






'''simple docstring'''




import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


__lowerCamelCase   :					Dict   							=  get_logger(__name__)


__lowerCamelCase   :					Any   							=  Path(__file__).parent / """model_card_template.md"""
__lowerCamelCase   :					int   							=  uuida().hex
__lowerCamelCase   :					List[str]   							=  os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase   :					List[Any]   							=  os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase   :					Tuple   							=  HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""

def 	__UpperCAmelCase       ( __magic_name__ = None						)->    str:



			"""simple docstring"""



			snake_case_				:					List[str]   =       F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += F'''; torch/{_torch_version}'''
			if is_flax_available():
						ua += F'''; jax/{_jax_version}'''
						ua += F'''; flax/{_flax_version}'''
			if is_onnx_available():
						ua += F'''; onnxruntime/{_onnxruntime_version}'''
			# CI will set this value to True
			if os.environ.get("DIFFUSERS_IS_CI"     ,""						).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(__magic_name__     ,__magic_name__						):
						ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()						)
			elif isinstance(__magic_name__     ,__magic_name__						):
						ua += "; " + user_agent
			return ua

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = None     ,__magic_name__ = None						)->    Union[str, Any]:



			"""simple docstring"""



			if token is None:
						snake_case_				:					Optional[Any]   =       HfFolder.get_token()
			if organization is None:
						snake_case_				:					str   =       whoami(__magic_name__						)["name"]
						return F'''{username}/{model_id}'''
			else:
						return F'''{organization}/{model_id}'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Any:



			"""simple docstring"""



			if not is_jinja_available():
						raise ValueError(
						    "Modelcard rendering is based on Jinja templates."
						    " Please make sure to have `jinja` installed before using `create_model_card`."
						    " To install it, please run `pip install Jinja2`."						)

			if hasattr(__magic_name__     ,"local_rank"						) and args.local_rank not in [-1, 0]:
						return

			snake_case_				:					int   =       args.hub_token if hasattr(__magic_name__     ,"hub_token"						) else None
			snake_case_				:					Union[str, Any]   =       get_full_repo_name(__magic_name__     ,token=__magic_name__						)

			snake_case_				:					int   =       ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language="en"     ,license="apache-2.0"     ,library_name="diffusers"     ,tags=[]     ,datasets=args.dataset_name     ,metrics=[]     ,)     ,template_path=__magic_name__     ,model_name=__magic_name__     ,repo_name=__magic_name__     ,dataset_name=args.dataset_name if hasattr(__magic_name__     ,"dataset_name"						) else None     ,learning_rate=args.learning_rate     ,train_batch_size=args.train_batch_size     ,eval_batch_size=args.eval_batch_size     ,gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(__magic_name__     ,"gradient_accumulation_steps"						) else None
			    )     ,adam_betaa=args.adam_betaa if hasattr(__magic_name__     ,"adam_beta1"						) else None     ,adam_betaa=args.adam_betaa if hasattr(__magic_name__     ,"adam_beta2"						) else None     ,adam_weight_decay=args.adam_weight_decay if hasattr(__magic_name__     ,"adam_weight_decay"						) else None     ,adam_epsilon=args.adam_epsilon if hasattr(__magic_name__     ,"adam_epsilon"						) else None     ,lr_scheduler=args.lr_scheduler if hasattr(__magic_name__     ,"lr_scheduler"						) else None     ,lr_warmup_steps=args.lr_warmup_steps if hasattr(__magic_name__     ,"lr_warmup_steps"						) else None     ,ema_inv_gamma=args.ema_inv_gamma if hasattr(__magic_name__     ,"ema_inv_gamma"						) else None     ,ema_power=args.ema_power if hasattr(__magic_name__     ,"ema_power"						) else None     ,ema_max_decay=args.ema_max_decay if hasattr(__magic_name__     ,"ema_max_decay"						) else None     ,mixed_precision=args.mixed_precision     ,)

			snake_case_				:					Dict   =       os.path.join(args.output_dir     ,"README.md"						)
			model_card.save(__magic_name__						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = None						)->    str:



			"""simple docstring"""



			if resolved_file is None or commit_hash is not None:
						return commit_hash
			snake_case_				:					List[str]   =       str(Path(__magic_name__						).as_posix()						)
			snake_case_				:					List[Any]   =       re.search(r"snapshots/([^/]+)/"     ,__magic_name__						)
			if search is None:
						return None
			snake_case_				:					Optional[Any]   =       search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(__magic_name__						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__lowerCamelCase   :					List[str]   							=  os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__lowerCamelCase   :					Optional[Any]   							=  os.path.join(hf_cache_home, '''diffusers''')

def 	__UpperCAmelCase       ( __magic_name__ = None     ,__magic_name__ = None						)->    None:



			"""simple docstring"""



			if new_cache_dir is None:
						snake_case_				:					Tuple   =       DIFFUSERS_CACHE
			if old_cache_dir is None:
						snake_case_				:					Dict   =       old_diffusers_cache

			snake_case_				:					str   =       Path(__magic_name__						).expanduser()
			snake_case_				:					int   =       Path(__magic_name__						).expanduser()
			for old_blob_path in old_cache_dir.glob("**/blobs/*"						):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									snake_case_				:					Optional[int]   =       new_cache_dir / old_blob_path.relative_to(__magic_name__						)
									new_blob_path.parent.mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
									os.replace(__magic_name__     ,__magic_name__						)
									try:
												os.symlink(__magic_name__     ,__magic_name__						)
									except OSError:
												logger.warning(
												    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."						)
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


__lowerCamelCase   :					str   							=  os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
		__lowerCamelCase   :					Any   							=  0
else:
		with open(cache_version_file) as f:
				try:
						__lowerCamelCase   :					Optional[int]   							=  int(f.read())
				except ValueError:
						__lowerCamelCase   :					Optional[Any]   							=  0

if cache_version < 1:
		__lowerCamelCase   :					Union[str, Any]   							=  os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
		if old_cache_is_not_empty:
				logger.warning(
				    '''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
				    '''existing cached models. This is a one-time operation, you can interrupt it or run it '''
				    '''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
				)
				try:
						move_cache()
				except Exception as e:
						__lowerCamelCase   :					Optional[Any]   							=  """\n""".join(traceback.format_tb(e.__traceback__))
						logger.error(
						    f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
						    '''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
						    '''message and we will do our best to help.'''
						)

if cache_version < 1:
		try:
				os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
				with open(cache_version_file, '''w''') as f:
						f.write('''1''')
		except Exception:
				logger.warning(
				    f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
				    '''the directory exists and can be written to.'''
				)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = None						)->    str:



			"""simple docstring"""



			if variant is not None:
						snake_case_				:					Optional[Any]   =       weights_name.split("."						)
						snake_case_				:					Tuple   =       splits[:-1] + [variant] + splits[-1:]
						snake_case_				:					Any   =       ".".join(__magic_name__						)

			return weights_name






def 	__UpperCAmelCase       ( __magic_name__     ,*,
    __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=None     ,)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					List[str]   =       str(__magic_name__						)
			if os.path.isfile(__magic_name__						):
						return pretrained_model_name_or_path
			elif os.path.isdir(__magic_name__						):
						if os.path.isfile(os.path.join(__magic_name__     ,__magic_name__						)						):
									# Load from a PyTorch checkpoint
									snake_case_				:					List[str]   =       os.path.join(__magic_name__     ,__magic_name__						)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(__magic_name__     ,__magic_name__     ,__magic_name__						)						):
									snake_case_				:					Union[str, Any]   =       os.path.join(__magic_name__     ,__magic_name__     ,__magic_name__						)
									return model_file
						else:
									raise EnvironmentError(
									    F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''						)
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(__magic_name__						).base_version						) >= version.parse("0.20.0"						)
						):
									try:
												snake_case_				:					Any   =       hf_hub_download(
												    __magic_name__     ,filename=_add_variant(__magic_name__     ,__magic_name__						)     ,cache_dir=__magic_name__     ,force_download=__magic_name__     ,proxies=__magic_name__     ,resume_download=__magic_name__     ,local_files_only=__magic_name__     ,use_auth_token=__magic_name__     ,user_agent=__magic_name__     ,subfolder=__magic_name__     ,revision=revision or commit_hash     ,)
												warnings.warn(
												    F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''     ,__magic_name__     ,)
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__magic_name__     ,__magic_name__						)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__magic_name__     ,__magic_name__						)}\' so that the correct variant file can be added.'''     ,__magic_name__     ,)
						try:
									# 2. Load model file as usual
									snake_case_				:					Tuple   =       hf_hub_download(
									    __magic_name__     ,filename=__magic_name__     ,cache_dir=__magic_name__     ,force_download=__magic_name__     ,proxies=__magic_name__     ,resume_download=__magic_name__     ,local_files_only=__magic_name__     ,use_auth_token=__magic_name__     ,user_agent=__magic_name__     ,subfolder=__magic_name__     ,revision=revision or commit_hash     ,)
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
									    "listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a "
									    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
									    "login`."						)
						except RevisionNotFoundError:
									raise EnvironmentError(
									    F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
									    "this model name. Check the model page at "
									    F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''						)
						except EntryNotFoundError:
									raise EnvironmentError(
									    F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''						)
						except HTTPError as err:
									raise EnvironmentError(
									    F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''						)
						except ValueError:
									raise EnvironmentError(
									    F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
									    F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
									    F''' directory containing a file named {weights_name} or'''
									    " \nCheckout your internet connection or see how to run the library in"
									    " offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'."						)
						except EnvironmentError:
									raise EnvironmentError(
									    F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
									    "\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. "
									    F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
									    F'''containing a file named {weights_name}'''						)












'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCamelCase   :					List[str]   							=					{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							a__   		=	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														a__   		=	{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														a__   		=	{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Dict				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       ZeroShotClassificationPipeline(
										    model=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							candidate_labels=["polics", "health"]				)
										return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# No kwarg
										snake_case_				:					List[Any]   =       classifier("Who are you voting for in 2020?"       ,							["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					Dict   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					int   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics, public health"				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					Optional[int]   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					str   =       classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="This text is about {}"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# https://github.com/huggingface/transformers/issues/13846
										snake_case_				:					Dict   =       classifier(["I am happy"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(1				)
										    ]       ,							)
										snake_case_				:					Tuple   =       classifier(["I am happy", "I am sad"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(2				)
										    ]       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(""       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(lowerCAmelCase__       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=""				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=lowerCAmelCase__				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="Not formatting template"       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template=lowerCAmelCase__       ,							)

										self.run_entailment_id(lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Pipeline				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       zero_shot_classifier.model.config
										snake_case_				:					Optional[int]   =       config.labelaid
										snake_case_				:					Tuple   =       zero_shot_classifier.entailment_id

										snake_case_				:					Optional[Any]   =       {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							-1				)

										snake_case_				:					Tuple   =       {"entailment": 0, "neutral": 1, "contradiction": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 0, "NON-ENTAIL": 1}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							2				)

										snake_case_				:					List[str]   =       original_labelaid
										self.assertEqual(lowerCAmelCase__       ,							zero_shot_classifier.entailment_id				)

							@require_torch
							def 							_A	(		self			:Tuple				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										# There was a regression in 4.10 for this
										# Adding a test so we don't make the mistake again.
										# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
										zero_shot_classifier(
										    "Who are you voting for in 2020?" * 100       ,							candidate_labels=["politics", "public health", "science"]				)

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										snake_case_				:					int   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@require_tf
							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="tf"       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@slow
							@require_torch
							def 							_A	(		self			:Union[str, Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="pt"				)
										snake_case_				:					str   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)






							@slow
							@require_tf
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="tf"				)
										snake_case_				:					Optional[Any]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Tuple   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)




