





'''simple docstring'''







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					lowerCAmelCase_   :       Any       =     """pt"""
elif is_tf_available():
					lowerCAmelCase_   :       Optional[Any]       =     """tf"""
else:
					lowerCAmelCase_   :       int       =     """jax"""
class 							__SCREAMING_SNAKE_CASE    (__SCREAMING_SNAKE_CASE			,				unittest.TestCase ):






				"""simple docstring"""







				__a  =PerceiverTokenizer
				__a  =False




				def     UpperCamelCase__  (    self	:   Any   ):
								super().setUp()
								_a				      =					PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname   )




				@cached_property
				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ,							**__a	:   Optional[int]   ):
								return self.tokenizer_class.from_pretrained(self.tmpdirname   ,							**_A   )




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   str   ,							__a	:   Dict=False   ,							__a	:   Union[str, Any]=20   ,							__a	:   int=5   ):
								_a				      =					[]
								for i in range(len(_A   )   ):
												try:
																_a				      =					tokenizer.decode([i]   ,							clean_up_tokenization_spaces=_A   )
												except UnicodeDecodeError:
																pass
												toks.append((i, tok)   )

								_a				      =					list(filter(lambda __a   : re.match(r"^[ a-zA-Z]+$"   ,							t[1]   )   ,							_A   )   )
								_a				      =					list(filter(lambda __a   : [t[0]] == tokenizer.encode(t[1]   ,							add_special_tokens=_A   )   ,							_A   )   )
								if max_length is not None and len(_A   ) > max_length:
												_a				      =					toks[:max_length]
								if min_length is not None and len(_A   ) < min_length and len(_A   ) > 0:
												while len(_A   ) < min_length:
																_a				      =					toks + toks
        # toks_str = [t[1] for t in toks]
								_a				      =					[t[0] for t in toks]

								# Ensure consistency
								_a				      =					tokenizer.decode(_A   ,							clean_up_tokenization_spaces=_A   )
								if " " not in output_txt and len(_A   ) > 1:
												_a				      =					(
												    tokenizer.decode([toks_ids[0]]   ,							clean_up_tokenization_spaces=_A   )
												    + """ """
												    + tokenizer.decode(toks_ids[1:]   ,							clean_up_tokenization_spaces=_A   )
												)
								if with_prefix_space:
												_a				      =					""" """ + output_txt
								_a				      =					tokenizer.encode(_A   ,							add_special_tokens=_A   )
								return output_txt, output_ids




				def     UpperCamelCase__  (    self	:   Any   ):
								_a				      =					self.perceiver_tokenizer
								_a				      =					"""Unicode €."""
								_a				      =					tokenizer(_A   )
								_a				      =					[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
								self.assertEqual(encoded["input_ids"]   ,							_A   )

								# decoding
								_a				      =					tokenizer.decode(_A   )
								self.assertEqual(_A   ,							"[CLS]Unicode €.[SEP]"   )

								_a				      =					tokenizer("e è é ê ë"   )
								_a				      =					[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
								self.assertEqual(encoded["input_ids"]   ,							_A   )
								# decoding
								_a				      =					tokenizer.decode(_A   )
								self.assertEqual(_A   ,							"[CLS]e è é ê ë[SEP]"   )

								# encode/decode, but with `encode` instead of `__call__`
								self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"   )   )   ,							"[CLS]e è é ê ë[SEP]"   )




				def     UpperCamelCase__  (    self	:   List[Any]   ):
								_a				      =					self.perceiver_tokenizer
								_a				      =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								# fmt: off
								_a				      =					[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
								# fmt: on
								_a				      =					tokenizer(_A   ,							padding=_A   ,							return_tensors=_A   )
								self.assertIsInstance(_A   ,							_A   )

								if FRAMEWORK != "jax":
												_a				      =					list(batch.input_ids.numpy()[0]   )
								else:
												_a				      =					list(batch.input_ids.tolist()[0]   )

								self.assertListEqual(_A   ,							_A   )

								self.assertEqual((2, 38)   ,							batch.input_ids.shape   )
								self.assertEqual((2, 38)   ,							batch.attention_mask.shape   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								_a				      =					self.perceiver_tokenizer
								_a				      =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								_a				      =					tokenizer(_A   ,							padding=_A   ,							return_tensors=_A   )
								# check if input_ids are returned and no decoder_input_ids
								self.assertIn("input_ids"   ,							_A   )
								self.assertIn("attention_mask"   ,							_A   )
								self.assertNotIn("decoder_input_ids"   ,							_A   )
								self.assertNotIn("decoder_attention_mask"   ,							_A   )




				def     UpperCamelCase__  (    self	:   str   ):
								_a				      =					self.perceiver_tokenizer
								_a				      =					[
								    """Summary of the text.""",
								    """Another summary.""",
								]
								_a				      =					tokenizer(
								    text_target=_A   ,							max_length=32   ,							padding="max_length"   ,							truncation=_A   ,							return_tensors=_A   )
								self.assertEqual(32   ,							targets["input_ids"].shape[1]   )




				def     UpperCamelCase__  (    self	:   int   ):
								_a				      =					self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																self.assertNotEqual(tokenizer.model_max_length   ,							42   )

        # Now let's start the test
								_a				      =					self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																# Isolate this from the other tests because we save additional tokens/etc
																_a				      =					tempfile.mkdtemp()

																_a				      =					""" He is very happy, UNwant\u00E9d,running"""
																_a				      =					tokenizer.encode(_A   ,							add_special_tokens=_A   )
																tokenizer.save_pretrained(_A   )

																_a				      =					tokenizer.__class__.from_pretrained(_A   )
																_a				      =					after_tokenizer.encode(_A   ,							add_special_tokens=_A   )
																self.assertListEqual(_A   ,							_A   )

																shutil.rmtree(_A   )

								_a				      =					self.get_tokenizers(model_max_length=42   )
								for tokenizer in tokenizers:
												with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																# Isolate this from the other tests because we save additional tokens/etc
																_a				      =					tempfile.mkdtemp()

																_a				      =					""" He is very happy, UNwant\u00E9d,running"""
																tokenizer.add_tokens(["bim", "bambam"]   )
																_a				      =					tokenizer.additional_special_tokens
																additional_special_tokens.append("new_additional_special_token"   )
																tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}   )
																_a				      =					tokenizer.encode(_A   ,							add_special_tokens=_A   )
																tokenizer.save_pretrained(_A   )

																_a				      =					tokenizer.__class__.from_pretrained(_A   )
																_a				      =					after_tokenizer.encode(_A   ,							add_special_tokens=_A   )
																self.assertListEqual(_A   ,							_A   )
																self.assertIn("new_additional_special_token"   ,							after_tokenizer.additional_special_tokens   )
																self.assertEqual(after_tokenizer.model_max_length   ,							42   )

																_a				      =					tokenizer.__class__.from_pretrained(_A   ,							model_max_length=43   )
																self.assertEqual(tokenizer.model_max_length   ,							43   )

																shutil.rmtree(_A   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								_a				      =					[]
								if self.test_slow_tokenizer:
												tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

								if self.test_rust_tokenizer:
												tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

								for tokenizer_class, tokenizer_utils in tokenizer_list:
												with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(_A   )

																with open(os.path.join(_A   ,							"special_tokens_map.json"   )   ,							encoding="utf-8"   ) as json_file:
																				_a				      =					json.load(_A   )

																with open(os.path.join(_A   ,							"tokenizer_config.json"   )   ,							encoding="utf-8"   ) as json_file:
																				_a				      =					json.load(_A   )

																_a				      =					[f'<extra_id_{i}>' for i in range(1_25   )]

																_a				      =					added_tokens_extra_ids + [
																    """an_additional_special_token"""
																]
																_a				      =					added_tokens_extra_ids + [
																    """an_additional_special_token"""
																]

																with open(os.path.join(_A   ,							"special_tokens_map.json"   )   ,							"w"   ,							encoding="utf-8"   ) as outfile:
																				json.dump(_A   ,							_A   )
																with open(os.path.join(_A   ,							"tokenizer_config.json"   )   ,							"w"   ,							encoding="utf-8"   ) as outfile:
																				json.dump(_A   ,							_A   )

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																_a				      =					tokenizer_class.from_pretrained(
																    _A   ,							)
																self.assertIn(
																    "an_additional_special_token"   ,							tokenizer_without_change_in_init.additional_special_tokens   )
																self.assertEqual(
																    ["an_additional_special_token"]   ,							tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]   )   )   ,							)

																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																_a				      =					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"   ,							lstrip=_A   )]
																_a				      =					tokenizer_class.from_pretrained(
																    _A   ,							additional_special_tokens=_A   ,							)

																self.assertIn("a_new_additional_special_token"   ,							tokenizer.additional_special_tokens   )
																self.assertEqual(
																    ["a_new_additional_special_token"]   ,							tokenizer.convert_ids_to_tokens(
																        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]   )   )   ,							)




				def     UpperCamelCase__  (    self	:   Tuple   ):
								_a				      =					self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([1_78]   )   ,							"�"   )




				def     UpperCamelCase__  (    self	:   List[str]   ):
								pass




				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								pass




				def     UpperCamelCase__  (    self	:   Optional[int]   ):
								pass




				def     UpperCamelCase__  (    self	:   int   ):
								pass




				def     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					self.get_tokenizers(fast=_A   ,							do_lower_case=_A   )
								for tokenizer in tokenizers:
												with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																_a				      =					["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																_a				      =					tokenizer.convert_tokens_to_string(_A   )

																self.assertIsInstance(_A   ,							_A   )







"""simple docstring"""


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCamelCase				:		Tuple				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		int				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		str				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[int]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]							,     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
						UpperCamelCase				:		Optional[Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Tuple				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		int				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						UpperCamelCase				:		str				   =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
						UpperCamelCase				:		Tuple				   =			features.copy()
						UpperCamelCase				:		Tuple				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[int]				   =			tmp_path / """cache"""
						UpperCamelCase				:		List[Any]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Any				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Dict				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     split=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""							,     [str, list]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			jsonl_path
						elif issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			[jsonl_path]
						UpperCamelCase				:		List[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		List[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[int]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=("train",)     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						for split in splits:
												UpperCamelCase				:		Optional[Any]				   =			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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			tmp_path / """cache"""
						UpperCamelCase				:		int				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCamelCase				:		Any				   =			JsonDatasetReader({"""train""": jsonl_path}							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Optional[Any]				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Optional[Any]				   =			JsonDatasetReader({"""train""": jsonl_path}							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if split:
												UpperCamelCase				:		List[Any]				   =			{split: jsonl_path}
						else:
												UpperCamelCase				:		Tuple				   =			"""train"""
												UpperCamelCase				:		Union[str, Any]				   =			{"""train""": jsonl_path, """test""": jsonl_path}
						UpperCamelCase				:		Optional[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			JsonDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_json_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     splits=list(path.keys()     )     )
						assert all(dataset[split].split == split for split in path.keys()     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return json.load(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return [json.loads(SCREAMING_SNAKE_CASE     ) for line in buffer]






class 							lowercase__			:

			"""simple docstring"""







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Optional[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@pytest.mark.parametrize(
			    """orient, container, keys, len_at"""						,   [
			        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
			        ("""split""", dict, {"""columns""", """data"""}, """data"""),
			        ("""index""", dict, set("""0123456789"""    ), None),
			        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
			        ("""values""", list, None, None),
			        ("""table""", dict, {"""schema""", """data"""}, """data"""),
			    ]						,   )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		str				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			@pytest.mark.parametrize("""lines, load_json_function"""						,   [(True, load_json_lines), (False, load_json)]    )
			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		List[Any]				   =			load_json_function(_A    )
									assert isinstance(_A						,   _A    )
									assert isinstance(exported_content[0]						,   _A    )
									assert len(_A    ) == 1_0







			@pytest.mark.parametrize(
			    """orient, container, keys, len_at"""						,   [
			        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
			        ("""split""", dict, {"""columns""", """data"""}, """data"""),
			        ("""index""", dict, set("""0123456789"""    ), None),
			        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
			        ("""values""", list, None, None),
			        ("""table""", dict, {"""schema""", """data"""}, """data"""),
			    ]						,   )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									with io.BytesIO() as buffer:
															JsonDatasetWriter(_A						,   _A						,   lines=_A						,   orient=_A						,   num_proc=2    ).write()
															buffer.seek(0    )
															UpperCamelCase				:		Dict				   =			load_json(_A    )
									assert isinstance(_A						,   _A    )
									if keys:
															if container is dict:
																					assert exported_content.keys() == keys
															else:
																					assert exported_content[0].keys() == keys
									else:
															assert not hasattr(_A						,   """keys"""    ) and not hasattr(exported_content[0]						,   """keys"""    )
									if len_at:
															assert len(exported_content[len_at]    ) == 1_0
									else:
															assert len(_A    ) == 1_0







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									with pytest.raises(_A    ):
															with io.BytesIO() as buffer:
																					JsonDatasetWriter(_A						,   _A						,   num_proc=0    )







			@pytest.mark.parametrize("""compression, extension"""						,   [("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]    )
			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			tmp_path_factory.mktemp("""data"""    ) / f"""test.json.{extension}"""
									UpperCamelCase				:		Tuple				   =			str(shared_datadir / f"""test_file.json.{extension}"""    )
									JsonDatasetWriter(_A						,   _A						,   compression=_A    ).write()

									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Dict				   =			f.read()
									with fsspec.open(_A						,   """rb"""						,   compression="""infer"""    ) as f:
															UpperCamelCase				:		Tuple				   =			f.read()
									assert exported_content == original_content



