
"""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

						lowerCAmelCase__				=					True
except ImportError:
						lowerCAmelCase__				=					False


try:
						from torch.hub import _get_torch_home

						lowerCAmelCase__				=					_get_torch_home()
except ImportError:
						lowerCAmelCase__				=					os.path.expanduser(
						    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
						)

lowerCAmelCase__				=					os.path.join(torch_cache_home, 'transformers')

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



def 							_lowerCamelCase			(			__a=OBJECTS,				__a=ATTRIBUTES     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		with open(__a     ) as f:
				for object in f.readlines():
						vg_classes.append(object.split(''','''     )[0].lower().strip()     )

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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			OrderedDict()
		with open(__a,				'''rb'''     ) as f:
				SCREAMING_SNAKE_CASE_    			=			pkl.load(__a     )['''model''']
		for k in copy.deepcopy(list(ckp.keys()     )     ):
				SCREAMING_SNAKE_CASE_    			=			ckp.pop(__a     )
				if isinstance(__a,				np.ndarray     ):
						SCREAMING_SNAKE_CASE_    			=			torch.tensor(__a     )
				else:
						assert isinstance(__a,				torch.tensor     ), type(__a     )
				SCREAMING_SNAKE_CASE_    			=			v
		return r





class 			snake_case  :
							UpperCAmelCase__      =   {}

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = "root"							,							SCREAMING_SNAKE_CASE_=0     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			name
									SCREAMING_SNAKE_CASE_    			=			level
									SCREAMING_SNAKE_CASE_    			=			{}
									for k, v in dictionary.items():
											if v is None:
													raise ValueError()
											SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(SCREAMING_SNAKE_CASE_     )
											if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			Config(SCREAMING_SNAKE_CASE_							,							name=SCREAMING_SNAKE_CASE_							,							level=level + 1     )
											SCREAMING_SNAKE_CASE_    			=			v
											setattr(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			d

							def __repr__(self     ):

									"""simple docstring"""







									return str(list((self._pointer.keys())     )     )

							def __setattr__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			val
									SCREAMING_SNAKE_CASE_    			=			val
									SCREAMING_SNAKE_CASE_    			=			key.split('''.'''     )
									SCREAMING_SNAKE_CASE_    			=			len(SCREAMING_SNAKE_CASE_     ) - 1
									SCREAMING_SNAKE_CASE_    			=			self._pointer
									if len(SCREAMING_SNAKE_CASE_     ) > 1:
											for i, l in enumerate(SCREAMING_SNAKE_CASE_     ):
													if hasattr(self							,							SCREAMING_SNAKE_CASE_     ) and isinstance(getattr(self							,							SCREAMING_SNAKE_CASE_     )							,							SCREAMING_SNAKE_CASE_     ):
															setattr(getattr(self							,							SCREAMING_SNAKE_CASE_     )							,							'''.'''.join(levels[i:]     )							,							SCREAMING_SNAKE_CASE_     )
													if l == last_level:
															SCREAMING_SNAKE_CASE_    			=			val
													else:
															SCREAMING_SNAKE_CASE_    			=			pointer[l]

							def _lowercase       (self     ):

									"""simple docstring"""







									return self._pointer

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									with open(f'{file_name}'							,							'''w'''     ) as stream:
											dump(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									with open(f'{file_name}'							,							'''w'''     ) as stream:
											json.dump(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							@staticmethod
							def _lowercase       (SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									with open(SCREAMING_SNAKE_CASE_     ) as stream:
											SCREAMING_SNAKE_CASE_    			=			load(SCREAMING_SNAKE_CASE_							,							Loader=SCREAMING_SNAKE_CASE_     )
									return data

							def __str__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			'''    '''
									if self._name != "root":
											SCREAMING_SNAKE_CASE_    			=			f'{t * (self._level-1)}{self._name}:\n'
									else:
											SCREAMING_SNAKE_CASE_    			=			''''''
									SCREAMING_SNAKE_CASE_    			=			self._level
									for i, (k, v) in enumerate(self._pointer.items()     ):
											if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
													r += f'{t * (self._level)}{v}\n'
													self._level += 1
											else:
													r += f'{t * (self._level)}{k}: {v} ({type(SCREAMING_SNAKE_CASE_     ).__name__})\n'
											SCREAMING_SNAKE_CASE_    			=			level
									return r[:-1]

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			cls.get_config_dict(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
									return cls(SCREAMING_SNAKE_CASE_     )







							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''cache_dir'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''force_download'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''resume_download'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''proxies'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''local_files_only'''							,							SCREAMING_SNAKE_CASE_     )

									if os.path.isdir(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			os.path.join(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
									elif os.path.isfile(SCREAMING_SNAKE_CASE_     ) or is_remote_url(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			pretrained_model_name_or_path
									else:
											SCREAMING_SNAKE_CASE_    			=			hf_bucket_url(SCREAMING_SNAKE_CASE_							,							filename=SCREAMING_SNAKE_CASE_							,							use_cdn=SCREAMING_SNAKE_CASE_     )

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

											SCREAMING_SNAKE_CASE_    			=			Config.load_yaml(SCREAMING_SNAKE_CASE_     )

									except EnvironmentError:
											SCREAMING_SNAKE_CASE_    			=			'''Can\'t load config for'''
											raise EnvironmentError(SCREAMING_SNAKE_CASE_     )

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

									return Config.load_yaml(SCREAMING_SNAKE_CASE_     ), kwargs







def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			torch.load('''dump.pt''',				map_location=in_tensor.device     )
		SCREAMING_SNAKE_CASE_    			=			in_tensor.numpy()
		SCREAMING_SNAKE_CASE_    			=			out_tensor.numpy()[0]
		print(na.shape,				na[0, 0, :5]     )
		print(na.shape,				na[0, 0, :5]     )
		assert np.allclose(__a,				__a,				rtol=0.0_1,				atol=0.1     ), (
		    F'{sum([1 for x in np.isclose(__a,				__a,				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 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			urlparse(__a     )
		return parsed.scheme in ("http", "https")



def 							_lowerCamelCase			(			__a,				__a,				__a=True     ):
		SCREAMING_SNAKE_CASE_    			=			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		SCREAMING_SNAKE_CASE_    			=			'''/''' not in model_id
		if legacy_format:
				return F'{endpoint}/{model_id}-{filename}'
		else:
				return F'{endpoint}/{model_id}/{filename}'



def 							_lowerCamelCase			(			__a,				__a,				__a=None,				__a=0,				__a=None,				):
		SCREAMING_SNAKE_CASE_    			=			'''python/{}'''.format(sys.version.split()[0]     )
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__     )
		if isinstance(__a,				__a     ):
				ua += "; " + "; ".join('''{}/{}'''.format(__a,				__a     ) for k, v in user_agent.items()     )
		elif isinstance(__a,				__a     ):
				ua += "; " + user_agent
		SCREAMING_SNAKE_CASE_    			=			{'''user-agent''': ua}
		if resume_size > 0:
				SCREAMING_SNAKE_CASE_    			=			'''bytes=%d-''' % (resume_size,)
		SCREAMING_SNAKE_CASE_    			=			requests.get(__a,				stream=__a,				proxies=__a,				headers=__a     )
		if response.status_code == 416:  # Range not satisfiable
				return
		SCREAMING_SNAKE_CASE_    			=			response.headers.get('''Content-Length'''     )
		SCREAMING_SNAKE_CASE_    			=			resume_size + int(__a     ) if content_length is not None else None
		SCREAMING_SNAKE_CASE_    			=			tqdm(
		    unit='''B''',				unit_scale=__a,				total=__a,				initial=__a,				desc='''Downloading''',				)
		for chunk in response.iter_content(chunk_size=1_024     ):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(__a     )     )
						temp_file.write(__a     )
		progress.close()



def 							_lowerCamelCase			(			__a,				__a=None,				__a=False,				__a=None,				__a=10,				__a=False,				__a=None,				__a=False,				):
		if cache_dir is None:
				SCREAMING_SNAKE_CASE_    			=			TRANSFORMERS_CACHE
		if isinstance(__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			str(__a     )

		os.makedirs(__a,				exist_ok=__a     )

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

		SCREAMING_SNAKE_CASE_    			=			url_to_filename(__a,				__a     )

		# get cache path to put the file
		SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )

		# 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(__a     ):
						return cache_path
				else:
						SCREAMING_SNAKE_CASE_    			=			[
						    file
						    for file in fnmatch.filter(os.listdir(__a     ),				filename + '''.*'''     )
						    if not file.endswith('''.json'''     ) and not file.endswith('''.lock'''     )
						]
						if len(__a     ) > 0:
								return os.path.join(__a,				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(__a     ) and not force_download:
				return cache_path

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

				if resume_download:
						SCREAMING_SNAKE_CASE_    			=			cache_path + '''.incomplete'''

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

						SCREAMING_SNAKE_CASE_    			=			_resumable_file_manager
						if os.path.exists(__a     ):
								SCREAMING_SNAKE_CASE_    			=			os.stat(__a     ).st_size
						else:
								SCREAMING_SNAKE_CASE_    			=			0
				else:
						SCREAMING_SNAKE_CASE_    			=			partial(tempfile.NamedTemporaryFile,				dir=__a,				delete=__a     )
						SCREAMING_SNAKE_CASE_    			=			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''',				__a,				temp_file.name,				)

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

				os.replace(temp_file.name,				__a     )

				SCREAMING_SNAKE_CASE_    			=			{'''url''': url, '''etag''': etag}
				SCREAMING_SNAKE_CASE_    			=			cache_path + '''.json'''
				with open(__a,				'''w'''     ) as meta_file:
						json.dump(__a,				__a     )

		return cache_path



def 							_lowerCamelCase			(			__a,				__a=None     ):
		SCREAMING_SNAKE_CASE_    			=			url.encode('''utf-8'''     )
		SCREAMING_SNAKE_CASE_    			=			shaaaa(__a     )
		SCREAMING_SNAKE_CASE_    			=			url_hash.hexdigest()

		if etag:
				SCREAMING_SNAKE_CASE_    			=			etag.encode('''utf-8'''     )
				SCREAMING_SNAKE_CASE_    			=			shaaaa(__a     )
				filename += "." + etag_hash.hexdigest()

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

		return filename



def 							_lowerCamelCase			(			__a,				__a=None,				__a=False,				__a=None,				__a=False,				__a=None,				__a=False,				__a=False,				__a=False,				):
		if cache_dir is None:
				SCREAMING_SNAKE_CASE_    			=			TRANSFORMERS_CACHE
		if isinstance(__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			str(__a     )
		if isinstance(__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			str(__a     )

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

		if extract_compressed_file:
				if not is_zipfile(__a     ) and not tarfile.is_tarfile(__a     ):
						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/"
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			os.path.split(__a     )
				SCREAMING_SNAKE_CASE_    			=			output_file.replace('''.''',				'''-'''     ) + '''-extracted'''
				SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )

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

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

				return output_path_extracted

		return output_path



def 							_lowerCamelCase			(			__a,				__a=","     ):
		assert isinstance(__a,				__a     )
		if os.path.isfile(__a     ):
				with open(__a     ) as f:
						SCREAMING_SNAKE_CASE_    			=			eval(f.read()     )
		else:
				SCREAMING_SNAKE_CASE_    			=			requests.get(__a     )
				try:
						SCREAMING_SNAKE_CASE_    			=			requests.json()
				except Exception:
						SCREAMING_SNAKE_CASE_    			=			req.content.decode()
						assert data is not None, "could not connect"
						try:
								SCREAMING_SNAKE_CASE_    			=			eval(__a     )
						except Exception:
								SCREAMING_SNAKE_CASE_    			=			data.split('''\n'''     )
				req.close()
		return data



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			requests.get(__a     )
		SCREAMING_SNAKE_CASE_    			=			np.array(Image.open(BytesIO(response.content     )     )     )
		return img



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



def 							_lowerCamelCase			(			):
		print(F'{os.path.abspath(os.path.join(__a,				os.pardir     )     )}/demo.ipynb'     )



def 							_lowerCamelCase			(			__a,				__a="RGB"     ):
		assert isinstance(__a,				__a     )
		if os.path.isfile(__a     ):
				SCREAMING_SNAKE_CASE_    			=			cva.imread(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			get_image_from_url(__a     )
				assert img is not None, F'could not connect to: {im}'
		SCREAMING_SNAKE_CASE_    			=			cva.cvtColor(__a,				cva.COLOR_BGR2RGB     )
		if input_format == "RGB":
				SCREAMING_SNAKE_CASE_    			=			img[:, :, ::-1]
		return img



def 							_lowerCamelCase			(			__a,				__a=1     ):
		return (images[i : i + batch] for i in range(0,				len(__a     ),				__a     ))

"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   True
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   '''tokenizer_file'''
							UpperCAmelCase__      =   {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

							def _lowercase       (self     ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_    			=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''     )
									tokenizer.save_pretrained(self.tmpdirname     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map     )
									return BloomTokenizerFast.from_pretrained(self.tmpdirname							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_    			=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
									SCREAMING_SNAKE_CASE_    			=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_     )['''input_ids''']
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=6     ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
													SCREAMING_SNAKE_CASE_    			=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													SCREAMING_SNAKE_CASE_    			=			'''This is a simple input'''
													SCREAMING_SNAKE_CASE_    			=			['''This is a simple input 1''', '''This is a simple input 2''']
													SCREAMING_SNAKE_CASE_    			=			('''This is a simple input''', '''This is a pair''')
													SCREAMING_SNAKE_CASE_    			=			[
													    ('''This is a simple input 1''', '''This is a simple input 2'''),
													    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
													]

													# Simple input tests
													try:
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )

															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
													except ValueError:
															self.fail('''Bloom Tokenizer should be able to deal with padding'''     )

													SCREAMING_SNAKE_CASE_    			=			None  # Hotfixing padding = None
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_    			=			load_dataset('''xnli'''							,							'''all_languages'''							,							split='''test'''							,							streaming=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			next(iter(SCREAMING_SNAKE_CASE_     )     )['''premise''']  # pick up one data
									SCREAMING_SNAKE_CASE_    			=			list(sample_data.values()     )

									SCREAMING_SNAKE_CASE_    			=			list(map(tokenizer.encode							,							SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			[tokenizer.decode(SCREAMING_SNAKE_CASE_							,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     ) for x in output_tokens]
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )							,							1     )
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )							,							1     )