
import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

__snake_case					:							Optional[int]           =							logging.get_logger(__name__)

__snake_case					:							Any           =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

__snake_case					:							List[Any]           =							{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__snake_case					:							Tuple           =							{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__snake_case					:							int           =							{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    (
			    list(range(ord('!'							)     ,						ord('~'							) + 1							)							) + list(range(ord('¡'							)     ,						ord('¬'							) + 1							)							) + list(range(ord('®'							)     ,						ord('ÿ'							) + 1							)							)
			)
			UpperCAmelCase__		:int 		=    bs[:]
			UpperCAmelCase__		:Optional[Any] 		=    0
			for b in range(2**8							):
						if b not in bs:
									bs.append(__SCREAMING_SNAKE_CASE							)
									cs.append(2**8 + n							)
									n += 1
			UpperCAmelCase__		:str 		=    [chr(__SCREAMING_SNAKE_CASE							) for n in cs]
			return dict(zip(__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							)							)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Union[str, Any] 		=    set()
			UpperCAmelCase__		:Tuple 		=    word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)							)
						UpperCAmelCase__		:List[str] 		=    char
			return pairs




class 	UpperCamelCase__       (  snake_case__):

						'''simple docstring'''



						__a     :    int												=			VOCAB_FILES_NAMES
						__a     :    Dict												=			PRETRAINED_VOCAB_FILES_MAP
						__a     :    List[str]												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__a     :    Any												=			["""input_ids""", """attention_mask""", """token_type_ids"""]


						def __init__(  self  , A  , A  , A="replace"  , A="[CLS]"  , A="[SEP]"  , A="[SEP]"  , A="[CLS]"  , A="[UNK]"  , A="[PAD]"  , A="[MASK]"  , A=False  , A=False  , **A  , )  ->Any:
									UpperCAmelCase__		:Union[str, Any] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else bos_token
									UpperCAmelCase__		:Dict 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else eos_token
									UpperCAmelCase__		:List[str] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else sep_token
									UpperCAmelCase__		:List[str] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else cls_token
									UpperCAmelCase__		:List[Any] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else unk_token
									UpperCAmelCase__		:Optional[int] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									UpperCAmelCase__		:Optional[Any] 		=    AddedToken(lowercase_  , lstrip=lowercase_  , rstrip=lowercase_	) if isinstance(lowercase_  , lowercase_	) else mask_token

									super().__init__(
									    errors=lowercase_  , bos_token=lowercase_  , eos_token=lowercase_  , unk_token=lowercase_  , sep_token=lowercase_  , cls_token=lowercase_  , pad_token=lowercase_  , mask_token=lowercase_  , add_prefix_space=lowercase_  , add_bos_token=lowercase_  , **lowercase_  , )
									UpperCAmelCase__		:Optional[int] 		=    add_bos_token

									with open(lowercase_  , encoding='utf-8'	) as vocab_handle:
												UpperCAmelCase__		:str 		=    json.load(lowercase_	)
									UpperCAmelCase__		:Any 		=    {v: k for k, v in self.encoder.items()}
									UpperCAmelCase__		:Dict 		=    errors  # how to handle errors in decoding
									UpperCAmelCase__		:List[str] 		=    bytes_to_unicode()
									UpperCAmelCase__		:Any 		=    {v: k for k, v in self.byte_encoder.items()}
									with open(lowercase_  , encoding='utf-8'	) as merges_handle:
												UpperCAmelCase__		:Tuple 		=    merges_handle.read().split('\n'	)[1:-1]
									UpperCAmelCase__		:Tuple 		=    [tuple(merge.split()	) for merge in bpe_merges]
									UpperCAmelCase__		:Union[str, Any] 		=    dict(zip(lowercase_  , range(len(lowercase_	)	)	)	)
									UpperCAmelCase__		:str 		=    {}
									UpperCAmelCase__		:Dict 		=    add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									UpperCAmelCase__		:str 		=    re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)


						@property
						# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
						def    A__       (  self	)  ->List[Any]:
									return len(self.encoder	)


						def    A__       (  self	)  ->List[str]:
									return dict(self.encoder  , **self.added_tokens_encoder	)


						def    A__       (  self  , A	)  ->List[Any]:
									if token in self.cache:
												return self.cache[token]
									UpperCAmelCase__		:Dict 		=    tuple(lowercase_	)
									UpperCAmelCase__		:Optional[Any] 		=    get_pairs(lowercase_	)

									if not pairs:
												return token

									while True:
												UpperCAmelCase__		:Optional[Any] 		=    min(lowercase_  , key=lambda A	: self.bpe_ranks.get(lowercase_  , float('inf'	)	)	)
												if bigram not in self.bpe_ranks:
															break
												UpperCAmelCase__		:Optional[int] 		=    bigram
												UpperCAmelCase__		:List[Any] 		=    []
												UpperCAmelCase__		:List[str] 		=    0
												while i < len(lowercase_	):
															try:
																		UpperCAmelCase__		:str 		=    word.index(lowercase_  , lowercase_	)
															except ValueError:
																		new_word.extend(word[i:]	)
																		break
															else:
																		new_word.extend(word[i:j]	)
																		UpperCAmelCase__		:int 		=    j

															if word[i] == first and i < len(lowercase_	) - 1 and word[i + 1] == second:
																		new_word.append(first + second	)
																		i += 2
															else:
																		new_word.append(word[i]	)
																		i += 1
												UpperCAmelCase__		:int 		=    tuple(lowercase_	)
												UpperCAmelCase__		:Optional[Any] 		=    new_word
												if len(lowercase_	) == 1:
															break
												else:
															UpperCAmelCase__		:Optional[Any] 		=    get_pairs(lowercase_	)
									UpperCAmelCase__		:List[str] 		=    " ".join(lowercase_	)
									UpperCAmelCase__		:Tuple 		=    word
									return word


						def    A__       (  self  , A  , A = None	)  ->int:
									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									UpperCAmelCase__		:int 		=    [self.cls_token_id]
									UpperCAmelCase__		:Optional[Any] 		=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep


						def    A__       (  self  , A  , A = None  , A = False	)  ->Optional[Any]:
									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowercase_  , token_ids_a=lowercase_  , already_has_special_tokens=lowercase_	)

									if token_ids_a is None:
												return [1] + ([0] * len(lowercase_	)) + [1]
									return [1] + ([0] * len(lowercase_	)) + [1] + ([0] * len(lowercase_	)) + [1]


						def    A__       (  self  , A  , A = None	)  ->Dict:
									UpperCAmelCase__		:Optional[Any] 		=    [self.sep_token_id]
									UpperCAmelCase__		:Optional[Any] 		=    [self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep	) * [0]
									return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]


						def    A__       (  self  , A	)  ->Dict:
									UpperCAmelCase__		:Any 		=    []
									for token in re.findall(self.pat  , lowercase_	):
												UpperCAmelCase__		:List[str] 		=    "".join(
												    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_	).split(' '	)	)
									return bpe_tokens


						def    A__       (  self  , A	)  ->Optional[Any]:
									return self.encoder.get(lowercase_  , self.encoder.get(self.unk_token	)	)


						def    A__       (  self  , A	)  ->Tuple:
									return self.decoder.get(lowercase_	)


						def    A__       (  self  , A	)  ->Any:
									UpperCAmelCase__		:Any 		=    "".join(lowercase_	)
									UpperCAmelCase__		:Tuple 		=    bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'  , errors=self.errors	)
									return text


						def    A__       (  self  , A  , A = None	)  ->Dict:
									if not os.path.isdir(lowercase_	):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
												return
									UpperCAmelCase__		:int 		=    os.path.join(
									    lowercase_  , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
									UpperCAmelCase__		:List[Any] 		=    os.path.join(
									    lowercase_  , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

									with open(lowercase_  , 'w'  , encoding='utf-8'	) as f:
												f.write(json.dumps(self.encoder  , indent=2  , sort_keys=lowercase_  , ensure_ascii=lowercase_	) + '\n'	)

									UpperCAmelCase__		:Union[str, Any] 		=    0
									with open(lowercase_  , 'w'  , encoding='utf-8'	) as writer:
												writer.write('#version: 0.2\n'	)
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  , key=lambda A	: kv[1]	):
															if index != token_index:
																		logger.warning(
																		    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																		    ' Please check that the tokenizer is not corrupted!'	)
																		UpperCAmelCase__		:Optional[Any] 		=    token_index
															writer.write(' '.join(lowercase_	) + '\n'	)
															index += 1

									return vocab_file, merge_file


						def    A__       (  self  , A  , A=False  , **A	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    kwargs.pop('add_prefix_space'  , self.add_prefix_space	)
									if (is_split_into_words or add_prefix_space) and (len(lowercase_	) > 0 and not text[0].isspace()):
												UpperCAmelCase__		:Union[str, Any] 		=    " " + text
									return (text, kwargs)


						def    A__       (  self  , A	)  ->str:
									UpperCAmelCase__		:Any 		=    []
									for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(lowercase_  , add_special_tokens=lowercase_	) + [self.eos_token_id]	)
									if len(lowercase_	) > self.model_max_length:
												UpperCAmelCase__		:List[Any] 		=    input_ids[-self.model_max_length :]
									return input_ids







'''simple docstring'''
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  UpperCAmelCase_					:





 def __init__(     self			,							lowercase_			,							lowercase_=13			,							lowercase_=32			,							lowercase_=2			,							lowercase_=3			,							lowercase_=16			,							lowercase_=[1, 2, 1]			,							lowercase_=[2, 2, 4]			,							lowercase_=2			,							lowercase_=2.0			,							lowercase_=True			,							lowercase_=0.0			,							lowercase_=0.0			,							lowercase_=0.1			,							lowercase_="gelu"			,							lowercase_=False			,							lowercase_=True			,							lowercase_=0.02			,							lowercase_=1E-5			,							lowercase_=True			,							lowercase_=None			,							lowercase_=True			,							lowercase_=10			,							lowercase_=8			,							):
     snake_case_			:     List[Any]        =    parent
     snake_case_			:     str        =    batch_size
     snake_case_			:     str        =    image_size
     snake_case_			:     Any        =    patch_size
     snake_case_			:     Any        =    num_channels
     snake_case_			:     Any        =    embed_dim
     snake_case_			:     Dict        =    depths
     snake_case_			:     Union[str, Any]        =    num_heads
     snake_case_			:     Dict        =    window_size
     snake_case_			:     str        =    mlp_ratio
     snake_case_			:     Optional[int]        =    qkv_bias
     snake_case_			:     Tuple        =    hidden_dropout_prob
     snake_case_			:     str        =    attention_probs_dropout_prob
     snake_case_			:     int        =    drop_path_rate
     snake_case_			:     List[str]        =    hidden_act
     snake_case_			:     int        =    use_absolute_embeddings
     snake_case_			:     Union[str, Any]        =    patch_norm
     snake_case_			:     Any        =    layer_norm_eps
     snake_case_			:     Optional[int]        =    initializer_range
     snake_case_			:     Optional[Any]        =    is_training
     snake_case_			:     Union[str, Any]        =    scope
     snake_case_			:     Dict        =    use_labels
     snake_case_			:     List[Any]        =    type_sequence_label_size
     snake_case_			:     Dict        =    encoder_stride





 def 							snake_case__		(     self):
     snake_case_			:     str        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     snake_case_			:     Any        =    self.get_config()

     return config, pixel_values, labels





 def 							snake_case__		(     self):
     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 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     str        =    DonutSwinModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     Union[str, Any]        =    model(lowercase_)

     snake_case_			:     Tuple        =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
     snake_case_			:     Any        =    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 							snake_case__		(     self):
     snake_case_			:     Tuple        =    self.prepare_config_and_inputs()
     (
         (
         snake_case_
     )      ,			(
         snake_case_
     )      ,			(
         snake_case_
     )      ,			
     )			:     Optional[int]        =    config_and_inputs
     snake_case_			:     Dict        =    {"pixel_values": pixel_values}
     return config, inputs_dict






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

 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    DonutSwinModelTester(self)
     snake_case_			:     Optional[Any]        =    ConfigTester(self			,							config_class=lowercase_			,							embed_dim=37)





 def 							snake_case__		(     self):
     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 							snake_case__		(     self):
     return





 def 							snake_case__		(     self):
     snake_case_			:     int        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def 							snake_case__		(     self):
     # DonutSwin does not use inputs_embeds
     pass





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Tuple        =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_			:     Any        =    model_class(lowercase_)
         self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module))
         snake_case_			:     List[str]        =    model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_			,							nn.Linear))





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Optional[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_			:     Any        =    ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,							lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_			:     Union[str, Any]        =    True

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

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

         self.assertListEqual(
             list(attentions[0].shape[-3:])			,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,							)
         snake_case_			:     Any        =    len(lowercase_)

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

         if hasattr(self.model_tester			,							"num_hidden_states_types"):
             snake_case_			:     Dict        =    self.model_tester.num_hidden_states_types
         else:
             # also another +1 for reshaped_hidden_states
             snake_case_			:     Dict        =    2
         self.assertEqual(out_len + added_hidden_states			,							len(lowercase_))

         snake_case_			:     Any        =    outputs.attentions

         self.assertEqual(len(lowercase_)			,							lowercase_)

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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    model_class(lowercase_)
     model.to(lowercase_)
     model.eval()

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

     snake_case_			:     Dict        =    outputs.hidden_states

     snake_case_			:     Dict        =    getattr(
         self.model_tester			,							"expected_num_hidden_layers"			,							len(self.model_tester.depths) + 1)
     self.assertEqual(len(lowercase_)			,							lowercase_)

     # DonutSwin has a different seq_length
     snake_case_			:     List[Any]        =    (
         config.patch_size
         if isinstance(config.patch_size			,							collections.abc.Iterable)
         else (config.patch_size, config.patch_size)
     )

     snake_case_			:     str        =    (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]			,							)

     snake_case_			:     str        =    outputs.reshaped_hidden_states
     self.assertEqual(len(lowercase_)			,							lowercase_)

     snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_			:     Dict        =    reshaped_hidden_states[0].shape
     snake_case_			:     Union[str, Any]        =    (
         reshaped_hidden_states[0].view(lowercase_			,							lowercase_			,							height * width).permute(0			,							2			,							1)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:])			,							[num_patches, self.model_tester.embed_dim]			,							)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     int        =    self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_			:     Union[str, Any]        =    (
         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:
         snake_case_			:     List[str]        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_			:     List[Any]        =    True

         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     List[str]        =    self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_			:     Any        =    3

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

     snake_case_			:     str        =    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     snake_case_			:     List[str]        =    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes:
         snake_case_			:     str        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							(padded_height, padded_width))

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_			:     Dict        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							(padded_height, padded_width))





 @slow
 def 							snake_case__		(     self):
     for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_			:     List[str]        =    DonutSwinModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_			:     Dict        =    _config_zero_init(lowercase_)
     for model_class in self.all_model_classes:
         snake_case_			:     str        =    model_class(config=lowercase_)
         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'			,							)




