ratio': 0.3,
            'mask_group_max_aspect_ratio': None,
            'codebook_do_resize': True,
            'codebook_size': 1_12,
            'codebook_do_center_crop': True,
            'codebook_crop_size': 1_12,
            'codebook_do_map_pixels': True,
            'codebook_do_normalize': True,
            'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
            'codebook_image_std': FLAVA_CODEBOOK_STD,
        }

        _A								=     os.path.join(self.tmpdirname,   UpperCamelCase__    )
        with open(self.image_processor_file,   'w',   encoding='utf-8'    ) as fp:
             json.dump(UpperCamelCase__,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Any,   **UpperCamelCase__			: Optional[Any]    )     -> Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: str,   **UpperCamelCase__			: Union[str, Any]    )     -> List[str]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Dict,   **UpperCamelCase__			: List[Any]    )     -> List[str]:
        return FlavaImageProcessor.from_pretrained(self.tmpdirname,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> List[Any]:
        shutil.rmtree(self.tmpdirname    )
   def   __UpperCAmelCase      (			self			: Any    )     -> Tuple:
        _A								=     [np.random.randint(2_55,   size=(3, 30, 4_00),   dtype=np.uinta    )]

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

        return image_inputs
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Tuple:
        _A								=     self.get_tokenizer()
        _A								=     self.get_rust_tokenizer()
        _A								=     self.get_image_processor()

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )
        processor_slow.save_pretrained(self.tmpdirname    )
        _A								=     FlavaProcessor.from_pretrained(self.tmpdirname,   use_fast=UpperCamelCase__    )

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )
        processor_fast.save_pretrained(self.tmpdirname    )
        _A								=     FlavaProcessor.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,   UpperCamelCase__    )
        self.assertIsInstance(processor_fast.tokenizer,   UpperCamelCase__    )

        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,   UpperCamelCase__    )
        self.assertIsInstance(processor_fast.image_processor,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: int    )     -> List[Any]:
        _A								=     FlavaProcessor(tokenizer=self.get_tokenizer(),   image_processor=self.get_image_processor()    )
        processor.save_pretrained(self.tmpdirname    )

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

        _A								=     FlavaProcessor.from_pretrained(
            self.tmpdirname,   bos_token='(BOS)',   eos_token='(EOS)',   do_normalize=UpperCamelCase__,   padding_value=1.0    )

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

        self.assertEqual(processor.image_processor.to_json_string(),   image_processor_add_kwargs.to_json_string()    )
        self.assertIsInstance(processor.image_processor,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: int    )     -> Union[str, Any]:
        _A								=     self.get_image_processor()
        _A								=     self.get_tokenizer()

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )

        _A								=     self.prepare_image_inputs()

        _A								=     image_processor(UpperCamelCase__,   return_tensors='np'    )
        _A								=     processor(images=UpperCamelCase__,   return_tensors='np'    )

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

        # With rest of the args
        random.seed(12_34    )
        _A								=     image_processor(
            UpperCamelCase__,   return_image_mask=UpperCamelCase__,   return_codebook_pixels=UpperCamelCase__,   return_tensors='np'    )
        random.seed(12_34    )
        _A								=     processor(
            images=UpperCamelCase__,   return_image_mask=UpperCamelCase__,   return_codebook_pixels=UpperCamelCase__,   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			: str    )     -> List[Any]:
        _A								=     self.get_image_processor()
        _A								=     self.get_tokenizer()

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )

        _A								=     'lower newer'

        _A								=     processor(text=UpperCamelCase__    )

        _A								=     tokenizer(UpperCamelCase__    )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],   encoded_processor[key]    )
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> str:
        _A								=     self.get_image_processor()
        _A								=     self.get_tokenizer()

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )

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

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

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

        # add extra args
        _A								=     processor(text=UpperCamelCase__,   images=UpperCamelCase__,   return_codebook_pixels=UpperCamelCase__,   return_image_mask=UpperCamelCase__    )

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

        # test if it raises when no input is passed
        with pytest.raises(UpperCamelCase__    ):
             processor()
   def   __UpperCAmelCase      (			self			: Any    )     -> Optional[Any]:
        _A								=     self.get_image_processor()
        _A								=     self.get_tokenizer()

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )

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

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

        self.assertListEqual(UpperCamelCase__,   UpperCamelCase__    )

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

        _A								=     FlavaProcessor(tokenizer=UpperCamelCase__,   image_processor=UpperCamelCase__    )

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

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

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




'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     assert dataset.num_rows == 4
     assert dataset.num_columns == 3
     assert dataset.column_names == ["col_1", "col_2", "col_3"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : List[str]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader(__snake_case      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : str      ,		__snake_case       : int							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		split=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     for split in splits:
          _A								=     dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 3
          assert dataset.column_names == ["col_1", "col_2", "col_3"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]      ,		__snake_case       : str							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader({'train': csv_path}      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : int							):
     if split:
          _A								=     {split: csv_path}
     else:
          _A								=     {'train': csv_path, 'test': csv_path}
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)



