

import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 						__magic_name__					(							lowercase_			,      unittest.TestCase				):
     UpperCamelCase_	:str										=      RoCBertTokenizer
     UpperCamelCase_	:List[Any]										=      None
     UpperCamelCase_	:int										=      False
     UpperCamelCase_	:Union[str, Any]										=      True
     UpperCamelCase_	:Optional[int]										=      filter_non_english






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         super().setUp()

         UpperCamelCase_			  =						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
         UpperCamelCase_			  =						{}
         UpperCamelCase_			  =						{}
         for i, value in enumerate(lowerCamelCase_	):
             UpperCamelCase_			  =						i
             UpperCamelCase_			  =						i
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["word_shape_file"]	)
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["word_pronunciation_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)
         with open(self.word_shape_file		,						"w"		,						encoding="utf-8"	) as word_shape_writer:
             json.dump(lowerCamelCase_		,						lowerCamelCase_		,						ensure_ascii=lowerCamelCase_	)
         with open(self.word_pronunciation_file		,						"w"		,						encoding="utf-8"	) as word_pronunciation_writer:
             json.dump(lowerCamelCase_		,						lowerCamelCase_		,						ensure_ascii=lowerCamelCase_	)






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file		,						self.word_shape_file		,						self.word_pronunciation_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("你好[SEP]你是谁"	)
         self.assertListEqual(lowerCamelCase_		,						["你", "好", "[SEP]", "你", "是", "谁"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_	)		,						[5, 6, 2, 5, 7, 8]	)
         self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowerCamelCase_	)		,						[5, 6, 2, 5, 7, 8]	)
         self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase_	)		,						[5, 6, 2, 5, 7, 8]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						RoCBertBasicTokenizer()

         self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"	)		,						["ah", "\u535A", "\u63A8", "zz"]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)		,						["hello", "!", "how", "are", "you", "?"]	)
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)		,						["hello"]	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_		,						strip_accents=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,						["hällo", "!", "how", "are", "you", "?"]	)
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)		,						["h\u00E9llo"]	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_		,						strip_accents=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,						["hallo", "!", "how", "are", "you", "?"]	)
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)		,						["hello"]	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,						["hallo", "!", "how", "are", "you", "?"]	)
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)		,						["hello"]	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)		,						["HeLLo", "!", "how", "Are", "yoU", "?"]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_		,						strip_accents=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,						["HäLLo", "!", "how", "Are", "yoU", "?"]	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_		,						strip_accents=lowerCamelCase_	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,						["HaLLo", "!", "how", "Are", "yoU", "?"]	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						RoCBertBasicTokenizer(do_lower_case=lowerCamelCase_		,						never_split=["[UNK]"]	)

         self.assertListEqual(
             tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"	)		,						["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

         UpperCamelCase_			  =						{}
         for i, token in enumerate(lowerCamelCase_	):
             UpperCamelCase_			  =						i
         UpperCamelCase_			  =						RoCBertWordpieceTokenizer(vocab=lowerCamelCase_		,						unk_token="[UNK]"	)

         self.assertListEqual(tokenizer.tokenize(""	)		,						[]	)

         self.assertListEqual(tokenizer.tokenize("unwanted running"	)		,						["un", "##want", "##ed", "runn", "##ing"]	)

         self.assertListEqual(tokenizer.tokenize("unwantedX running"	)		,						["[UNK]", "runn", "##ing"]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         self.assertTrue(_is_whitespace(" "	)	)
         self.assertTrue(_is_whitespace("\t"	)	)
         self.assertTrue(_is_whitespace("\r"	)	)
         self.assertTrue(_is_whitespace("\n"	)	)
         self.assertTrue(_is_whitespace("\u00A0"	)	)

         self.assertFalse(_is_whitespace("A"	)	)
         self.assertFalse(_is_whitespace("-"	)	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         self.assertTrue(_is_control("\u0005"	)	)

         self.assertFalse(_is_control("A"	)	)
         self.assertFalse(_is_control(" "	)	)
         self.assertFalse(_is_control("\t"	)	)
         self.assertFalse(_is_control("\r"	)	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         self.assertTrue(_is_punctuation("-"	)	)
         self.assertTrue(_is_punctuation("$"	)	)
         self.assertTrue(_is_punctuation("`"	)	)
         self.assertTrue(_is_punctuation("."	)	)

         self.assertFalse(_is_punctuation("A"	)	)
         self.assertFalse(_is_punctuation(" "	)	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.get_tokenizer()

         # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
         self.assertListEqual([tokenizer.tokenize(lowerCamelCase_	) for t in ["Test", "\xad", "test"]]		,						[["[UNK]"], [], ["[UNK]"]]	)

         if self.test_rust_tokenizer:
             UpperCamelCase_			  =						self.get_rust_tokenizer()
             self.assertListEqual(
                 [rust_tokenizer.tokenize(lowerCamelCase_	) for t in ["Test", "\xad", "test"]]		,						[["[UNK]"], [], ["[UNK]"]]	)






     def 	UpperCAmelCase_				(				self	)->     Any:



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                 UpperCamelCase_			  =						self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		,						**lowerCamelCase_	)

                 UpperCamelCase_			  =						F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                 UpperCamelCase_			  =						tokenizer_r.encode_plus(
                     lowerCamelCase_		,						return_attention_mask=lowerCamelCase_		,						return_token_type_ids=lowerCamelCase_		,						return_offsets_mapping=lowerCamelCase_		,						add_special_tokens=lowerCamelCase_		,						)

                 UpperCamelCase_			  =						tokenizer_r.do_lower_case if hasattr(lowerCamelCase_		,						"do_lower_case"	) else False
                 UpperCamelCase_			  =						(
                     [
                         ((0, 0), tokenizer_r.cls_token),
                         ((0, 1), """A"""),
                         ((1, 2), ""","""),
                         ((3, 5), """na"""),
                         ((5, 6), """##ï"""),
                         ((6, 8), """##ve"""),
                         ((9, 15), tokenizer_r.mask_token),
                         ((16, 21), """Allen"""),
                         ((21, 23), """##NL"""),
                         ((23, 24), """##P"""),
                         ((25, 33), """sentence"""),
                         ((33, 34), """."""),
                         ((0, 0), tokenizer_r.sep_token),
                     ]
                     if not do_lower_case
                     else [
                         ((0, 0), tokenizer_r.cls_token),
                         ((0, 1), """a"""),
                         ((1, 2), ""","""),
                         ((3, 8), """naive"""),
                         ((9, 15), tokenizer_r.mask_token),
                         ((16, 21), """allen"""),
                         ((21, 23), """##nl"""),
                         ((23, 24), """##p"""),
                         ((25, 33), """sentence"""),
                         ((33, 34), """."""),
                         ((0, 0), tokenizer_r.sep_token),
                     ]
                 )

                 self.assertEqual(
                     [e[1] for e in expected_results]		,						tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]	)	)
                 self.assertEqual([e[0] for e in expected_results]		,						tokens["offset_mapping"]	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						["""的""", """人""", """有"""]
         UpperCamelCase_			  =						"""""".join(lowerCamelCase_	)
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                 UpperCamelCase_			  =						True
                 UpperCamelCase_			  =						self.tokenizer_class.from_pretrained(lowerCamelCase_		,						**lowerCamelCase_	)
                 UpperCamelCase_			  =						self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		,						**lowerCamelCase_	)

                 UpperCamelCase_			  =						tokenizer_p.encode(lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer_r.encode(lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)

                 UpperCamelCase_			  =						tokenizer_r.convert_ids_to_tokens(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer_p.convert_ids_to_tokens(lowerCamelCase_	)

                 # it is expected that each Chinese character is not preceded by "##"
                 self.assertListEqual(lowerCamelCase_		,						lowerCamelCase_	)
                 self.assertListEqual(lowerCamelCase_		,						lowerCamelCase_	)

                 UpperCamelCase_			  =						False
                 UpperCamelCase_			  =						self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		,						**lowerCamelCase_	)
                 UpperCamelCase_			  =						self.tokenizer_class.from_pretrained(lowerCamelCase_		,						**lowerCamelCase_	)

                 UpperCamelCase_			  =						tokenizer_r.encode(lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer_p.encode(lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)

                 UpperCamelCase_			  =						tokenizer_r.convert_ids_to_tokens(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer_p.convert_ids_to_tokens(lowerCamelCase_	)

                 # it is expected that only the first Chinese character is not preceded by "##".
                 UpperCamelCase_			  =						[
                     F"##{token}" if idx != 0 else token for idx, token in enumerate(lowerCamelCase_	)
                 ]
                 self.assertListEqual(lowerCamelCase_		,						lowerCamelCase_	)
                 self.assertListEqual(lowerCamelCase_		,						lowerCamelCase_	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file		,						self.word_shape_file		,						self.word_pronunciation_file	)

         UpperCamelCase_			  =						tokenizer.encode("你好"		,						add_special_tokens=lowerCamelCase_	)
         UpperCamelCase_			  =						tokenizer.encode("你是谁"		,						add_special_tokens=lowerCamelCase_	)

         UpperCamelCase_			  =						tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	)
         UpperCamelCase_			  =						tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		,						lowerCamelCase_	)

         assert encoded_sentence == [1] + text + [2]
         assert encoded_pair == [1] + text + [2] + text_a + [2]






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.get_tokenizers(do_lower_case=lowerCamelCase_	)
         for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase_			  =						"""你好，你是谁"""
                 UpperCamelCase_			  =						tokenizer.tokenize(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer.convert_tokens_to_ids(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer.convert_tokens_to_shape_ids(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase_	)
                 UpperCamelCase_			  =						tokenizer.prepare_for_model(
                     lowerCamelCase_		,						lowerCamelCase_		,						lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)

                 UpperCamelCase_			  =						tokenizer.encode_plus(lowerCamelCase_		,						add_special_tokens=lowerCamelCase_	)

                 self.assertEqual(lowerCamelCase_		,						lowerCamelCase_	)







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



