

import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					10
							snake_case_           =					datasets.Features(
							    {
							        'tokens': datasets.Sequence(datasets.Value('string'   )   ),
							        'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive']   )   ),
							        'answers': datasets.Sequence(
							            {
							                'text': datasets.Value('string'   ),
							                'answer_start': datasets.Value('int32'   ),
							            }   ),
							        'id': datasets.Value('int64'   ),
							    }   )
							snake_case_           =					datasets.Dataset.from_dict(
							    {
							        'tokens': [['foo'] * 5] * n,
							        'labels': [[1] * 5] * n,
							        'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
							        'id': list(range(UpperCamelCase__   )   ),
							    }     ,							features=UpperCamelCase__     ,							)
							return dataset





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'file.arrow'   )
							dataset.map(cache_file_name=UpperCamelCase__   )
							return filename


# FILE_CONTENT + files


_UpperCAmelCase	:     List[str]		       = """\
    Text data.
    Second line of data."""





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt'
							snake_case_           =					FILE_CONTENT
							with open(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__   )
							return filename





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							import bza

							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.bz2'
							snake_case_           =					bytes(UpperCamelCase__     ,							'utf-8'   )
							with bza.open(UpperCamelCase__     ,							'wb'   ) as f:
														f.write(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							import gzip

							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'file.txt.gz'   )
							snake_case_           =					bytes(UpperCamelCase__     ,							'utf-8'   )
							with gzip.open(UpperCamelCase__     ,							'wb'   ) as f:
														f.write(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if datasets.config.LZ4_AVAILABLE:
														import lza.frame

														snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.lz4'
														snake_case_           =					bytes(UpperCamelCase__     ,							'utf-8'   )
														with lza.frame.open(UpperCamelCase__     ,							'wb'   ) as f:
																					f.write(UpperCamelCase__   )
														return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if datasets.config.PY7ZR_AVAILABLE:
														import pyazr

														snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.7z'
														with pyazr.SevenZipFile(UpperCamelCase__     ,							'w'   ) as archive:
																					archive.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import tarfile

							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.tar'
							with tarfile.TarFile(UpperCamelCase__     ,							'w'   ) as f:
														f.add(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							import lzma

							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.xz'
							snake_case_           =					bytes(UpperCamelCase__     ,							'utf-8'   )
							with lzma.open(UpperCamelCase__     ,							'wb'   ) as f:
														f.write(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import zipfile

							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if datasets.config.ZSTANDARD_AVAILABLE:
														import zstandard as zstd

														snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.txt.zst'
														snake_case_           =					bytes(UpperCamelCase__     ,							'utf-8'   )
														with zstd.open(UpperCamelCase__     ,							'wb'   ) as f:
																					f.write(UpperCamelCase__   )
														return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'file.xml'
							snake_case_           =					textwrap.dedent(
							    '\\n    <?xml version="1.0" encoding="UTF-8" ?>\n    <tmx version="1.4">\n      <header segtype="sentence" srclang="ca" />\n      <body>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n        </tu>\n      </body>\n    </tmx>'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__   )
							return filename


_UpperCAmelCase	:     Union[str, Any]		       = [
    {"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
    {"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
    {"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
    {"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
_UpperCAmelCase	:     List[Any]		       = [
    {"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
    {"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
_UpperCAmelCase	:     Tuple		       = {
    """col_1""": ["""0""", """1""", """2""", """3"""],
    """col_2""": [0, 1, 2, 3],
    """col_3""": [0.0, 1.0, 2.0, 3.0],
}

_UpperCAmelCase	:     List[Any]		       = [
    {"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
    {"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]

_UpperCAmelCase	:     Dict		       = [
    {"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
    {"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
    {"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
    {"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				):







							'''simple docstring'''






							return DATA_DICT_OF_LISTS





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					datasets.Dataset.from_dict(UpperCamelCase__   )
							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.arrow'   )
							dataset.map(cache_file_name=UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.sqlite'   )
							with contextlib.closing(sqlitea.connect(UpperCamelCase__   )   ) as con:
														snake_case_           =					con.cursor()
														cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)'   )
														for item in DATA:
																					cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)'     ,							tuple(item.values()   )   )
														con.commit()
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.csv'   )
							with open(UpperCamelCase__     ,							'w'     ,							newline=''   ) as f:
														snake_case_           =					csv.DictWriter(UpperCamelCase__     ,							fieldnames=['col_1', 'col_2', 'col_3']   )
														writer.writeheader()
														for item in DATA:
																					writer.writerow(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset2.csv'   )
							with open(UpperCamelCase__     ,							'w'     ,							newline=''   ) as f:
														snake_case_           =					csv.DictWriter(UpperCamelCase__     ,							fieldnames=['col_1', 'col_2', 'col_3']   )
														writer.writeheader()
														for item in DATA:
																					writer.writerow(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import bza

							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.csv.bz2'
							with open(UpperCamelCase__     ,							'rb'   ) as f:
														snake_case_           =					f.read()
							# data = bytes(FILE_CONTENT, "utf-8")
							with bza.open(UpperCamelCase__     ,							'wb'   ) as f:
														f.write(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.csv.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.csv.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(csv_path.replace('.csv'     ,							'.CSV'   )   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(csva_path.replace('.csv'     ,							'.CSV'   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset_with_dir.csv.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.parquet'   )
							snake_case_           =					pa.schema(
							    {
							        'col_1': pa.string(),
							        'col_2': pa.intaa(),
							        'col_3': pa.floataa(),
							    }   )
							with open(UpperCamelCase__     ,							'wb'   ) as f:
														snake_case_           =					pq.ParquetWriter(UpperCamelCase__     ,							schema=UpperCamelCase__   )
														snake_case_           =					pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase__   )   )] for k in DATA[0]}     ,							schema=UpperCamelCase__   )
														writer.write_table(UpperCamelCase__   )
														writer.close()
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.json'   )
							snake_case_           =					{'data': DATA}
							with open(UpperCamelCase__     ,							'w'   ) as f:
														json.dump(UpperCamelCase__     ,							UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.json'   )
							snake_case_           =					{'data': DATA_DICT_OF_LISTS}
							with open(UpperCamelCase__     ,							'w'   ) as f:
														json.dump(UpperCamelCase__     ,							UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.jsonl'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in DATA:
																					f.write(json.dumps(UpperCamelCase__   ) + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset2.jsonl'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in DATA:
																					f.write(json.dumps(UpperCamelCase__   ) + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset_312.jsonl'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in DATA_312:
																					f.write(json.dumps(UpperCamelCase__   ) + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset-str.jsonl'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in DATA_STR:
																					f.write(json.dumps(UpperCamelCase__   ) + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import gzip

							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.txt.gz'   )
							with open(UpperCamelCase__     ,							'rb'   ) as orig_file:
														with gzip.open(UpperCamelCase__     ,							'wb'   ) as zipped_file:
																					zipped_file.writelines(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							import gzip

							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.jsonl.gz'   )
							with open(UpperCamelCase__     ,							'rb'   ) as orig_file:
														with gzip.open(UpperCamelCase__     ,							'wb'   ) as zipped_file:
																					zipped_file.writelines(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.jsonl.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset_nested.jsonl.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.join('nested'     ,							os.path.basename(UpperCamelCase__   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset_with_dir.jsonl.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.jsonl.tar'
							with tarfile.TarFile(UpperCamelCase__     ,							'w'   ) as f:
														f.add(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														f.add(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset_nested.jsonl.tar'
							with tarfile.TarFile(UpperCamelCase__     ,							'w'   ) as f:
														f.add(UpperCamelCase__     ,							arcname=os.path.join('nested'     ,							os.path.basename(UpperCamelCase__   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					['0', '1', '2', '3']
							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset.txt'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in data:
																					f.write(item + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					['0', '1', '2', '3']
							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset2.txt'   )
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in data:
																					f.write(item + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					['0', '1', '2', '3']
							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.abc'
							with open(UpperCamelCase__     ,							'w'   ) as f:
														for item in data:
																					f.write(item + '\n'   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.text.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset_with_dir.text.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.join('main_dir'     ,							os.path.basename(UpperCamelCase__   )   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.ext.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename('unsupported.ext'   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename('unsupported_2.ext'   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					'\n'.join(['First', 'Second\u2029with Unicode new line', 'Third']   )
							snake_case_           =					str(tmp_path_factory.mktemp('data'   ) / 'dataset_with_unicode_new_lines.txt'   )
							with open(UpperCamelCase__     ,							'w'     ,							encoding='utf-8'   ) as f:
														f.write(UpperCamelCase__   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				):







							'''simple docstring'''






							return os.path.join('tests'     ,							'features'     ,							'data'     ,							'test_image_rgb.jpg'   )





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				):







							'''simple docstring'''






							return os.path.join('tests'     ,							'features'     ,							'data'     ,							'test_audio_44100.wav'   )





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data'   ) / 'dataset.img.zip'
							with zipfile.ZipFile(UpperCamelCase__     ,							'w'   ) as f:
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   )   )
														f.write(UpperCamelCase__     ,							arcname=os.path.basename(UpperCamelCase__   ).replace('.jpg'     ,							'2.jpg'   )   )
							return path





@pytest.fixture(scope='session'   )
def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tmp_path_factory.mktemp('data_dir'   )

							(data_dir / "subdir").mkdir()
							with open(data_dir / 'subdir' / 'train.txt'     ,							'w'   ) as f:
														f.write('foo\n' * 10   )
							with open(data_dir / 'subdir' / 'test.txt'     ,							'w'   ) as f:
														f.write('bar\n' * 10   )
							# hidden file
							with open(data_dir / 'subdir' / '.test.txt'     ,							'w'   ) as f:
														f.write('bar\n' * 10   )

							# hidden directory
							(data_dir / ".subdir").mkdir()
							with open(data_dir / '.subdir' / 'train.txt'     ,							'w'   ) as f:
														f.write('foo\n' * 10   )
							with open(data_dir / '.subdir' / 'test.txt'     ,							'w'   ) as f:
														f.write('bar\n' * 10   )

							return data_dir







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DetrImageProcessor







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=7   ,     snake_case=3   ,     snake_case=30   ,     snake_case=400   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=1 / 255   ,     snake_case=True   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=True   ,     ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case_           =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					num_channels
											snake_case_           =					min_resolution
											snake_case_           =					max_resolution
											snake_case_           =					do_resize
											snake_case_           =					size
											snake_case_           =					do_rescale
											snake_case_           =					rescale_factor
											snake_case_           =					do_normalize
											snake_case_           =					image_mean
											snake_case_           =					image_std
											snake_case_           =					do_pad




				def 					a							(      self ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}




				def 					a							(      self   ,     snake_case   ,     snake_case=False ):
											if not batched:
																		snake_case_           =					image_inputs[0]
																		if isinstance(snake_case   ,     Image.Image ):
																									snake_case_       ,					snake_case_           =					image.size
																		else:
																									snake_case_       ,					snake_case_           =					image.shape[1], image.shape[2]
																		if w < h:
																									snake_case_           =					int(self.size['shortest_edge'] * h / w )
																									snake_case_           =					self.size['shortest_edge']
																		elif w > h:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					int(self.size['shortest_edge'] * w / h )
																		else:
																									snake_case_           =					self.size['shortest_edge']
																									snake_case_           =					self.size['shortest_edge']

											else:
																		snake_case_           =					[]
																		for image in image_inputs:
																									snake_case_       ,					snake_case_           =					self.get_expected_values([image] )
																									expected_values.append((expected_height, expected_width) )
																		snake_case_           =					max(snake_case   ,     key=lambda snake_case : item[0] )[0]
																		snake_case_           =					max(snake_case   ,     key=lambda snake_case : item[1] )[1]

											return expected_height, expected_width







@require_torch
@require_vision
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    DetrImageProcessor if is_vision_available() else None




				def 					a							(      self ):
											snake_case_           =					DetrImageProcessingTester(self )




				@property
				def 					a							(      self ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											self.assertTrue(hasattr(snake_case   ,     'image_mean' ) )
											self.assertTrue(hasattr(snake_case   ,     'image_std' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_normalize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_rescale' ) )
											self.assertTrue(hasattr(snake_case   ,     'rescale_factor' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_pad' ) )




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class.from_dict(self.image_processor_dict )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 18, 'longest_edge': 1333} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )

											snake_case_           =					self.image_processing_class.from_dict(
											    self.image_processor_dict   ,     size=42   ,     max_size=84   ,     pad_and_return_pixel_mask=snake_case )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 42, 'longest_edge': 84} )
											self.assertEqual(image_processor.do_pad   ,     snake_case )




				def 					a							(      self ):
											pass




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PIL images
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     Image.Image )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values
											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random numpy tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     numpify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     np.ndarray )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				def 					a							(      self ):
											# Initialize image_processing
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											# create random PyTorch tensors
											snake_case_           =					prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=snake_case   ,     torchify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     torch.Tensor )

											# Test not batched input
											snake_case_           =					image_processing(image_inputs[0]   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,     )

											# Test batched
											snake_case_           =					image_processing(snake_case   ,     return_tensors='pt' ).pixel_values

											snake_case_       ,					snake_case_           =					self.image_processor_tester.get_expected_values(snake_case   ,     batched=snake_case )

											self.assertEqual(
											    encoded_images.shape   ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )   ,     )




				@slow
				def 					a							(      self ):
											# prepare image and target
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'image_id': 3_9769, 'annotations': target}

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([75, 75, 63, 65, 17, 17] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




				@slow
				def 					a							(      self ):
											# prepare image, target and masks_path
											snake_case_           =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,     'r' ) as f:
																		snake_case_           =					json.loads(f.read() )

											snake_case_           =					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

											snake_case_           =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

											# encode them
											snake_case_           =					DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
											snake_case_           =					image_processing(images=snake_case   ,     annotations=snake_case   ,     masks_path=snake_case   ,     return_tensors='pt' )

											# verify pixel values
											snake_case_           =					torch.Size([1, 3, 800, 1066] )
											self.assertEqual(encoding['pixel_values'].shape   ,     snake_case )

											snake_case_           =					torch.tensor([0.27_96, 0.31_38, 0.34_81] )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,     snake_case   ,     atol=1e-4 ) )

											# verify area
											snake_case_           =					torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,     snake_case ) )
											# verify boxes
											snake_case_           =					torch.Size([6, 4] )
											self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     snake_case )
											snake_case_           =					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,     snake_case   ,     atol=1e-3 ) )
											# verify image_id
											snake_case_           =					torch.tensor([3_9769] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     snake_case ) )
											# verify is_crowd
											snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     snake_case ) )
											# verify class_labels
											snake_case_           =					torch.tensor([17, 17, 63, 75, 75, 93] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     snake_case ) )
											# verify masks
											snake_case_           =					82_2873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,     snake_case )
											# verify orig_size
											snake_case_           =					torch.tensor([480, 640] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     snake_case ) )
											# verify size
											snake_case_           =					torch.tensor([800, 1066] )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     snake_case ) )




