




import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)

class 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  foo







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


									'''simple docstring'''


									return self.foo





class 		lowercase_       (	datasets.Dataset				):




					@fingerprint_transform(inplace=_lowercase)
					def     _lowercase						(   self:		List[Any],     _lowercase:		List[str],     *_lowercase:		Optional[int],     **_lowercase:		List[Any]):


									'''simple docstring'''


									return DatasetChild(self.data,     fingerprint=_lowercase)







					@fingerprint_transform(inplace=_lowercase)
					def     _lowercase						(   self:		Optional[int],     _lowercase:		Optional[int],     *_lowercase:		int,     **_lowercase:		Optional[int]):


									'''simple docstring'''


									return DatasetChild(self.data,     fingerprint=_lowercase)





class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  callable




					def __call__(   self:		Optional[Any],     *_lowercase:		Dict,     **_lowercase:		str):


									'''simple docstring'''


									if self.callable is not None:
													return self.callable(*_lowercase,     **_lowercase)







					def __getstate__(   self:		Optional[Any]):


									'''simple docstring'''


									raise pickle.PicklingError()





class 		lowercase_       (	lowerCAmelCase__				):




					@require_transformers
					@pytest.mark.integration
					def     _lowercase						(   self:		str):


									'''simple docstring'''


									from transformers import AutoTokenizer

									def encode(_lowercase:		int):
													return tokenizer(_lowercase)

									# TODO: add hash consistency tests across sessions
									__lowerCAmelCase					=  AutoTokenizer.from_pretrained("""bert-base-uncased""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(lambda _lowercase: tokenizer(_lowercase))).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  AutoTokenizer.from_pretrained("""bert-base-cased""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(lambda _lowercase: tokenizer(_lowercase))).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  AutoTokenizer.from_pretrained("""bert-base-uncased""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(lambda _lowercase: tokenizer(_lowercase))).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					@require_transformers
					@pytest.mark.integration
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									from transformers import AutoTokenizer

									__lowerCAmelCase					=  AutoTokenizer.from_pretrained("""gpt2""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									tokenizer("""Hello world !""")  # call once to change the tokenizer's cache
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)







					@require_regex
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									import regex

									__lowerCAmelCase					=  regex.Regex("""foo""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  regex.Regex("""bar""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  regex.Regex("""foo""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)





class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									def func():
													return foo

									__lowerCAmelCase					=  [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  [1]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									def func():
													pass

									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()

									def func():
													pass

									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([0]))).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([1]))).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([0]))).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([0]).__call__)).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([1]).__call__)).hexdigest()
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(Foo([0]).__call__)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  (
									    """co_argcount""",
									    """co_kwonlyargcount""",
									    """co_nlocals""",
									    """co_stacksize""",
									    """co_flags""",
									    """co_code""",
									    """co_consts""",
									    """co_names""",
									    """co_varnames""",
									    """co_filename""",
									    """co_name""",
									    """co_firstlineno""",
									    """co_lnotab""",
									    """co_freevars""",
									    """co_cellvars""",
									)

									def _create_code_pyaa(*_lowercase:		Any):
													if hasattr(args[-3],     """encode"""):
																	__lowerCAmelCase					=  list(_lowercase)
																	__lowerCAmelCase					=  args[-3].encode()  # co_lnotab
																	__lowerCAmelCase					=  args[-10].encode()  # co_code
													if hasattr(_lowercase,     """co_posonlyargcount"""):
																	if len(_lowercase) == 16:
																					return CodeType(*_lowercase)
																	elif len(_lowercase) == 15:
																					return CodeType(args[0],     0,     *args[1:])
																	return CodeType(args[0],     0,     0,     *args[1:])
													elif hasattr(_lowercase,     """co_kwonlyargcount"""):
																	if len(_lowercase) == 16:
																					return CodeType(args[0],     *args[2:])
																	elif len(_lowercase) == 15:
																					return CodeType(*_lowercase)
																	return CodeType(args[0],     0,     *args[1:])
													if len(_lowercase) == 16:
																	return CodeType(args[0],     *args[3:])
													elif len(_lowercase) == 15:
																	return CodeType(args[0],     *args[2:])
													return CodeType(*_lowercase)

									def create_ipython_func(_lowercase:		Dict,     _lowercase:		List[str]):
													def func():
																	return returned_obj

													__lowerCAmelCase					=  func.__code__
													# Use _create_code from dill in order to make it work for different python versions
													if datasets.config.PY_VERSION < version.parse("""3.8"""):
																	__lowerCAmelCase					=  _create_code_pyaa(
																	    *[getattr(_lowercase,     _lowercase) if k != """co_filename""" else co_filename for k in code_args_pyaa])
													else:
																	__lowerCAmelCase					=  code.replace(co_filename=_lowercase)
													return FunctionType(_lowercase,     func.__globals__,     func.__name__,     func.__defaults__,     func.__closure__)

									__lowerCAmelCase	,				__lowerCAmelCase					=  """<ipython-input-2-e0383a102aae>""", [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									__lowerCAmelCase	,				__lowerCAmelCase					=  """<ipython-input-2-e0383a102aae>""", [1]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									__lowerCAmelCase	,				__lowerCAmelCase					=  """<ipython-input-5-713f6613acf3>""", [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)

									__lowerCAmelCase	,				__lowerCAmelCase					=  os.path.join(gettempdir(),     """ipykernel_12345""",     """321456789.py"""), [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									__lowerCAmelCase	,				__lowerCAmelCase					=  os.path.join(gettempdir(),     """ipykernel_12345""",     """321456789.py"""), [1]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									__lowerCAmelCase	,				__lowerCAmelCase					=  os.path.join(gettempdir(),     """ipykernel_12345""",     """654123987.py"""), [0]
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(create_ipython_func(_lowercase,     _lowercase))).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)







					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  [0], [1]

									def func():
													return foo, bar

									__lowerCAmelCase					=  """__main__"""

									def globalvars_mocka_side_effect(_lowercase:		int,     *_lowercase:		Any,     **_lowercase:		Union[str, Any]):
													return {"foo": foo, "bar": bar}

									def globalvars_mocka_side_effect(_lowercase:		List[Any],     *_lowercase:		Optional[int],     **_lowercase:		Dict):
													return {"bar": bar, "foo": foo}

									with patch("""dill.detect.globalvars""",     side_effect=_lowercase) as globalvars_mocka:
													__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
													self.assertGreater(globalvars_mocka.call_count,     0)
									with patch("""dill.detect.globalvars""",     side_effect=_lowercase) as globalvars_mocka:
													__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
													self.assertGreater(globalvars_mocka.call_count,     0)
									self.assertEqual(_lowercase,     _lowercase)





class 		lowercase_       (	lowerCAmelCase__				):




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


									'''simple docstring'''


									__lowerCAmelCase					=  Hasher.hash("""hello""")
									__lowerCAmelCase					=  Hasher.hash("""hello""")
									__lowerCAmelCase					=  Hasher.hash("""there""")
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  Hasher.hash(Foo("""hello"""))
									__lowerCAmelCase					=  Hasher.hash(Foo("""hello"""))
									__lowerCAmelCase					=  Hasher.hash(Foo("""there"""))
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  Hasher()
									for x in ["hello", Foo("""hello""")]:
													hasher.update(_lowercase)
									__lowerCAmelCase					=  hasher.hexdigest()
									__lowerCAmelCase					=  Hasher()
									for x in ["hello", Foo("""hello""")]:
													hasher.update(_lowercase)
									__lowerCAmelCase					=  hasher.hexdigest()
									__lowerCAmelCase					=  Hasher()
									for x in ["there", Foo("""there""")]:
													hasher.update(_lowercase)
									__lowerCAmelCase					=  hasher.hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									with self.assertRaises(pickle.PicklingError):
													Hasher.hash(UnpicklableCallable(Foo("""hello""")))




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  """abc"""
									__lowerCAmelCase					=  [string, string]  # two strings have the same ids
									__lowerCAmelCase					=  [string, string]
									__lowerCAmelCase					=  json.loads(f'''["{string}", "{string}"]''')  # two strings have different ids
									self.assertIs(obja[0],     _lowercase)
									self.assertIs(obja[0],     obja[1])
									self.assertIs(obja[0],     _lowercase)
									self.assertIs(obja[0],     obja[1])
									self.assertIsNot(obja[0],     _lowercase)
									self.assertIsNot(obja[0],     obja[1])
									__lowerCAmelCase					=  Hasher.hash(_lowercase)
									__lowerCAmelCase					=  Hasher.hash(_lowercase)
									__lowerCAmelCase					=  Hasher.hash(_lowercase)
									self.assertEqual(_lowercase,     _lowercase)
									self.assertEqual(_lowercase,     _lowercase)




					@require_tiktoken
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									import tiktoken

									__lowerCAmelCase					=  tiktoken.get_encoding("""gpt2""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  tiktoken.get_encoding("""r50k_base""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  tiktoken.get_encoding("""gpt2""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)




					@require_torch
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									import torch

									__lowerCAmelCase					=  torch.tensor([1.0])
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  torch.tensor([2.0])
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  torch.tensor([1.0])
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)







					@require_spacy
					@require_spacy_model("""en_core_web_sm""")
					@require_spacy_model("""fr_core_news_sm""")
					@pytest.mark.integration
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									import spacy

									__lowerCAmelCase					=  spacy.load("""en_core_web_sm""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  spacy.load("""fr_core_news_sm""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									__lowerCAmelCase					=  spacy.load("""en_core_web_sm""")
									__lowerCAmelCase					=  mda(datasets.utils.py_utils.dumps(_lowercase)).hexdigest()
									self.assertEqual(_lowercase,     _lowercase)
									self.assertNotEqual(_lowercase,     _lowercase)





@pytest.mark.integration
def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[int]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """dir1"""
				__lowerCAmelCase					=  tmp_path / """dir2"""
				dira.mkdir()
				dira.mkdir()
				__lowerCAmelCase					=  """script.py"""
				__lowerCAmelCase					=  dedent(
				    """
    from datasets.fingerprint import Hasher
    def foo():
        pass
    print(Hasher.hash(foo))
    """   )
				__lowerCAmelCase					=  dira / script_filename
				__lowerCAmelCase					=  dira / script_filename
				with script_patha.open("""w"""   ) as f:
								f.write(UpperCamelCase__   )
				with script_patha.open("""w"""   ) as f:
								f.write(UpperCamelCase__   )
				__lowerCAmelCase					=  subprocess.check_output(["""python""", str(UpperCamelCase__   )]   )
				__lowerCAmelCase					=  subprocess.check_output(["""python""", str(UpperCamelCase__   )]   )
				assert fingerprinta == fingerprinta





def      UpperCAmelCase  (						)     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  {"""a""": [0, 1, 2]}
				__lowerCAmelCase					=  DatasetChild(InMemoryTable.from_pydict(UpperCamelCase__   )   )
				__lowerCAmelCase					=  dataset.funca()._fingerprint
				assert expected_fingerprint == dataset.funca()._fingerprint
				assert expected_fingerprint != dataset.funca()._fingerprint

				with Pool(2   ) as p:
								assert expected_fingerprint == p.apply_async(dataset.funca   ).get()._fingerprint
								assert expected_fingerprint != p.apply_async(dataset.funca   ).get()._fingerprint





def      UpperCAmelCase  (						)     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  {"""a""": [0, 1, 2]}

				class 		lowercase_       (	datasets.Dataset				):




									@fingerprint_transform(inplace=_lowercase)
									def     _lowercase						(   self:		Any,     _lowercase:		Any):


													'''simple docstring'''


													return DummyDatasetChild(self.data,     fingerprint=_lowercase)





				__lowerCAmelCase					=  DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__   )   ).func()

				class 		lowercase_       (	datasets.Dataset				):




									@fingerprint_transform(inplace=_lowercase,     version="""1.0.0""")
									def     _lowercase						(   self:		List[str],     _lowercase:		Optional[Any]):


													'''simple docstring'''


													return DummyDatasetChild(self.data,     fingerprint=_lowercase)





				__lowerCAmelCase					=  DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__   )   ).func()

				class 		lowercase_       (	datasets.Dataset				):




									@fingerprint_transform(inplace=_lowercase,     version="""2.0.0""")
									def     _lowercase						(   self:		str,     _lowercase:		Optional[int]):


													'''simple docstring'''


													return DummyDatasetChild(self.data,     fingerprint=_lowercase)





				__lowerCAmelCase					=  DummyDatasetChild(InMemoryTable.from_pydict(UpperCamelCase__   )   ).func()

				assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}   ) == 3





def      UpperCAmelCase  (						)     ->     int:







				'''simple docstring'''


				__lowerCAmelCase					=  Hasher()
				hasher.update(lambda UpperCamelCase__   : x   )










from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__SCREAMING_SNAKE_CASE     =	{
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}




@pytest.mark.parametrize(
    "task_cls"			,     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,     )
def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    LanguageModeling(text_column="input_text"			)
									assert {"input_text": "text"} == task.column_mapping







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[int]:
									A				:       List[Any]														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Optional[int]:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[Any]														=    {"text_column": "input_text", "label_column": "input_labels"}
									A				:       Any														=    TextClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("text-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       Dict														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Any														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    QuestionAnsweringExtractive(
									    context_column="input_context"						,						question_column="input_question"						,						answers_column="input_answers"			)
									self.assertDictEqual(
									    {"input_context": "context", "input_question": "question", "input_answers": "answers"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    Summarization(text_column="input_text"						,						summary_column="input_summary"			)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    AutomaticSpeechRecognition(audio_column="input_audio"						,						transcription_column="input_transcription"			)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       Tuple														=    Features({"audio": Audio()}			)
									A				:       Any														=    Features({"transcription": Value("string"			)}			)
									A				:       List[str]														=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									A				:       Dict														=    AutomaticSpeechRecognition.from_dict(__lowerCamelCase			)
									self.assertEqual("automatic-speech-recognition"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, Any]:
									A				:       Optional[int]														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Union[str, Any]														=    Features({"audio": Audio()}			)
									A				:       Dict														=    Features({"labels": ClassLabel}			)
									A				:       Tuple														=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Any														=    AudioClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("audio-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[Any]														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    ImageClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("image-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[Any]														=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,						info=__lowerCamelCase			)
									assert isinstance(dataset.info.task_templates						,						__lowerCamelCase			)
									assert len(dataset.info.task_templates			) == 1

									def keep_task(__lowerCamelCase   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Tuple:
									A				:       Optional[Any]														=    Features({"text": Value("string"			), "label": ClassLabel(names=("pos", "neg")			)}			)
									A				:       Optional[Any]														=    TextClassification(text_column="text"						,						label_column="label"			)
									A				:       Optional[Any]														=    DatasetInfo(features=__lowerCamelCase						,						task_templates=__lowerCamelCase			)
									A				:       Optional[int]														=    Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}						,						info=__lowerCamelCase			)

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []