"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "facebook/bart-large-mnli"
       SCREAMING_SNAKE_CASE_			=      (
           "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
           "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
           "It returns the most likely label in the list of provided `labels` for the input text."
       )
       SCREAMING_SNAKE_CASE_			=      "text_classifier"
       SCREAMING_SNAKE_CASE_			=      AutoTokenizer
       SCREAMING_SNAKE_CASE_			=      AutoModelForSequenceClassification

       SCREAMING_SNAKE_CASE_			=      ["text", ["text"]]
       SCREAMING_SNAKE_CASE_			=      ["text"]







       def 		a_						( self)      ->      Dict:
            super().setup()
            snake_case_					       =					self.model.config
            snake_case_					       =					-1
            for idx, label in config.idalabel.items():
                 if label.lower().startswith('entail'):
                      snake_case_					       =					int(lowerCAmelCase__)
            if self.entailment_id == -1:
                 raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					labels
            return self.pre_processor(
                [text] * len(lowerCAmelCase__),							[f'This example is {label}' for label in labels],							return_tensors='pt',							padding='max_length',							)







       def 		a_						( self,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					outputs.logits
            snake_case_					       =					torch.argmax(logits[:, 2]).item()
            return self._labels[label_id]







'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	)
					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


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_									=				SqlDatasetReader(
										    '''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase,      keep_in_memory=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


@require_sqlalchemy
@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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					snake_case_									=				features.copy() if features else default_expected_features
					snake_case_									=				(
					    Features({feature: Value(__UpperCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      features=__UpperCAmelCase,      cache_dir=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					with contextlib.closing(sqlitea.connect(__UpperCAmelCase	)	) as con:
										snake_case_									=				con.cursor()
										cur.execute('''SELECT * FROM dataset'''	)
										for row in cur:
															yield row


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=1	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

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


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=2	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

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


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					with pytest.raises(__UpperCAmelCase	):
										SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=0	).write()

