





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A					:				List[Any]				=			"""
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

_A					:				Dict				=			"""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


_A					:				Dict				=			"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""


def 							__snake_case							(    lowerCAmelCase_	)				->						List[str]:

    def remove_articles(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			re.compile(r'''\b(a|an|the)\b''' ,							re.UNICODE	)
        return re.sub(lowerCAmelCase_ ,							''' ''' ,							lowerCAmelCase_	)

    def white_space_fix(lowerCAmelCase_	):
        return " ".join(text.split()	)

    def remove_punc(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(lowerCAmelCase_	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_	)	)	)	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    return int(normalize_answer(lowerCAmelCase_	) == normalize_answer(lowerCAmelCase_	)	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__											=			[any(compute_exact(lowerCAmelCase_ ,							lowerCAmelCase_	) for ref in refs	) for pred, refs in zip(lowerCAmelCase_ ,							lowerCAmelCase_	)]
    return (sum(lowerCAmelCase_	) / len(lowerCAmelCase_	)) * 1_0_0


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__											=			scount * numref

    SCREAMING_SNAKE_CASE__											=			Counter(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__											=			ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE__											=			keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep & rgramcounter

    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			keeptmpscorea / len(lowerCAmelCase_	)
    if len(lowerCAmelCase_	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        SCREAMING_SNAKE_CASE__											=			keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    SCREAMING_SNAKE_CASE__											=			0
    if keepscore_precision > 0 or keepscore_recall > 0:
        SCREAMING_SNAKE_CASE__											=			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE__											=			delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__											=			sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			deltmpscorea / len(lowerCAmelCase_	)

    # ADDITION
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) - set(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) & set(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			set(lowerCAmelCase_	) - set(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__											=			1
    SCREAMING_SNAKE_CASE__											=			1
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			addtmpscore / len(lowerCAmelCase_	)
    if len(lowerCAmelCase_	) > 0:
        SCREAMING_SNAKE_CASE__											=			addtmpscore / len(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE__											=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			ssent.split(''' '''	)
    SCREAMING_SNAKE_CASE__											=			csent.split(''' '''	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]

    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]
    for rsent in rsents:
        SCREAMING_SNAKE_CASE__											=			rsent.split(''' '''	)
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        ragramslist.append(lowerCAmelCase_	)
        for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
            if i < len(lowerCAmelCase_	) - 1:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1]
                ragrams.append(lowerCAmelCase_	)
            if i < len(lowerCAmelCase_	) - 2:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                ragrams.append(lowerCAmelCase_	)
            if i < len(lowerCAmelCase_	) - 3:
                SCREAMING_SNAKE_CASE__											=			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                ragrams.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)
        ragramslist.append(lowerCAmelCase_	)

    for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
        if i < len(lowerCAmelCase_	) - 1:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 2:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 3:
            SCREAMING_SNAKE_CASE__											=			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(lowerCAmelCase_	)

    for i in range(0 ,							len(lowerCAmelCase_	) - 1	):
        if i < len(lowerCAmelCase_	) - 1:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 2:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(lowerCAmelCase_	)
        if i < len(lowerCAmelCase_	) - 3:
            SCREAMING_SNAKE_CASE__											=			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(lowerCAmelCase_	)

    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    ((SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__)				,		(SCREAMING_SNAKE_CASE__))											=			SARIngram(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			sum([delascore, delascore, delascore, delascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			sum([addascore, addascore, addascore, addascore]	) / 4
    SCREAMING_SNAKE_CASE__											=			(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ = True ,							lowerCAmelCase_ = "13a" ,							lowerCAmelCase_ = True	)				->						Optional[Any]:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        SCREAMING_SNAKE_CASE__											=			sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            SCREAMING_SNAKE_CASE__											=			sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_	)()(lowerCAmelCase_	)
        else:
            SCREAMING_SNAKE_CASE__											=			sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_	)
    elif tokenizer == "moses":
        SCREAMING_SNAKE_CASE__											=			sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_ ,							return_str=lowerCAmelCase_ ,							escape=lowerCAmelCase_	)
    elif tokenizer == "penn":
        SCREAMING_SNAKE_CASE__											=			sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_ ,							return_str=lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			sentence

    if not return_str:
        SCREAMING_SNAKE_CASE__											=			normalized_sent.split()

    return normalized_sent


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[Any]:
    if not (len(lowerCAmelCase_	) == len(lowerCAmelCase_	) == len(lowerCAmelCase_	)):
        raise ValueError('''Sources length must match predictions and references lengths.'''	)
    SCREAMING_SNAKE_CASE__											=			0
    for src, pred, refs in zip(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	):
        sari_score += SARIsent(normalize(lowerCAmelCase_	) ,							normalize(lowerCAmelCase_	) ,							[normalize(lowerCAmelCase_	) for sent in refs]	)
    SCREAMING_SNAKE_CASE__											=			sari_score / len(lowerCAmelCase_	)
    return 1_0_0 * sari_score


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_="exp" ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							)				->						Any:
    SCREAMING_SNAKE_CASE__											=			len(references[0]	)
    if any(len(lowerCAmelCase_	) != references_per_prediction for refs in references	):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
    SCREAMING_SNAKE_CASE__											=			[[refs[i] for refs in references] for i in range(lowerCAmelCase_	)]
    SCREAMING_SNAKE_CASE__											=			sacrebleu.corpus_bleu(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							smooth_method=lowerCAmelCase_ ,							smooth_value=lowerCAmelCase_ ,							force=lowerCAmelCase_ ,							lowercase=lowerCAmelCase_ ,							use_effective_order=lowerCAmelCase_ ,							)
    return output.score



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


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        return datasets.MetricInfo(
            description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''   ,				id='''sequence'''     ),
                    '''references''': datasets.Sequence(datasets.Value('''string'''   ,				id='''sequence'''     )   ,				id='''references'''     ),
                }     )   ,				codebase_urls=[
                '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                '''https://github.com/mjpost/sacreBLEU''',
            ]   ,				reference_urls=[
                '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                '''https://github.com/mjpost/sacreBLEU''',
                '''https://en.wikipedia.org/wiki/BLEU''',
                '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
            ]   ,				)




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{}
        result.update({'''sari''': compute_sari(sources=A_   ,				predictions=A_   ,				references=A_     )}     )
        result.update({'''sacrebleu''': compute_sacrebleu(predictions=A_   ,				references=A_     )}     )
        result.update({'''exact''': compute_em(predictions=A_   ,				references=A_     )}     )
        return result







import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
  )



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=3   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=False   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_input_mask:
            SCREAMING_SNAKE_CASE__											=			random_attention_mask([self.batch_size, self.seq_length]     )

        SCREAMING_SNAKE_CASE__											=			None

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

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return FalconConfig(
            vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=A_   ,				initializer_range=self.initializer_range   ,				pad_token_id=1   ,				new_decoder_architecture=A_   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconModel(config=A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			FalconModel(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(config=A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(config=A_     )
        model.to(A_     )
        model.eval()

        # first forward pass
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				use_cache=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE__											=			ids_tensor((self.batch_size, 3)   ,				config.vocab_size     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor((self.batch_size, 3)   ,				vocab_size=2     )

        # append to next input_ids and
        SCREAMING_SNAKE_CASE__											=			torch.cat([input_ids, next_tokens]   ,				dim=-1     )
        SCREAMING_SNAKE_CASE__											=			torch.cat([input_mask, next_mask]   ,				dim=-1     )

        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]
        SCREAMING_SNAKE_CASE__											=			model(
            A_   ,				attention_mask=A_   ,				encoder_hidden_states=A_   ,				encoder_attention_mask=A_   ,				past_key_values=A_   ,				output_hidden_states=A_   ,				)['''hidden_states'''][0]

        # select random slice
        SCREAMING_SNAKE_CASE__											=			ids_tensor((1,)   ,				output_from_past.shape[-1]     ).item()
        SCREAMING_SNAKE_CASE__											=			output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_SNAKE_CASE__											=			output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(A_   ,				A_   ,				atol=1E-3     )     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

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





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	(
        (
            FalconModel,
            FalconForCausalLM,
            FalconForSequenceClassification,
            FalconForTokenClassification,
            FalconForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase__    :		Dict				=	(FalconForCausalLM,) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(
        {
            """feature-extraction""": FalconModel,
            """text-classification""": FalconForSequenceClassification,
            """text-generation""": FalconForCausalLM,
            """question-answering""": FalconForQuestionAnswering,
            """token-classification""": FalconForTokenClassification,
            """zero-shot""": FalconForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	False
    lowerCamelCase__    :		Tuple				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			FalconModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				hidden_size=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		*SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        for alibi in [True, False]:
            SCREAMING_SNAKE_CASE__											=			alibi
            self.model_tester.create_and_check_model(A_   ,				*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			'''single_label_classification'''
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.model_tester.batch_size]   ,				self.model_tester.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				use_cache=A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.shape[0]
        SCREAMING_SNAKE_CASE__											=			model._convert_to_rw_cache(result.past_key_values     )
        SCREAMING_SNAKE_CASE__											=			model._convert_cache_to_standard_format(A_   ,				A_     )
        for layer in range(len(A_     )     ):
            for tensor_idx in range(2     ):
                self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3     )
                self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4     )
                self.assertTrue(
                    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			'''multi_label_classification'''
        SCREAMING_SNAKE_CASE__											=			input_dict['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			input_ids.ne(1     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			ids_tensor(
            [self.model_tester.batch_size, config.num_labels]   ,				self.model_tester.type_sequence_label_size     ).to(torch.float     )
        SCREAMING_SNAKE_CASE__											=			FalconForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_   ,				labels=A_     )
        self.assertEqual(result.logits.shape   ,				(self.model_tester.batch_size, self.model_tester.num_labels)     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_class in self.all_generative_model_classes:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

            # If it doesn't support cache, pass the test
            if not hasattr(A_   ,				'''use_cache'''     ):
                return

            SCREAMING_SNAKE_CASE__											=			model_class(A_     ).to(A_     )
            if "use_cache" not in inputs:
                SCREAMING_SNAKE_CASE__											=			True
            SCREAMING_SNAKE_CASE__											=			model(**A_     )

            # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
            if "past_key_values" not in outputs:
                return

            SCREAMING_SNAKE_CASE__											=			(
                getattr(A_   ,				'''decoder_layers'''   ,				A_     )
                or getattr(A_   ,				'''num_decoder_layers'''   ,				A_     )
                or config.num_hidden_layers
            )
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				'''num_kv_heads'''   ,				config.num_attention_heads     )
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				'''d_model'''   ,				config.hidden_size     )
            SCREAMING_SNAKE_CASE__											=			embed_dim // num_attention_heads

            SCREAMING_SNAKE_CASE__											=			outputs['''past_key_values''']
            self.assertEqual(len(A_     )   ,				A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			inputs['''input_ids'''].shape
            for i in range(A_     ):
                if config.new_decoder_architecture:
                    SCREAMING_SNAKE_CASE__											=			config.num_attention_heads
                elif config.multi_query:
                    SCREAMING_SNAKE_CASE__											=			1
                self.assertEqual(len(past_kv[0]     )   ,				2     )  # K V for the decoder = 2
                self.assertEqual(
                    past_kv[i][0].shape   ,				(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )
                self.assertEqual(
                    past_kv[i][1].shape   ,				(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b'''     )
        SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b'''     )
        model.eval()
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

        SCREAMING_SNAKE_CASE__											=			(
            '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
        )

        SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=19     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(A_     )[0]

        self.assertEqual(A_   ,				A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
            SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(A_     )
            SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained(A_     )
            model.eval()
            model.to(A_     )
            SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

            # We just test that these run without errors - the models are randomly initialized
            # and so the actual text outputs will be garbage
            model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=4     )
            model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=4     )
            model.generate(**A_   ,				num_beams=2   ,				max_new_tokens=4     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        with torch.no_grad():
            for repo in [
                "Rocketknight1/falcon-rw-1b",
                "Rocketknight1/tiny-random-falcon-7b",
                "Rocketknight1/tiny-random-falcon-40b",
            ]:
                SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(A_     )
                SCREAMING_SNAKE_CASE__											=			FalconForCausalLM.from_pretrained(A_     )
                model.eval()
                model.to(device=A_     )
                SCREAMING_SNAKE_CASE__											=			tokenizer('''My favorite food is'''   ,				return_tensors='''pt'''     ).to(A_     )

                # Test results are the same with and without cache
                SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=20   ,				use_cache=A_     )
                SCREAMING_SNAKE_CASE__											=			model.generate(**A_   ,				do_sample=A_   ,				max_new_tokens=20   ,				use_cache=A_     )
                self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0     )
