
import sacrebleu as scb
from packaging import version

import datasets


A__       :   Tuple   						=					"""\
@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__       :   Any   						=					"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

A__       :   Optional[int]   						=					"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,		_KWARGS_DESCRIPTION					)
class 						lowercase       (  datasets.Metric					):
					def lowercase_			(		self    ):
								"""simple docstring"""







								if version.parse(scb.__version__    ) < version.parse('''1.4.12'''    ):
											raise ImportWarning(
											    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
											    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''    )
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,	citation=_CITATION ,	homepage='''https://github.com/mjpost/sacreBLEU''' ,	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/mjpost/sacreBLEU'''] ,	reference_urls=[
								        '''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 ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__="exp" ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=False ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			len(references[0]    )
								if any(len(SCREAMING_SNAKE_CASE__    ) != references_per_prediction for refs in references    ):
											raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
								lowerCAmelCase__ :    Optional[Any]				      =			[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__    )]
								lowerCAmelCase__ :    Tuple				      =			scb.corpus_bleu(
								    SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	smooth_method=SCREAMING_SNAKE_CASE__ ,	smooth_value=SCREAMING_SNAKE_CASE__ ,	force=SCREAMING_SNAKE_CASE__ ,	lowercase=SCREAMING_SNAKE_CASE__ ,	use_effective_order=SCREAMING_SNAKE_CASE__ ,	**({'''tokenize''': tokenize} if tokenize else {}) ,	)
								lowerCAmelCase__ :    Optional[int]				      =			{
								    '''score''': output.score,
								    '''counts''': output.counts,
								    '''totals''': output.totals,
								    '''precisions''': output.precisions,
								    '''bp''': output.bp,
								    '''sys_len''': output.sys_len,
								    '''ref_len''': output.ref_len,
								}
								return output_dict





import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 DonutSwinModel
							from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=32 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=3 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=[1, 2, 1] ,	SCREAMING_SNAKE_CASE__=[2, 2, 4] ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=2.0 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.0 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0.02 ,	SCREAMING_SNAKE_CASE__=1E-5 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=10 ,	SCREAMING_SNAKE_CASE__=8 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			parent
								lowerCAmelCase__ :    str				      =			batch_size
								lowerCAmelCase__ :    Tuple				      =			image_size
								lowerCAmelCase__ :    Union[str, Any]				      =			patch_size
								lowerCAmelCase__ :    int				      =			num_channels
								lowerCAmelCase__ :    Tuple				      =			embed_dim
								lowerCAmelCase__ :    Optional[int]				      =			depths
								lowerCAmelCase__ :    Tuple				      =			num_heads
								lowerCAmelCase__ :    Tuple				      =			window_size
								lowerCAmelCase__ :    Dict				      =			mlp_ratio
								lowerCAmelCase__ :    int				      =			qkv_bias
								lowerCAmelCase__ :    Dict				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Dict				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    Tuple				      =			drop_path_rate
								lowerCAmelCase__ :    int				      =			hidden_act
								lowerCAmelCase__ :    Union[str, Any]				      =			use_absolute_embeddings
								lowerCAmelCase__ :    Dict				      =			patch_norm
								lowerCAmelCase__ :    Any				      =			layer_norm_eps
								lowerCAmelCase__ :    Tuple				      =			initializer_range
								lowerCAmelCase__ :    Optional[Any]				      =			is_training
								lowerCAmelCase__ :    str				      =			scope
								lowerCAmelCase__ :    List[str]				      =			use_labels
								lowerCAmelCase__ :    Optional[Any]				      =			type_sequence_label_size
								lowerCAmelCase__ :    int				      =			encoder_stride
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								lowerCAmelCase__ :    Tuple				      =			None
								if self.use_labels:
											lowerCAmelCase__ :    Tuple				      =			ids_tensor([self.batch_size] ,	self.type_sequence_label_size    )

								lowerCAmelCase__ :    Tuple				      =			self.get_config()

								return config, pixel_values, labels
					def lowercase_			(		self    ):
								"""simple docstring"""







								return DonutSwinConfig(
								    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 ,	encoder_stride=self.encoder_stride ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel(config=SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()
								lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
								lowerCAmelCase__ :    int				      =			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

								self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, expected_seq_len, expected_dim)    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				(
								    lowerCAmelCase__
								)      ,				
								) :    Optional[Any]				      =			config_and_inputs
								lowerCAmelCase__ :    Union[str, Any]				      =			{'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class 						lowercase       (  __UpperCamelCase	,		__UpperCamelCase	,		unittest.TestCase					):
					__a					=    (DonutSwinModel,) if is_torch_available() else ()
					__a					=    {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
					__a					=    True

					__a					=    False
					__a					=    False
					__a					=    False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			DonutSwinModelTester(self    )
								lowerCAmelCase__ :    Union[str, Any]				      =			ConfigTester(self ,	config_class=SCREAMING_SNAKE_CASE__ ,	embed_dim=37    )
					def lowercase_			(		self    ):
								"""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 lowercase_			(		self    ):
								"""simple docstring"""







								return
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								pass
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    List[str]				      =			model_class(SCREAMING_SNAKE_CASE__    )
											self.assertIsInstance(model.get_input_embeddings() ,	(nn.Module)    )
											lowerCAmelCase__ :    Dict				      =			model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__ ,	nn.Linear    )    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Tuple				      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Union[str, Any]				      =			model_class(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Optional[Any]				      =			inspect.signature(model.forward    )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase__ :    Tuple				      =			[*signature.parameters.keys()]

											lowerCAmelCase__ :    List[str]				      =			['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    List[Any]				      =			True

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

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

											self.assertListEqual(
											    list(attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
											lowerCAmelCase__ :    Optional[int]				      =			len(SCREAMING_SNAKE_CASE__    )

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

											if hasattr(self.model_tester ,	'''num_hidden_states_types'''    ):
														lowerCAmelCase__ :    Tuple				      =			self.model_tester.num_hidden_states_types
											else:
														# also another +1 for reshaped_hidden_states
														lowerCAmelCase__ :    str				      =			2
											self.assertEqual(out_len + added_hidden_states ,	len(SCREAMING_SNAKE_CASE__    )    )

											lowerCAmelCase__ :    List[str]				      =			outputs.attentions

											self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]    ) ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			model_class(SCREAMING_SNAKE_CASE__    )
								model.to(SCREAMING_SNAKE_CASE__    )
								model.eval()

								with torch.no_grad():
											lowerCAmelCase__ :    List[str]				      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )    )

								lowerCAmelCase__ :    List[str]				      =			outputs.hidden_states

								lowerCAmelCase__ :    Optional[Any]				      =			getattr(
								    self.model_tester ,	'''expected_num_hidden_layers''' ,	len(self.model_tester.depths    ) + 1    )
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								# DonutSwin has a different seq_length
								lowerCAmelCase__ :    int				      =			(
								    config.patch_size
								    if isinstance(config.patch_size ,	collections.abc.Iterable    )
								    else (config.patch_size, config.patch_size)
								)

								lowerCAmelCase__ :    Dict				      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)

								lowerCAmelCase__ :    List[str]				      =			outputs.reshaped_hidden_states
								self.assertEqual(len(SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			reshaped_hidden_states[0].shape
								lowerCAmelCase__ :    Dict				      =			(
								    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	height * width    ).permute(0 ,	2 ,	1    )
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]    ) ,	[num_patches, self.model_tester.embed_dim] ,	)
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Optional[int]				      =			(
								    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:
											lowerCAmelCase__ :    Any				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Union[str, Any]				      =			True

											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__ :    Union[str, Any]				      =			3

								lowerCAmelCase__ :    int				      =			(
								    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)
								)
								lowerCAmelCase__ :    str				      =			(
								    config.patch_size
								    if isinstance(config.patch_size ,	collections.abc.Iterable    )
								    else (config.patch_size, config.patch_size)
								)

								lowerCAmelCase__ :    Optional[int]				      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCAmelCase__ :    Any				      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCAmelCase__ :    Dict				      =			True
											self.check_hidden_states_output(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	(padded_height, padded_width)    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase__ :    Optional[Any]				      =			DonutSwinModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase__ :    Union[str, Any]				      =			_config_zero_init(SCREAMING_SNAKE_CASE__    )
								for model_class in self.all_model_classes:
											lowerCAmelCase__ :    Union[str, Any]				      =			model_class(config=SCREAMING_SNAKE_CASE__    )
											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''' ,	)



