



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_		:  str										=  '''\
@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},
}
'''

A_		:  Optional[int]										=  '''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


A_		:  int										=  '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:str			,       UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		[rgram for rgrams in rgramslist for rgram in rgrams]
 a							=		Counter(UpperCAmelCase__       )

 a							=		Counter(UpperCAmelCase__       )
 a							=		Counter()
 for sgram, scount in sgramcounter.items():
  a							=		scount * numref

 a							=		Counter(UpperCAmelCase__       )
 a							=		Counter()
 for cgram, ccount in cgramcounter.items():
  a							=		ccount * numref

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

 a							=		0
 a							=		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.
 a							=		1
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		keeptmpscorea / len(UpperCAmelCase__       )
 if len(UpperCAmelCase__       ) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  a							=		keeptmpscorea / sum(keepgramcounterall_rep.values()       )
 a							=		0
 if keepscore_precision > 0 or keepscore_recall > 0:
  a							=		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 a							=		sgramcounter_rep - cgramcounter_rep
 a							=		delgramcounter_rep - rgramcounter
 a							=		sgramcounter_rep - rgramcounter
 a							=		0
 a							=		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.
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		deltmpscorea / len(UpperCAmelCase__       )

 # ADDITION
 a							=		set(UpperCAmelCase__       ) - set(UpperCAmelCase__       )
 a							=		set(UpperCAmelCase__       ) & set(UpperCAmelCase__       )
 a							=		set(UpperCAmelCase__       ) - set(UpperCAmelCase__       )

 a							=		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.
 a							=		1
 a							=		1
 if len(UpperCAmelCase__       ) > 0:
  a							=		addtmpscore / len(UpperCAmelCase__       )
 if len(UpperCAmelCase__       ) > 0:
  a							=		addtmpscore / len(UpperCAmelCase__       )
 a							=		0
 if addscore_precision > 0 or addscore_recall > 0:
  a							=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		len(UpperCAmelCase__       )

 a							=		ssent.split(" "       )
 a							=		csent.split(" "       )
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]
 a							=		[]

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

 for i in range(0			,       len(UpperCAmelCase__       ) - 1       ):
  if i < len(UpperCAmelCase__       ) - 1:
   a							=		sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 2:
   a							=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 3:
   a							=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
   sagrams.append(UpperCAmelCase__       )

 for i in range(0			,       len(UpperCAmelCase__       ) - 1       ):
  if i < len(UpperCAmelCase__       ) - 1:
   a							=		cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 2:
   a							=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(UpperCAmelCase__       )
  if i < len(UpperCAmelCase__       ) - 3:
   a							=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(UpperCAmelCase__       )

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


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:bool = True			,       UpperCAmelCase__			:str = "13a"			,       UpperCAmelCase__			:bool = True       ):






 '''simple docstring'''

 if lowercase:
  a							=		sentence.lower()

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

 if not return_str:
  a							=		normalized_sent.split()

 return normalized_sent




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

   def 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    return datasets.MetricInfo(
        description=_DESCRIPTION				, citation=_CITATION				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
            {
                "sources": datasets.Value("string"				, id="sequence"				),
                "predictions": datasets.Value("string"				, id="sequence"				),
                "references": datasets.Sequence(datasets.Value("string"				, id="sequence"				)				, id="references"				),
            }				)				, codebase_urls=[
            "https://github.com/cocoxu/simplification/blob/master/SARI.py",
            "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
        ]				, reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]				, )




   def 		A				(   self      :							Any				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							List[Any]				)							->					Any:


    """simple docstring"""






    if not (len(__lowerCAmelCase				) == len(__lowerCAmelCase				) == len(__lowerCAmelCase				)):
     raise ValueError("Sources length must match predictions and references lengths."				)
    a							=		0
    for src, pred, refs in zip(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				):
     sari_score += SARIsent(normalize(__lowerCAmelCase				)				, normalize(__lowerCAmelCase				)				, [normalize(__lowerCAmelCase				) for sent in refs]				)
    a							=		sari_score / len(__lowerCAmelCase				)
    return {"sari": 100 * sari_score}


"""simple docstring"""

import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import NatBackbone, NatForImageClassification, NatModel
				from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Tuple     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    Optional[Any]=13     , UpperCamelCase__							:    Optional[Any]=64     , UpperCamelCase__							:    str=4     , UpperCamelCase__							:    List[str]=3     , UpperCamelCase__							:    int=16     , UpperCamelCase__							:    str=[1, 2, 1]     , UpperCamelCase__							:    List[Any]=[2, 4, 8]     , UpperCamelCase__							:    Optional[Any]=3     , UpperCamelCase__							:    List[Any]=2.0     , UpperCamelCase__							:    int=True     , UpperCamelCase__							:    List[str]=0.0     , UpperCamelCase__							:    Optional[Any]=0.0     , UpperCamelCase__							:    Optional[Any]=0.1     , UpperCamelCase__							:    Tuple="gelu"     , UpperCamelCase__							:    Tuple=True     , UpperCamelCase__							:    Any=0.02     , UpperCamelCase__							:    str=1E-5     , UpperCamelCase__							:    Dict=True     , UpperCamelCase__							:    Optional[int]=None     , UpperCamelCase__							:    Any=True     , UpperCamelCase__							:    Tuple=10     , UpperCamelCase__							:    List[Any]=["stage1", "stage2"]     , UpperCamelCase__							:    Optional[int]=[1, 2]     , )				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=parent
													__UpperCamelCase							=batch_size
													__UpperCamelCase							=image_size
													__UpperCamelCase							=patch_size
													__UpperCamelCase							=num_channels
													__UpperCamelCase							=embed_dim
													__UpperCamelCase							=depths
													__UpperCamelCase							=num_heads
													__UpperCamelCase							=kernel_size
													__UpperCamelCase							=mlp_ratio
													__UpperCamelCase							=qkv_bias
													__UpperCamelCase							=hidden_dropout_prob
													__UpperCamelCase							=attention_probs_dropout_prob
													__UpperCamelCase							=drop_path_rate
													__UpperCamelCase							=hidden_act
													__UpperCamelCase							=patch_norm
													__UpperCamelCase							=layer_norm_eps
													__UpperCamelCase							=initializer_range
													__UpperCamelCase							=is_training
													__UpperCamelCase							=scope
													__UpperCamelCase							=use_labels
													__UpperCamelCase							=num_labels
													__UpperCamelCase							=out_features
													__UpperCamelCase							=out_indices





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

													__UpperCamelCase							=self.get_config()

													return config, pixel_values, labels





						def   UpperCAmelCase_ (				self							:    int				)				->				Union[str, Any]:
													'''simple docstring'''



													return NatConfig(
													    num_labels=self.num_labels     , image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , embed_dim=self.embed_dim     , depths=self.depths     , num_heads=self.num_heads     , kernel_size=self.kernel_size     , mlp_ratio=self.mlp_ratio     , qkv_bias=self.qkv_bias     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , drop_path_rate=self.drop_path_rate     , hidden_act=self.hidden_act     , patch_norm=self.patch_norm     , layer_norm_eps=self.layer_norm_eps     , initializer_range=self.initializer_range     , out_features=self.out_features     , out_indices=self.out_indices     , )





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=NatModel(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													__UpperCamelCase							=__UpperCamelCase							=(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
													__UpperCamelCase							=int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

													self.parent.assertEqual(
													    result.last_hidden_state.shape     , (self.batch_size, expected_height, expected_width, expected_dim)				)





						def   UpperCAmelCase_ (				self							:    Dict     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    Any				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__     , labels=UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)

													# test greyscale images
													__UpperCamelCase							=1
													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													__UpperCamelCase							=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													__UpperCamelCase							=model(UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Dict     , UpperCamelCase__							:    str				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify hidden states
													self.parent.assertEqual(len(result.feature_maps				)     , len(config.out_features				)				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[0], 16, 16]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , len(config.out_features				)				)

													# verify backbone works with out_features=None
													__UpperCamelCase							=None
													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify feature maps
													self.parent.assertEqual(len(result.feature_maps				)     , 1				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[-1], 4, 4]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , 1				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.prepare_config_and_inputs()
													__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=config_and_inputs
													__UpperCamelCase							={'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_natten
@require_torch
class 		_lowercase (						__a					,					__a					,					unittest.TestCase      ):



						"""simple docstring"""




						lowercase__       =  (
						    (
						        NatModel,
						        NatForImageClassification,
						        NatBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						lowercase__       =  (
						    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
						    if is_torch_available()
						    else {}
						)
						lowercase__       =  False

						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)
													__UpperCamelCase							=ConfigTester(self     , config_class=UpperCamelCase__     , embed_dim=37				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				str:
													'''simple docstring'''



													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Optional[int]:
													'''simple docstring'''



													return





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Optional[int]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*UpperCamelCase__				)





						@unittest.skip(reason='''Nat does not use inputs_embeds'''				)
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



													pass





						@unittest.skip(reason='''Nat does not use feedforward chunking'''				)
						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													pass





						def   UpperCAmelCase_ (				self							:    Any				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)				)
																				__UpperCamelCase							=model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(UpperCamelCase__     , nn.Linear				)				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				__UpperCamelCase							=inspect.signature(model.forward				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__UpperCamelCase							=[*signature.parameters.keys()]

																				__UpperCamelCase							=['''pixel_values''']
																				self.assertListEqual(arg_names[:1]     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''				)





						def   UpperCAmelCase_ (				self							:    Tuple     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    List[str]     , UpperCamelCase__							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=model_class(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													with torch.no_grad():
																				__UpperCamelCase							=model(**self._prepare_for_class(UpperCamelCase__     , UpperCamelCase__				)				)

													__UpperCamelCase							=outputs.hidden_states

													__UpperCamelCase							=getattr(
													    self.model_tester     , '''expected_num_hidden_layers'''     , len(self.model_tester.depths				) + 1				)
													self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

													# Nat has a different seq_length
													__UpperCamelCase							=(
													    config.patch_size
													    if isinstance(config.patch_size     , collections.abc.Iterable				)
													    else (config.patch_size, config.patch_size)
													)

													__UpperCamelCase							=image_size[0] // patch_size[0]
													__UpperCamelCase							=image_size[1] // patch_size[1]

													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )

													if model_class.__name__ != "NatBackbone":
																				__UpperCamelCase							=outputs.reshaped_hidden_states
																				self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

																				__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=reshaped_hidden_states[0].shape
																				__UpperCamelCase							=(
																				    reshaped_hidden_states[0].view(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				).permute(0     , 2     , 3     , 1				)
																				)
																				self.assertListEqual(
																				    list(reshaped_hidden_states.shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=(
													    self.model_tester.image_size
													    if isinstance(self.model_tester.image_size     , collections.abc.Iterable				)
													    else (self.model_tester.image_size, self.model_tester.image_size)
													)

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=True
																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__UpperCamelCase							=True

																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)





						@slow
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



													for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__UpperCamelCase							=NatModel.from_pretrained(UpperCamelCase__				)
																				self.assertIsNotNone(UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				int:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=_config_zero_init(UpperCamelCase__				)
													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(config=UpperCamelCase__				)
																				for name, param in model.named_parameters():
																											if "embeddings" not in name and param.requires_grad:
																																		self.assertIn(
																																		    ((param.data.mean() * 1E9).round() / 1E9).item()     , [0.0, 1.0]     , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     , )






@require_natten
@require_vision
@require_torch
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						@cached_property
						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Any:
													'''simple docstring'''



													return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''				) if is_vision_available() else None





						@slow
						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''				).to(UpperCamelCase__				)
													__UpperCamelCase							=self.default_image_processor

													__UpperCamelCase							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													__UpperCamelCase							=image_processor(images=UpperCamelCase__     , return_tensors='''pt'''				).to(UpperCamelCase__				)

													# forward pass
													with torch.no_grad():
																				__UpperCamelCase							=model(**UpperCamelCase__				)

													# verify the logits
													__UpperCamelCase							=torch.Size((1, 1000)				)
													self.assertEqual(outputs.logits.shape     , UpperCamelCase__				)
													__UpperCamelCase							=torch.tensor([0.38_05, -0.86_76, -0.39_12]				).to(UpperCamelCase__				)
													self.assertTrue(torch.allclose(outputs.logits[0, :3]     , UpperCamelCase__     , atol=1E-4				)				)






@require_torch
@require_natten
class 		_lowercase (						unittest.TestCase					,					__a      ):



						"""simple docstring"""




						lowercase__       =  (NatBackbone,) if is_torch_available() else ()
						lowercase__       =  NatConfig





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)



