
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
lowercase__				:				Optional[int]						=      "\\n"

lowercase__				:				str						=      "\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n"

lowercase__				:				Any						=      "\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

lowercase__				:				Optional[int]						=      ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
lowercase__				:				Tuple						=      ""

try:
				from jiwer import transforms as tr

				lowercase__				:				int						=      True
except ImportError:
				lowercase__				:				Any						=      False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):






				class 							SCREAMING_SNAKE_CASE__     (				tr.AbstractTransform     ):

											"""simple docstring"""






											def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = " "      )-> str:





																	'''simple docstring'''






																	__UpperCamelCase  	=      sentence_delimiter



											def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





																	'''simple docstring'''






																	return list(SCREAMING_SNAKE_CASE_      )



											def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





																	'''simple docstring'''






																	__UpperCamelCase  	=      []
																	for sent_idx, sentence in enumerate(SCREAMING_SNAKE_CASE_      ):
																							chars.extend(self.process_string(SCREAMING_SNAKE_CASE_      )      )
																							if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(SCREAMING_SNAKE_CASE_      ) - 1:
																													chars.append(self.sentence_delimiter      )
																	return chars





				lowercase__				:				int						=      tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				lowercase__				:				Optional[int]						=      tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				lowercase__				:				int						=      None




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						return float((preds == labels).mean()			)




def 						A_			(				snake_case				: Any			,   snake_case				: str			)   ->     Union[str, Any]:



						'''simple docstring'''

						return {
						    "f1": float(fa_score(y_true=snake_case			,   y_pred=snake_case			,   average='''macro'''			)			),
						    "accuracy": simple_accuracy(snake_case			,   snake_case			),
						}




def 						A_			(				snake_case				: Any			,   snake_case				: Tuple			,   snake_case				: Dict="exp"			,   snake_case				: Dict=None			,   snake_case				: Any=False			,   snake_case				: Union[str, Any]=False			,   snake_case				: int=None			,   snake_case				: Any=False			,   )   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      [[label] for label in labels]
						__UpperCamelCase  	=      list(snake_case			)
						try:
												import sacrebleu as scb
						except ImportError:
												raise ValueError(
												    '''sacrebleu has to be installed in order to apply the bleu metric for covost2.'''
												    '''You can install it via `pip install sacrebleu`.'''			)

						if version.parse(scb.__version__			) < version.parse('''1.4.12'''			):
												raise ImportWarning(
												    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
												    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''			)

						__UpperCamelCase  	=      len(labels[0]			)
						if any(len(snake_case			) != references_per_prediction for refs in labels			):
												raise ValueError('''Sacrebleu requires the same number of references for each prediction'''			)
						__UpperCamelCase  	=      [[refs[i] for refs in labels] for i in range(snake_case			)]
						__UpperCamelCase  	=      scb.corpus_bleu(
						    snake_case			,   snake_case			,   smooth_method=snake_case			,   smooth_value=snake_case			,   force=snake_case			,   lowercase=snake_case			,   use_effective_order=snake_case			,   **({'''tokenize''': tokenize} if tokenize else {})			,   )
						return {"bleu": output.score}




def 						A_			(				snake_case				: str			,   snake_case				: Dict			,   snake_case				: Tuple			,   snake_case				: Tuple			)   ->     Dict:



						'''simple docstring'''

						try:
												from jiwer import compute_measures
						except ImportError:
												raise ValueError(
												    f"jiwer has to be installed in order to apply the wer metric for {config_name}."
												    '''You can install it via `pip install jiwer`.'''			)

						if concatenate_texts:
												__UpperCamelCase  	=      compute_measures(snake_case			,   snake_case			)['''wer''']

												__UpperCamelCase  	=      compute_measures(snake_case			,   snake_case			,   truth_transform=snake_case			,   hypothesis_transform=snake_case			)['''wer''']
												return {"wer": wer, "cer": cer}
						else:

												def compute_score(snake_case				: int			,   snake_case				: Optional[Any]			,   snake_case				: Dict="wer"			):
																		__UpperCamelCase  	=      0
																		__UpperCamelCase  	=      0
																		for prediction, reference in zip(snake_case			,   snake_case			):
																								if score_type == "wer":
																														__UpperCamelCase  	=      compute_measures(snake_case			,   snake_case			)
																								elif score_type == "cer":
																														__UpperCamelCase  	=      compute_measures(
																														    snake_case			,   snake_case			,   truth_transform=snake_case			,   hypothesis_transform=snake_case			)
																								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																		return incorrect / total

												return {"wer": compute_score(snake_case			,   snake_case			,   '''wer'''			), "cer": compute_score(snake_case			,   snake_case			,   '''cer'''			)}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,		_KWARGS_DESCRIPTION     )
class 							SCREAMING_SNAKE_CASE__     (				datasets.Metric     ):

							"""simple docstring"""






							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													if self.config_name not in _CONFIG_NAMES:
																			raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"      )

													__UpperCamelCase  	=      '''int64''' if self.config_name in ['''fleurs-lang_id''', '''minds14'''] else '''string'''

													return datasets.MetricInfo(
													    description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
													        {'''predictions''': datasets.Value(SCREAMING_SNAKE_CASE_      ), '''references''': datasets.Value(SCREAMING_SNAKE_CASE_      )}      )  ,						codebase_urls=[]  ,						reference_urls=[]  ,						format='''numpy'''  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      bleu_kwargs if bleu_kwargs is not None else {}
													__UpperCamelCase  	=      wer_kwargs if wer_kwargs is not None else {}

													if self.config_name == "fleurs-lang_id":
																			return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )}
													elif self.config_name == "minds14":
																			return fa_and_simple_accuracy(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													elif self.config_name == "covost2":
																			__UpperCamelCase  	=      bleu_kwargs.pop('''smooth_method'''  ,						'''exp'''      )
																			__UpperCamelCase  	=      bleu_kwargs.pop('''smooth_value'''  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      bleu_kwargs.pop('''force'''  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      bleu_kwargs.pop('''lowercase'''  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      bleu_kwargs.pop('''tokenize'''  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      bleu_kwargs.pop('''use_effective_order'''  ,						SCREAMING_SNAKE_CASE_      )
																			return bleu(
																			    preds=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_  ,						smooth_method=SCREAMING_SNAKE_CASE_  ,						smooth_value=SCREAMING_SNAKE_CASE_  ,						force=SCREAMING_SNAKE_CASE_  ,						lowercase=SCREAMING_SNAKE_CASE_  ,						tokenize=SCREAMING_SNAKE_CASE_  ,						use_effective_order=SCREAMING_SNAKE_CASE_  ,						)
													elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
																			__UpperCamelCase  	=      wer_kwargs.pop('''concatenate_texts'''  ,						SCREAMING_SNAKE_CASE_      )
																			return wer_and_cer(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						self.config_name      )
													else:
																			raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"      )






import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    FunnelBaseModel,
				    FunnelForMaskedLM,
				    FunnelForMultipleChoice,
				    FunnelForPreTraining,
				    FunnelForQuestionAnswering,
				    FunnelForSequenceClassification,
				    FunnelForTokenClassification,
				    FunnelModel,
				)






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=13  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=[1, 1, 2]  ,						SCREAMING_SNAKE_CASE_=1  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=8  ,						SCREAMING_SNAKE_CASE_=37  ,						SCREAMING_SNAKE_CASE_="gelu_new"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=False  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      seq_length
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_input_mask
													__UpperCamelCase  	=      use_token_type_ids
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      block_sizes
													__UpperCamelCase  	=      num_decoder_layers
													__UpperCamelCase  	=      d_model
													__UpperCamelCase  	=      n_head
													__UpperCamelCase  	=      d_head
													__UpperCamelCase  	=      d_inner
													__UpperCamelCase  	=      hidden_act
													__UpperCamelCase  	=      hidden_dropout
													__UpperCamelCase  	=      attention_dropout
													__UpperCamelCase  	=      activation_dropout
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      type_vocab_size
													__UpperCamelCase  	=      2
													__UpperCamelCase  	=      num_labels
													__UpperCamelCase  	=      num_choices
													__UpperCamelCase  	=      scope
													__UpperCamelCase  	=      initializer_std

													# Used in the tests to check the size of the first attention layer
													__UpperCamelCase  	=      n_head
													# Used in the tests to check the size of the first hidden state
													__UpperCamelCase  	=      self.d_model
													# Used in the tests to check the number of output hidden states/attentions
													__UpperCamelCase  	=      sum(self.block_sizes      ) + (0 if base else self.num_decoder_layers)
													# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
													# the last hidden state of the first block (which is the first hidden state of the decoder).
													if not base:
																			__UpperCamelCase  	=      self.num_hidden_layers + 2



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size      )

													__UpperCamelCase  	=      None
													if self.use_input_mask:
																			__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2      )

													__UpperCamelCase  	=      None
													if self.use_token_type_ids:
																			__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size      )

													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      None
													if self.use_labels:
																			__UpperCamelCase  	=      ids_tensor([self.batch_size]  ,						self.type_sequence_label_size      )
																			__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels      )
																			__UpperCamelCase  	=      ids_tensor([self.batch_size]  ,						self.num_choices      )
																			__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						1      )

													__UpperCamelCase  	=      self.get_config()

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_mask,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													    fake_token_labels,
													)



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													return FunnelConfig(
													    vocab_size=self.vocab_size  ,						block_sizes=self.block_sizes  ,						num_decoder_layers=self.num_decoder_layers  ,						d_model=self.d_model  ,						n_head=self.n_head  ,						d_head=self.d_head  ,						d_inner=self.d_inner  ,						hidden_act=self.hidden_act  ,						hidden_dropout=self.hidden_dropout  ,						attention_dropout=self.attention_dropout  ,						activation_dropout=self.activation_dropout  ,						max_position_embeddings=self.max_position_embeddings  ,						type_vocab_size=self.type_vocab_size  ,						initializer_std=self.initializer_std  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModel(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.d_model)      )

													__UpperCamelCase  	=      False
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.d_model)      )

													__UpperCamelCase  	=      False
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.d_model)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelBaseModel(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, 2, self.d_model)      )

													__UpperCamelCase  	=      False
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, 3, self.d_model)      )

													__UpperCamelCase  	=      False
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, 2, self.d_model)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForPreTraining(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelForMaskedLM(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForSequenceClassification(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_choices
													__UpperCamelCase  	=      FunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      input_ids.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      token_type_ids.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      input_mask.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      model(
													    SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_  ,						)
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForTokenClassification(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(
													    SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						start_positions=SCREAMING_SNAKE_CASE_  ,						end_positions=SCREAMING_SNAKE_CASE_  ,						)
													self.parent.assertEqual(result.start_logits.shape  ,						(self.batch_size, self.seq_length)      )
													self.parent.assertEqual(result.end_logits.shape  ,						(self.batch_size, self.seq_length)      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				
													)  	=      config_and_inputs
													__UpperCamelCase  	=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	(
							    (
							        FunnelModel,
							        FunnelForMaskedLM,
							        FunnelForPreTraining,
							        FunnelForQuestionAnswering,
							        FunnelForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case  		=	(
							    {
							        'feature-extraction': (FunnelBaseModel, FunnelModel),
							        'fill-mask': FunnelForMaskedLM,
							        'question-answering': FunnelForQuestionAnswering,
							        'text-classification': FunnelForSequenceClassification,
							        'token-classification': FunnelForTokenClassification,
							        'zero-shot': FunnelForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      super()._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )

													if return_labels:
																			if model_class in get_values(SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE_      )
													return inputs_dict



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''weight'''      ) and module.weight is not None:
																			module.weight.data.fill_(3      )
													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''bias'''      ) and module.bias is not None:
																			module.bias.data.fill_(3      )

													for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																			if hasattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) and getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) is not None:
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									weight.data.fill_(3      )






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	(
							    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
							)



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModelTester(self  ,						base=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase  	=      True

													for model_class in self.all_model_classes:
																			if model_class.__name__ == "FunnelBaseModel":
																									continue
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.train()
																			__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      ).loss
																			loss.backward()



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''weight'''      ) and module.weight is not None:
																			module.weight.data.fill_(3      )
													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''bias'''      ) and module.bias is not None:
																			module.bias.data.fill_(3      )

													for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
																			if hasattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) and getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) is not None:
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									weight.data.fill_(3      )






@require_torch
@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      13
													__UpperCamelCase  	=      7
													__UpperCamelCase  	=      torch.arange(0  ,						batch_size * sequence_length      ).long().reshape(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
													__UpperCamelCase  	=      torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]      )

													__UpperCamelCase  	=      FunnelModel.from_pretrained('''sgugger/funnel-random-tiny'''      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )[0].abs()

													__UpperCamelCase  	=      torch.tensor(2_3_4_4.8_3_5_2      )
													__UpperCamelCase  	=      torch.tensor(0.8_0_5_2      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )

													__UpperCamelCase  	=      torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )[0].abs()

													__UpperCamelCase  	=      torch.tensor(2_3_4_3.8_4_2_5      )
													__UpperCamelCase  	=      torch.tensor(0.8_0_4_9      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )



							@slow
							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelTokenizer.from_pretrained('''huggingface/funnel-small'''      )
													__UpperCamelCase  	=      FunnelModel.from_pretrained('''huggingface/funnel-small'''      )
													__UpperCamelCase  	=      tokenizer('''Hello! I am the Funnel Transformer model.'''  ,						return_tensors='''pt'''      )
													__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      )[0]

													__UpperCamelCase  	=      torch.tensor(2_3_5.7_2_4_6      )
													__UpperCamelCase  	=      torch.tensor(0.0_2_5_6      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )




