from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_snake_case			=						'''\
@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},
}
'''

_snake_case			=						'''\
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
'''


_snake_case			=						'''
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   (			snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   List[str]:
							__UpperCAmelCase						:			List[Any]         =   [rgram for rgrams in rgramslist for rgram in rgrams]
							__UpperCAmelCase						:			Optional[int]         =   Counter(snake_case__							)

							__UpperCAmelCase						:			Any         =   Counter(snake_case__							)
							__UpperCAmelCase						:			Tuple         =   Counter()
							for sgram, scount in sgramcounter.items():
														__UpperCAmelCase						:			List[str]         =   scount * numref

							__UpperCAmelCase						:			Union[str, Any]         =   Counter(snake_case__							)
							__UpperCAmelCase						:			Dict         =   Counter()
							for cgram, ccount in cgramcounter.items():
														__UpperCAmelCase						:			Union[str, Any]         =   ccount * numref

							# KEEP
							__UpperCAmelCase						:			Optional[int]         =   sgramcounter_rep & cgramcounter_rep
							__UpperCAmelCase						:			List[str]         =   keepgramcounter_rep & rgramcounter
							__UpperCAmelCase						:			List[str]         =   sgramcounter_rep & rgramcounter

							__UpperCAmelCase						:			int         =   0
							__UpperCAmelCase						:			Union[str, Any]         =   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.
							__UpperCAmelCase						:			Dict         =   1
							__UpperCAmelCase						:			List[Any]         =   1
							if len(snake_case__							) > 0:
														__UpperCAmelCase						:			Dict         =   keeptmpscorea / len(snake_case__							)
							if len(snake_case__							) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														__UpperCAmelCase						:			int         =   keeptmpscorea / sum(keepgramcounterall_rep.values()							)
							__UpperCAmelCase						:			str         =   0
							if keepscore_precision > 0 or keepscore_recall > 0:
														__UpperCAmelCase						:			Optional[Any]         =   2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							__UpperCAmelCase						:			List[str]         =   sgramcounter_rep - cgramcounter_rep
							__UpperCAmelCase						:			str         =   delgramcounter_rep - rgramcounter
							__UpperCAmelCase						:			Union[str, Any]         =   sgramcounter_rep - rgramcounter
							__UpperCAmelCase						:			Any         =   0
							__UpperCAmelCase						:			int         =   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.
							__UpperCAmelCase						:			List[str]         =   1
							if len(snake_case__							) > 0:
														__UpperCAmelCase						:			Union[str, Any]         =   deltmpscorea / len(snake_case__							)

							# ADDITION
							__UpperCAmelCase						:			int         =   set(snake_case__							) - set(snake_case__							)
							__UpperCAmelCase						:			Optional[Any]         =   set(snake_case__							) & set(snake_case__							)
							__UpperCAmelCase						:			Any         =   set(snake_case__							) - set(snake_case__							)

							__UpperCAmelCase						:			List[Any]         =   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.
							__UpperCAmelCase						:			List[Any]         =   1
							__UpperCAmelCase						:			List[str]         =   1
							if len(snake_case__							) > 0:
														__UpperCAmelCase						:			str         =   addtmpscore / len(snake_case__							)
							if len(snake_case__							) > 0:
														__UpperCAmelCase						:			List[str]         =   addtmpscore / len(snake_case__							)
							__UpperCAmelCase						:			int         =   0
							if addscore_precision > 0 or addscore_recall > 0:
														__UpperCAmelCase						:			Optional[int]         =   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   int:
							__UpperCAmelCase						:			List[Any]         =   len(snake_case__							)

							__UpperCAmelCase						:			Union[str, Any]         =   ssent.split(" "							)
							__UpperCAmelCase						:			Tuple         =   csent.split(" "							)
							__UpperCAmelCase						:			Any         =   []
							__UpperCAmelCase						:			List[Any]         =   []
							__UpperCAmelCase						:			Optional[int]         =   []
							__UpperCAmelCase						:			Optional[Any]         =   []
							__UpperCAmelCase						:			int         =   []
							__UpperCAmelCase						:			Any         =   []

							__UpperCAmelCase						:			Optional[Any]         =   []
							__UpperCAmelCase						:			Optional[Any]         =   []
							__UpperCAmelCase						:			str         =   []
							__UpperCAmelCase						:			Any         =   []
							for rsent in rsents:
														__UpperCAmelCase						:			Dict         =   rsent.split(" "							)
														__UpperCAmelCase						:			Any         =   []
														__UpperCAmelCase						:			str         =   []
														__UpperCAmelCase						:			Any         =   []
														ragramslist.append(snake_case__							)
														for i in range(0,    len(snake_case__							) - 1							):
																					if i < len(snake_case__							) - 1:
																												__UpperCAmelCase						:			Optional[Any]         =   ragrams[i] + " " + ragrams[i + 1]
																												ragrams.append(snake_case__							)
																					if i < len(snake_case__							) - 2:
																												__UpperCAmelCase						:			Any         =   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																												ragrams.append(snake_case__							)
																					if i < len(snake_case__							) - 3:
																												__UpperCAmelCase						:			Any         =   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																												ragrams.append(snake_case__							)
														ragramslist.append(snake_case__							)
														ragramslist.append(snake_case__							)
														ragramslist.append(snake_case__							)

							for i in range(0,    len(snake_case__							) - 1							):
														if i < len(snake_case__							) - 1:
																					__UpperCAmelCase						:			str         =   sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(snake_case__							)
														if i < len(snake_case__							) - 2:
																					__UpperCAmelCase						:			Optional[int]         =   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(snake_case__							)
														if i < len(snake_case__							) - 3:
																					__UpperCAmelCase						:			Any         =   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																					sagrams.append(snake_case__							)

							for i in range(0,    len(snake_case__							) - 1							):
														if i < len(snake_case__							) - 1:
																					__UpperCAmelCase						:			Dict         =   cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(snake_case__							)
														if i < len(snake_case__							) - 2:
																					__UpperCAmelCase						:			Dict         =   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(snake_case__							)
														if i < len(snake_case__							) - 3:
																					__UpperCAmelCase						:			Optional[Any]         =   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(snake_case__							)

							((__UpperCAmelCase)    ,	(__UpperCAmelCase)    ,	(__UpperCAmelCase))						:			Tuple         =   SARIngram(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
							((__UpperCAmelCase)    ,	(__UpperCAmelCase)    ,	(__UpperCAmelCase))						:			Optional[int]         =   SARIngram(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
							((__UpperCAmelCase)    ,	(__UpperCAmelCase)    ,	(__UpperCAmelCase))						:			Optional[Any]         =   SARIngram(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
							((__UpperCAmelCase)    ,	(__UpperCAmelCase)    ,	(__UpperCAmelCase))						:			Dict         =   SARIngram(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[str]         =   sum([keepascore, keepascore, keepascore, keepascore]							) / 4
							__UpperCAmelCase						:			Any         =   sum([delascore, delascore, delascore, delascore]							) / 4
							__UpperCAmelCase						:			str         =   sum([addascore, addascore, addascore, addascore]							) / 4
							__UpperCAmelCase						:			Any         =   (avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore




def   _UpperCamelCase   (			snake_case__,    snake_case__ = True,    snake_case__ = "13a",    snake_case__ = True							)						->   Union[str, 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:
														__UpperCAmelCase						:			Any         =   sentence.lower()

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

							if not return_str:
														__UpperCAmelCase						:			List[str]         =   normalized_sent.split()

							return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,      _KWARGS_DESCRIPTION				)
class 					_snake_case						(						datasets.Metric				):
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												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    _lowerCamelCase (  self:				int	,  __lowerCamelCase:				int	,  __lowerCamelCase:				List[str]	,  __lowerCamelCase:				int     )    ->				List[str]:
												if not (len(__lowerCamelCase     ) == len(__lowerCamelCase     ) == len(__lowerCamelCase     )):
																			raise ValueError("Sources length must match predictions and references lengths."     )
												__UpperCAmelCase						:			int         =   0
												for src, pred, refs in zip(__lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase     ):
																			sari_score += SARIsent(normalize(__lowerCamelCase     )	,  normalize(__lowerCamelCase     )	,  [normalize(__lowerCamelCase     ) for sent in refs]     )
												__UpperCAmelCase						:			List[str]         =   sari_score / len(__lowerCamelCase     )
												return {"sari": 1_00 * sari_score}





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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 SwinaSRForImageSuperResolution, SwinaSRModel
	from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import SwinaSRImageProcessor




class 					_snake_case						:
					def __init__(  self:				Tuple	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				Union[str, Any]=13	,  __lowerCamelCase:				str=32	,  __lowerCamelCase:				Dict=1	,  __lowerCamelCase:				List[Any]=3	,  __lowerCamelCase:				List[Any]=16	,  __lowerCamelCase:				Any=[1, 2, 1]	,  __lowerCamelCase:				List[str]=[2, 2, 4]	,  __lowerCamelCase:				Tuple=2	,  __lowerCamelCase:				Tuple=2.0	,  __lowerCamelCase:				Optional[Any]=True	,  __lowerCamelCase:				List[str]=0.0	,  __lowerCamelCase:				str=0.0	,  __lowerCamelCase:				List[str]=0.1	,  __lowerCamelCase:				Optional[Any]="gelu"	,  __lowerCamelCase:				List[str]=False	,  __lowerCamelCase:				int=True	,  __lowerCamelCase:				List[str]=0.02	,  __lowerCamelCase:				Dict=1e-5	,  __lowerCamelCase:				Dict=True	,  __lowerCamelCase:				List[Any]=None	,  __lowerCamelCase:				Tuple=False	,  __lowerCamelCase:				Optional[Any]=2	,  )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Any         =   parent
												__UpperCAmelCase						:			Optional[int]         =   batch_size
												__UpperCAmelCase						:			Union[str, Any]         =   image_size
												__UpperCAmelCase						:			int         =   patch_size
												__UpperCAmelCase						:			str         =   num_channels
												__UpperCAmelCase						:			Any         =   embed_dim
												__UpperCAmelCase						:			str         =   depths
												__UpperCAmelCase						:			Optional[int]         =   num_heads
												__UpperCAmelCase						:			Any         =   window_size
												__UpperCAmelCase						:			Tuple         =   mlp_ratio
												__UpperCAmelCase						:			List[str]         =   qkv_bias
												__UpperCAmelCase						:			int         =   hidden_dropout_prob
												__UpperCAmelCase						:			Dict         =   attention_probs_dropout_prob
												__UpperCAmelCase						:			Union[str, Any]         =   drop_path_rate
												__UpperCAmelCase						:			Union[str, Any]         =   hidden_act
												__UpperCAmelCase						:			Optional[int]         =   use_absolute_embeddings
												__UpperCAmelCase						:			Any         =   patch_norm
												__UpperCAmelCase						:			str         =   layer_norm_eps
												__UpperCAmelCase						:			Any         =   initializer_range
												__UpperCAmelCase						:			List[str]         =   is_training
												__UpperCAmelCase						:			Union[str, Any]         =   scope
												__UpperCAmelCase						:			Optional[int]         =   use_labels
												__UpperCAmelCase						:			int         =   upscale

												# here we set some attributes to make tests pass
												__UpperCAmelCase						:			Union[str, Any]         =   len(__lowerCamelCase     )
												__UpperCAmelCase						:			Optional[Any]         =   embed_dim
												__UpperCAmelCase						:			List[Any]         =   (image_size // patch_size) ** 2
					def    _lowerCamelCase (  self:				List[Any]     )    ->				List[Any]:
												__UpperCAmelCase						:			Any         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

												__UpperCAmelCase						:			Union[str, Any]         =   None
												if self.use_labels:
																			__UpperCAmelCase						:			Any         =   ids_tensor([self.batch_size]	,  self.type_sequence_label_size     )

												__UpperCAmelCase						:			Optional[Any]         =   self.get_config()

												return config, pixel_values, labels
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Tuple:
												return SwinaSRConfig(
												    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	,  window_size=self.window_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	,  use_absolute_embeddings=self.use_absolute_embeddings	,  path_norm=self.patch_norm	,  layer_norm_eps=self.layer_norm_eps	,  initializer_range=self.initializer_range	,  upscale=self.upscale	,  )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				List[Any]	,  __lowerCamelCase:				Any	,  __lowerCamelCase:				List[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			str         =   SwinaSRModel(config=__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												self.parent.assertEqual(
												    result.last_hidden_state.shape	,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)     )
					def    _lowerCamelCase (  self:				Union[str, Any]	,  __lowerCamelCase:				Optional[int]	,  __lowerCamelCase:				Tuple	,  __lowerCamelCase:				int     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Dict         =   SwinaSRForImageSuperResolution(__lowerCamelCase     )
												model.to(__lowerCamelCase     )
												model.eval()
												__UpperCAmelCase						:			List[str]         =   model(__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   self.image_size * self.upscale
												self.parent.assertEqual(
												    result.reconstruction.shape	,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)     )





					def    _lowerCamelCase (  self:				Any     )    ->				Dict:
												__UpperCAmelCase						:			List[Any]         =   self.prepare_config_and_inputs()
												__UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   config_and_inputs
												__UpperCAmelCase						:			Dict         =   {"pixel_values": pixel_values}
												return config, inputs_dict




@require_torch
class 					_snake_case						(						_lowercase					,      _lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		List[str]  =     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
					lowerCamelCase__:		List[Any]  =     {"feature-extraction": SwinaSRModel} if is_torch_available() else {}

					lowerCamelCase__:		List[Any]  =     False
					lowerCamelCase__:		Any  =     False
					lowerCamelCase__:		int  =     False
					lowerCamelCase__:		Optional[Any]  =     False
					lowerCamelCase__:		str  =     False
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModelTester(self     )
												__UpperCAmelCase						:			Optional[int]         =   ConfigTester(self	,  config_class=__lowerCamelCase	,  embed_dim=37     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[Any]:
												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    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Optional[Any]:
												__UpperCAmelCase						:			Optional[int]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_super_resolution(*__lowerCamelCase     )
					@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
					def    _lowerCamelCase (  self:				str     )    ->				List[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not use inputs_embeds"     )
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				int     )    ->				Dict:
												pass
					@unittest.skip(reason="Swin2SR does not support training yet"     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				Optional[Any]:
												pass
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Union[str, Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)     )
																			__UpperCAmelCase						:			Union[str, Any]         =   model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__lowerCamelCase	,  nn.Linear     )     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			__UpperCAmelCase						:			Dict         =   inspect.signature(model.forward     )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCAmelCase						:			int         =   [*signature.parameters.keys()]

																			__UpperCAmelCase						:			int         =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,  __lowerCamelCase     )
					@slow
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase						:			Union[str, Any]         =   SwinaSRModel.from_pretrained(__lowerCamelCase     )
																			self.assertIsNotNone(__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				Optional[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   self.model_tester.prepare_config_and_inputs_for_common()

												__UpperCAmelCase						:			Union[str, Any]         =   _config_zero_init(__lowerCamelCase     )
												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Tuple         =   model_class(config=__lowerCamelCase     )
																			for name, param in model.named_parameters():
																										if "logit_scale" in name:
																																	continue
																										if 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'''	,  )





					def    _lowerCamelCase (  self:				Optional[int]     )    ->				List[Any]:
												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.model_tester.prepare_config_and_inputs_for_common()
												__UpperCAmelCase						:			List[Any]         =   True

												for model_class in self.all_model_classes:
																			__UpperCAmelCase						:			Optional[int]         =   True
																			__UpperCAmelCase						:			str         =   False
																			__UpperCAmelCase						:			int         =   True
																			__UpperCAmelCase						:			Dict         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			Any         =   outputs.attentions
																			__UpperCAmelCase						:			Union[str, Any]         =   len(self.model_tester.depths     )
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			Any         =   config.window_size**2
																			__UpperCAmelCase						:			List[str]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			int         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )
																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions
																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )
																			__UpperCAmelCase						:			List[str]         =   len(__lowerCamelCase     )

																			# Check attention is always last and order is fine
																			__UpperCAmelCase						:			Tuple         =   True
																			__UpperCAmelCase						:			List[str]         =   True
																			__UpperCAmelCase						:			List[Any]         =   model_class(__lowerCamelCase     )
																			model.to(__lowerCamelCase     )
																			model.eval()
																			with torch.no_grad():
																										__UpperCAmelCase						:			Optional[int]         =   model(**self._prepare_for_class(__lowerCamelCase	,  __lowerCamelCase     )     )

																			self.assertEqual(out_len + 1	,  len(__lowerCamelCase     )     )

																			__UpperCAmelCase						:			List[Any]         =   outputs.attentions

																			self.assertEqual(len(__lowerCamelCase     )	,  __lowerCamelCase     )

																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]     )	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )




@require_vision
@require_torch
@slow
class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   SwinaSRImageProcessor()
												__UpperCAmelCase						:			int         =   SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"     ).to(__lowerCamelCase     )

												__UpperCAmelCase						:			Union[str, Any]         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
												__UpperCAmelCase						:			Dict         =   processor(images=__lowerCamelCase	,  return_tensors="pt"     ).to(__lowerCamelCase     )

												# forward pass
												with torch.no_grad():
																			__UpperCAmelCase						:			int         =   model(**__lowerCamelCase     )

												# verify the logits
												__UpperCAmelCase						:			str         =   torch.Size([1, 3, 9_76, 12_96]     )
												self.assertEqual(outputs.reconstruction.shape	,  __lowerCamelCase     )
												__UpperCAmelCase						:			Union[str, Any]         =   torch.tensor(
												    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]     ).to(__lowerCamelCase     )
												self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,  __lowerCamelCase	,  atol=1e-4     )     )




