





'''simple docstring'''






import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
	import torch

	__UpperCamelCase      :   Tuple								=     True
except ImportError:
	__UpperCamelCase      :   Optional[int]								=     False


try:
	from torch.hub import _get_torch_home

	__UpperCamelCase      :   Optional[Any]								=     _get_torch_home()
except ImportError:
	__UpperCamelCase      :   Optional[int]								=     os.path.expanduser(
	    os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
	)

__UpperCamelCase      :   Optional[int]								=     os.path.join(torch_cache_home, '''transformers''')

__UpperCamelCase      :   int								=     '''https://cdn.huggingface.co'''
__UpperCamelCase      :   Any								=     '''https://s3.amazonaws.com/models.huggingface.co/bert'''
__UpperCamelCase      :   Optional[int]								=     '''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
__UpperCamelCase      :   Tuple								=     os.path.join(PATH, '''config.yaml''')
__UpperCamelCase      :   List[Any]								=     os.path.join(PATH, '''attributes.txt''')
__UpperCamelCase      :   Dict								=     os.path.join(PATH, '''objects.txt''')
__UpperCamelCase      :   List[str]								=     os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
__UpperCamelCase      :   Any								=     os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
__UpperCamelCase      :   Any								=     os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
__UpperCamelCase      :   List[str]								=     '''pytorch_model.bin'''
__UpperCamelCase      :   Union[str, Any]								=     '''config.yaml'''





def      lowercase  (  lowerCAmelCase :						Tuple=OBJECTS   , lowerCAmelCase :						List[Any]=ATTRIBUTES):



				"""simple docstring"""


				_A :				Tuple				=  []
				with open(lowerCAmelCase) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''',''')[0].lower().strip())

				_A :				Any				=  []
				with open(lowerCAmelCase) as f:
								for object in f.readlines():
												vg_attrs.append(object.split(''',''')[0].lower().strip())
				return vg_classes, vg_attrs





def      lowercase  (  lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  OrderedDict()
				with open(lowerCAmelCase   , '''rb''') as f:
								_A :				Any				=  pkl.load(lowerCAmelCase)['''model''']
				for k in copy.deepcopy(list(ckp.keys())):
								_A :				Dict				=  ckp.pop(lowerCAmelCase)
								if isinstance(lowerCAmelCase   , np.ndarray):
												_A :				int				=  torch.tensor(lowerCAmelCase)
								else:
												assert isinstance(lowerCAmelCase   , torch.tensor), type(lowerCAmelCase)
								_A :				Optional[int]				=  v
				return r



class  lowerCamelCase__				:


	"""simple docstring"""





	__magic_name__							   =		{}




	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = "root"   ,     UpperCAmelCase__=0			)       ->				List[Any]:
					_A :				Tuple				=  name
					_A :				Optional[Any]				=  level
					_A :				int				=  {}
					for k, v in dictionary.items():
									if v is None:
													raise ValueError()
									_A :				Optional[int]				=  copy.deepcopy(UpperCAmelCase__			)
									_A :				Any				=  copy.deepcopy(UpperCAmelCase__			)
									if isinstance(UpperCAmelCase__   ,     UpperCAmelCase__			):
													_A :				List[Any]				=  Config(UpperCAmelCase__   ,     name=UpperCAmelCase__   ,     level=level + 1			)
									_A :				Optional[int]				=  v
									setattr(self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)

					_A :				str				=  d




	def __repr__(					self			)       ->				int:
					return str(list((self._pointer.keys())			)			)




	def __setattr__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					_A :				List[Any]				=  val
					_A :				Any				=  val
					_A :				Any				=  key.split('''.'''			)
					_A :				Tuple				=  len(UpperCAmelCase__			) - 1
					_A :				Union[str, Any]				=  self._pointer
					if len(UpperCAmelCase__			) > 1:
									for i, l in enumerate(UpperCAmelCase__			):
													if hasattr(self   ,     UpperCAmelCase__			) and isinstance(getattr(self   ,     UpperCAmelCase__			)   ,     UpperCAmelCase__			):
																	setattr(getattr(self   ,     UpperCAmelCase__			)   ,     '''.'''.join(levels[i:]			)   ,     UpperCAmelCase__			)
													if l == last_level:
																	_A :				int				=  val
													else:
																	_A :				List[Any]				=  pointer[l]




	def 				_lowerCamelCase				(					self			)       ->				Dict:
					return self._pointer




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[Any]:
					with open(F"""{file_name}"""   ,     '''w'''			) as stream:
									dump(UpperCAmelCase__   ,     UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					with open(F"""{file_name}"""   ,     '''w'''			) as stream:
									json.dump(UpperCAmelCase__   ,     UpperCAmelCase__			)




	@staticmethod
	def 				_lowerCamelCase				(					UpperCAmelCase__			)       ->				Optional[Any]:
					with open(UpperCAmelCase__			) as stream:
									_A :				List[Any]				=  load(UpperCAmelCase__   ,     Loader=UpperCAmelCase__			)
					return data




	def __str__(					self			)       ->				Dict:
					_A :				Optional[int]				=  '''    '''
					if self._name != "root":
									_A :				List[str]				=  F"""{t * (self._level-1)}{self._name}:\n"""
					else:
									_A :				Dict				=  ''''''
					_A :				Tuple				=  self._level
					for i, (k, v) in enumerate(self._pointer.items()			):
									if isinstance(UpperCAmelCase__   ,     UpperCAmelCase__			):
													r += F"""{t * (self._level)}{v}\n"""
													self._level += 1
									else:
													r += F"""{t * (self._level)}{k}: {v} ({type(UpperCAmelCase__			).__name__})\n"""
									_A :				Tuple				=  level
					return r[:-1]




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Dict:
					_A	,       _A :				List[str]				=  cls.get_config_dict(UpperCAmelCase__   ,     **UpperCAmelCase__			)
					return cls(UpperCAmelCase__			)




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Any:
					_A :				str				=  kwargs.pop('''cache_dir'''   ,     UpperCAmelCase__			)
					_A :				Optional[int]				=  kwargs.pop('''force_download'''   ,     UpperCAmelCase__			)
					_A :				Dict				=  kwargs.pop('''resume_download'''   ,     UpperCAmelCase__			)
					_A :				Dict				=  kwargs.pop('''proxies'''   ,     UpperCAmelCase__			)
					_A :				Union[str, Any]				=  kwargs.pop('''local_files_only'''   ,     UpperCAmelCase__			)

					if os.path.isdir(UpperCAmelCase__			):
									_A :				Tuple				=  os.path.join(UpperCAmelCase__   ,     UpperCAmelCase__			)
					elif os.path.isfile(UpperCAmelCase__			) or is_remote_url(UpperCAmelCase__			):
									_A :				str				=  pretrained_model_name_or_path
					else:
									_A :				int				=  hf_bucket_url(UpperCAmelCase__   ,     filename=UpperCAmelCase__   ,     use_cdn=UpperCAmelCase__			)

					try:
									# Load from URL or cache if already cached
									_A :				Optional[Any]				=  cached_path(
									    UpperCAmelCase__   ,     cache_dir=UpperCAmelCase__   ,     force_download=UpperCAmelCase__   ,     proxies=UpperCAmelCase__   ,     resume_download=UpperCAmelCase__   ,     local_files_only=UpperCAmelCase__   ,     )
									# Load config dict
									if resolved_config_file is None:
													raise EnvironmentError

									_A :				Dict				=  Config.load_yaml(UpperCAmelCase__			)

					except EnvironmentError:
									_A :				List[str]				=  '''Can\'t load config for'''
									raise EnvironmentError(UpperCAmelCase__			)

					if resolved_config_file == config_file:
									print('''loading configuration file from path'''			)
					else:
									print('''loading configuration file cache'''			)

					return Config.load_yaml(UpperCAmelCase__			), kwargs





def      lowercase  (  lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				List[Any]				=  torch.load('''dump.pt'''   , map_location=in_tensor.device)
				_A :				List[Any]				=  in_tensor.numpy()
				_A :				List[Any]				=  out_tensor.numpy()[0]
				print(na.shape   , na[0, 0, :5])
				print(na.shape   , na[0, 0, :5])
				assert np.allclose(lowerCAmelCase   , lowerCAmelCase   , rtol=0.0_1   , atol=0.1), (
				    f"""{sum([1 for x in np.isclose(lowerCAmelCase   , lowerCAmelCase   , rtol=0.0_1   , atol=0.1).flatten() if x is False])/len(na.flatten())*100:.4f} %"""
				    " element-wise mismatch"
				)
				raise Exception('''tensors are all good''')

				# Hugging face functions below





def      lowercase  (  lowerCAmelCase :						List[str]):



				"""simple docstring"""


				_A :				str				=  urlparse(lowerCAmelCase)
				return parsed.scheme in ("http", "https")





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						str   , lowerCAmelCase :						Dict=True):



				"""simple docstring"""


				_A :				Any				=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				_A :				str				=  '''/''' not in model_id
				if legacy_format:
								return f"""{endpoint}/{model_id}-{filename}"""
				else:
								return f"""{endpoint}/{model_id}/{filename}"""





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						List[Any]=None   , lowerCAmelCase :						Optional[Any]=0   , lowerCAmelCase :						Dict=None   , ):



				"""simple docstring"""


				_A :				Any				=  '''python/{}'''.format(sys.version.split()[0])
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__)
				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								ua += "; " + "; ".join('''{}/{}'''.format(lowerCAmelCase   , lowerCAmelCase) for k, v in user_agent.items())
				elif isinstance(lowerCAmelCase   , lowerCAmelCase):
								ua += "; " + user_agent
				_A :				Dict				=  {'''user-agent''': ua}
				if resume_size > 0:
								_A :				int				=  '''bytes=%d-''' % (resume_size,)
				_A :				Optional[Any]				=  requests.get(lowerCAmelCase   , stream=lowerCAmelCase   , proxies=lowerCAmelCase   , headers=lowerCAmelCase)
				if response.status_code == 416:  # Range not satisfiable
								return
				_A :				List[Any]				=  response.headers.get('''Content-Length''')
				_A :				str				=  resume_size + int(lowerCAmelCase) if content_length is not None else None
				_A :				Dict				=  tqdm(
				    unit='''B'''   , unit_scale=lowerCAmelCase   , total=lowerCAmelCase   , initial=lowerCAmelCase   , desc='''Downloading'''   , )
				for chunk in response.iter_content(chunk_size=1024):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(lowerCAmelCase))
												temp_file.write(lowerCAmelCase)
				progress.close()





def      lowercase  (  lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						int=None   , lowerCAmelCase :						List[Any]=False   , lowerCAmelCase :						List[Any]=None   , lowerCAmelCase :						str=10   , lowerCAmelCase :						List[Any]=False   , lowerCAmelCase :						Optional[Any]=None   , lowerCAmelCase :						List[Any]=False   , ):



				"""simple docstring"""


				if cache_dir is None:
								_A :				Optional[Any]				=  TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								_A :				str				=  str(lowerCAmelCase)

				os.makedirs(lowerCAmelCase   , exist_ok=lowerCAmelCase)

				_A :				int				=  None
				if not local_files_only:
								try:
												_A :				Any				=  requests.head(lowerCAmelCase   , allow_redirects=lowerCAmelCase   , proxies=lowerCAmelCase   , timeout=lowerCAmelCase)
												if response.status_code == 200:
																_A :				Tuple				=  response.headers.get('''ETag''')
								except (EnvironmentError, requests.exceptions.Timeout):
												# etag is already None
												pass

				_A :				Any				=  url_to_filename(lowerCAmelCase   , lowerCAmelCase)

				# get cache path to put the file
				_A :				str				=  os.path.join(lowerCAmelCase   , lowerCAmelCase)

				# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
				# try to get the last downloaded one
				if etag is None:
								if os.path.exists(lowerCAmelCase):
												return cache_path
								else:
												_A :				str				=  [
												    file
												    for file in fnmatch.filter(os.listdir(lowerCAmelCase)   , filename + '''.*''')
												    if not file.endswith('''.json''') and not file.endswith('''.lock''')
												]
												if len(lowerCAmelCase) > 0:
																return os.path.join(lowerCAmelCase   , matching_files[-1])
												else:
																# If files cannot be found and local_files_only=True,
																# the models might've been found if local_files_only=False
																# Notify the user about that
																if local_files_only:
																				raise ValueError(
																				    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
																				    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
																				    ''' to False.''')
																return None

    # From now on, etag is not None.
				if os.path.exists(lowerCAmelCase) and not force_download:
								return cache_path

				# Prevent parallel downloads of the same file with a lock.
				_A :				List[Any]				=  cache_path + '''.lock'''
				with FileLock(lowerCAmelCase):
								# If the download just completed while the lock was activated.
								if os.path.exists(lowerCAmelCase) and not force_download:
												# Even if returning early like here, the lock will be released.
												return cache_path

								if resume_download:
												_A :				Tuple				=  cache_path + '''.incomplete'''

												@contextmanager
												def _resumable_file_manager():
																with open(lowerCAmelCase   , '''a+b''') as f:
																				yield f

												_A :				int				=  _resumable_file_manager
												if os.path.exists(lowerCAmelCase):
																_A :				Dict				=  os.stat(lowerCAmelCase).st_size
												else:
																_A :				str				=  0
								else:
												_A :				Dict				=  partial(tempfile.NamedTemporaryFile   , dir=lowerCAmelCase   , delete=lowerCAmelCase)
												_A :				List[str]				=  0

								# Download to temporary file, then copy to cache dir once finished.
								# Otherwise you get corrupt cache entries if the download gets interrupted.
								with temp_file_manager() as temp_file:
												print(
												    '''%s not found in cache or force_download set to True, downloading to %s'''   , lowerCAmelCase   , temp_file.name   , )

												http_get(
												    lowerCAmelCase   , lowerCAmelCase   , proxies=lowerCAmelCase   , resume_size=lowerCAmelCase   , user_agent=lowerCAmelCase   , )

								os.replace(temp_file.name   , lowerCAmelCase)

								_A :				Dict				=  {'''url''': url, '''etag''': etag}
								_A :				str				=  cache_path + '''.json'''
								with open(lowerCAmelCase   , '''w''') as meta_file:
												json.dump(lowerCAmelCase   , lowerCAmelCase)

				return cache_path





def      lowercase  (  lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						int=None):



				"""simple docstring"""


				_A :				Any				=  url.encode('''utf-8''')
				_A :				Dict				=  shaaaa(lowerCAmelCase)
				_A :				int				=  url_hash.hexdigest()

				if etag:
								_A :				str				=  etag.encode('''utf-8''')
								_A :				Dict				=  shaaaa(lowerCAmelCase)
								filename += "." + etag_hash.hexdigest()

				if url.endswith('''.h5'''):
								filename += ".h5"

				return filename





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]=None   , lowerCAmelCase :						Optional[Any]=False   , lowerCAmelCase :						str=None   , lowerCAmelCase :						Union[str, Any]=False   , lowerCAmelCase :						Optional[int]=None   , lowerCAmelCase :						str=False   , lowerCAmelCase :						Dict=False   , lowerCAmelCase :						Any=False   , ):



				"""simple docstring"""


				if cache_dir is None:
								_A :				Dict				=  TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								_A :				List[Any]				=  str(lowerCAmelCase)
				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								_A :				List[str]				=  str(lowerCAmelCase)

				if is_remote_url(lowerCAmelCase):
								# URL, so get it from the cache (downloading if necessary)
								_A :				Optional[Any]				=  get_from_cache(
								    lowerCAmelCase   , cache_dir=lowerCAmelCase   , force_download=lowerCAmelCase   , proxies=lowerCAmelCase   , resume_download=lowerCAmelCase   , user_agent=lowerCAmelCase   , local_files_only=lowerCAmelCase   , )
				elif os.path.exists(lowerCAmelCase):
								# File, and it exists.
								_A :				Union[str, Any]				=  url_or_filename
				elif urlparse(lowerCAmelCase).scheme == "":
								# File, but it doesn't exist.
								raise EnvironmentError('''file {} not found'''.format(lowerCAmelCase))
				else:
								# Something unknown
								raise ValueError('''unable to parse {} as a URL or as a local path'''.format(lowerCAmelCase))

				if extract_compressed_file:
								if not is_zipfile(lowerCAmelCase) and not tarfile.is_tarfile(lowerCAmelCase):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								_A	,       _A :				Tuple				=  os.path.split(lowerCAmelCase)
								_A :				Dict				=  output_file.replace('''.'''   , '''-''') + '''-extracted'''
								_A :				Optional[Any]				=  os.path.join(lowerCAmelCase   , lowerCAmelCase)

								if os.path.isdir(lowerCAmelCase) and os.listdir(lowerCAmelCase) and not force_extract:
												return output_path_extracted

								# Prevent parallel extractions
								_A :				Union[str, Any]				=  output_path + '''.lock'''
								with FileLock(lowerCAmelCase):
												shutil.rmtree(lowerCAmelCase   , ignore_errors=lowerCAmelCase)
												os.makedirs(lowerCAmelCase)
												if is_zipfile(lowerCAmelCase):
																with ZipFile(lowerCAmelCase   , '''r''') as zip_file:
																				zip_file.extractall(lowerCAmelCase)
																				zip_file.close()
												elif tarfile.is_tarfile(lowerCAmelCase):
																_A :				List[str]				=  tarfile.open(lowerCAmelCase)
																tar_file.extractall(lowerCAmelCase)
																tar_file.close()
												else:
																raise EnvironmentError('''Archive format of {} could not be identified'''.format(lowerCAmelCase))

								return output_path_extracted

				return output_path





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str=","):



				"""simple docstring"""


				assert isinstance(lowerCAmelCase   , lowerCAmelCase)
				if os.path.isfile(lowerCAmelCase):
								with open(lowerCAmelCase) as f:
												_A :				Optional[int]				=  eval(f.read())
				else:
								_A :				Union[str, Any]				=  requests.get(lowerCAmelCase)
								try:
												_A :				Dict				=  requests.json()
								except Exception:
												_A :				Any				=  req.content.decode()
												assert data is not None, "could not connect"
												try:
																_A :				List[Any]				=  eval(lowerCAmelCase)
												except Exception:
																_A :				Dict				=  data.split('''\n''')
								req.close()
				return data





def      lowercase  (  lowerCAmelCase :						List[str]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  requests.get(lowerCAmelCase)
				_A :				List[str]				=  np.array(Image.open(BytesIO(response.content)))
				return img





def      lowercase  (  lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				str				=  url.split('''/''')[-1]
				if fn not in os.listdir(os.getcwd()):
								wget.download(lowerCAmelCase)
				with open(lowerCAmelCase   , '''rb''') as stream:
								_A :				List[str]				=  pkl.load(lowerCAmelCase)
				_A :				Dict				=  weights.pop('''model''')
				_A :				str				=  {}
				for k, v in model.items():
								_A :				Any				=  torch.from_numpy(lowerCAmelCase)
								if "running_var" in k:
												_A :				int				=  torch.tensor([0])
												_A :				List[Any]				=  k.replace('''running_var'''   , '''num_batches_tracked''')
												_A :				Any				=  zero
				return new





def      lowercase  (  ):



				"""simple docstring"""


				print(f"""{os.path.abspath(os.path.join(lowerCAmelCase   , os.pardir))}/demo.ipynb""")





def      lowercase  (  lowerCAmelCase :						List[str]   , lowerCAmelCase :						Optional[Any]="RGB"):



				"""simple docstring"""


				assert isinstance(lowerCAmelCase   , lowerCAmelCase)
				if os.path.isfile(lowerCAmelCase):
								_A :				Optional[Any]				=  cva.imread(lowerCAmelCase)
				else:
								_A :				List[Any]				=  get_image_from_url(lowerCAmelCase)
								assert img is not None, f"""could not connect to: {im}"""
				_A :				Any				=  cva.cvtColor(lowerCAmelCase   , cva.COLOR_BGR2RGB)
				if input_format == "RGB":
								_A :				List[Any]				=  img[:, :, ::-1]
				return img





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict=1):



				"""simple docstring"""


				return (images[i : i + batch] for i in range(0   , len(lowerCAmelCase)   , lowerCAmelCase))







'''simple docstring'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
