






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							lowercase  :							Optional[Any]			  =    get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowercase  :							Dict			  =    12_80_22
lowercase  :							List[Any]			  =    12_80_28




@require_sentencepiece
class 		UpperCAmelCase_						(	__UpperCAmelCase					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Dict						=					MaMaaaTokenizer
						A     :   Any						=					False
						A     :   Tuple						=					False
						A     :   List[Any]						=					True






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								super().setUp()

								snake_case_				:							Optional[Any]						 =       ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
								snake_case_				:							Union[str, Any]						 =       dict(zip(__SCREAMING_SNAKE_CASE		,				range(len(__SCREAMING_SNAKE_CASE				)				)				)				)
								snake_case_				:							List[Any]						 =       Path(self.tmpdirname				)
								save_json(__SCREAMING_SNAKE_CASE		,				save_dir / VOCAB_FILES_NAMES["vocab_file"]				)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
										copyfile(__SCREAMING_SNAKE_CASE		,				save_dir / VOCAB_FILES_NAMES["spm_file"]				)

								snake_case_				:							List[str]						 =       MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
								tokenizer.save_pretrained(self.tmpdirname				)






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Any:
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname		,				**__SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								return (
								    "This is a test",
								    "This is a test",
								)






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							List[Any]						 =       "</s>"
								snake_case_				:							Union[str, Any]						 =       0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE				)		,				__SCREAMING_SNAKE_CASE				)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE				)		,				__SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Optional[int]						 =       self.get_tokenizer()
								snake_case_				:							Optional[int]						 =       list(tokenizer.get_vocab().keys()				)

								self.assertEqual(vocab_keys[0]		,				"</s>"				)
								self.assertEqual(vocab_keys[1]		,				"<unk>"				)
								self.assertEqual(vocab_keys[-1]		,				"<s>"				)
								self.assertEqual(len(__SCREAMING_SNAKE_CASE				)		,				tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)






						@unittest.skip("Skip this test while all models are still to be uploaded."				)
						def       _lowerCAmelCase       (	self				) ->				Any:
								pass






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							int						 =       self.get_tokenizer()

								snake_case_				:							str						 =       tokenizer.tokenize("This is a test"				)
								self.assertListEqual(__SCREAMING_SNAKE_CASE		,				["▁This", "▁is", "▁a", "▁t", "est"]				)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE				)		,				[2, 3, 4, 5, 6]		,				)

								snake_case_				:							Any						 =       tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
								self.assertListEqual(__SCREAMING_SNAKE_CASE		,				["▁This", "▁is", "▁a", "▁t", "est"]				)

								snake_case_				:							Any						 =       tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE				)
								self.assertEqual(__SCREAMING_SNAKE_CASE		,				"This is a test"				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							List[str]						 =       {"input_ids": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=__SCREAMING_SNAKE_CASE		,				model_name="facebook/m2m100_418M"		,				revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"		,				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						A     :   int						=					'''facebook/m2m100_418M'''
						A     :   int						=					[
						    '''In my opinion, there are two levels of response from the French government.''',
						    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
						]
						A     :   List[Any]						=					[
						    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
						    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
						]

						# fmt: off
						A     :   List[Any]						=					[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]






						@classmethod
						def       _lowerCAmelCase       (	cls				) ->				Optional[Any]:
								snake_case_				:							Tuple						 =       MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name		,				src_lang="en"		,				tgt_lang="fr"				)
								snake_case_				:							List[Any]						 =       1
								return cls






						def       _lowerCAmelCase       (	self				) ->				Any:
								self.assertEqual(self.tokenizer.get_lang_id("ar"				)		,				12_8006				)
								self.assertEqual(self.tokenizer.get_lang_id("en"				)		,				12_8022				)
								self.assertEqual(self.tokenizer.get_lang_id("ro"				)		,				12_8076				)
								self.assertEqual(self.tokenizer.get_lang_id("mr"				)		,				12_8063				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       self.tokenizer.get_vocab()
								self.assertEqual(len(__SCREAMING_SNAKE_CASE				)		,				self.tokenizer.vocab_size				)
								self.assertEqual(vocab["<unk>"]		,				3				)
								self.assertIn(self.tokenizer.get_lang_token("en"				)		,				__SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Union[str, Any]						 =       "en"
								snake_case_				:							Any						 =       self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
								self.assertListEqual(self.expected_src_tokens		,				__SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								self.assertIn(__SCREAMING_SNAKE_CASE		,				self.tokenizer.all_special_ids				)
								# fmt: off
								snake_case_				:							List[str]						 =       [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
								# fmt: on
								snake_case_				:							Any						 =       self.tokenizer.decode(__SCREAMING_SNAKE_CASE		,				skip_special_tokens=__SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       self.tokenizer.decode(generated_ids[1:]		,				skip_special_tokens=__SCREAMING_SNAKE_CASE				)
								self.assertEqual(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE				)
								self.assertNotIn(self.tokenizer.eos_token		,				__SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							int						 =       tempfile.mkdtemp()
								snake_case_				:							List[Any]						 =       self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       MaMaaaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
								self.assertDictEqual(new_tok.lang_token_to_id		,				__SCREAMING_SNAKE_CASE				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							List[str]						 =       "en"
								snake_case_				:							Optional[int]						 =       "fr"

								snake_case_				:							str						 =       self.tokenizer(self.src_text		,				text_target=self.tgt_text		,				padding=__SCREAMING_SNAKE_CASE		,				return_tensors="pt"				)

								snake_case_				:							Dict						 =       shift_tokens_right(
								    batch["labels"]		,				self.tokenizer.pad_token_id		,				self.tokenizer.eos_token_id				)

								for k in batch:
										snake_case_				:							List[str]						 =       batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							List[Any]						 =       "mr"
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id("mr"				)]				)
								self.assertListEqual(self.tokenizer.suffix_tokens		,				[self.tokenizer.eos_token_id]				)

								snake_case_				:							List[Any]						 =       "zh"
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id("zh"				)]				)
								self.assertListEqual(self.tokenizer.suffix_tokens		,				[self.tokenizer.eos_token_id]				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							List[Any]						 =       "mr"
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id("mr"				)]				)
								self.assertListEqual(self.tokenizer.suffix_tokens		,				[self.tokenizer.eos_token_id]				)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)

								snake_case_				:							int						 =       "zh"
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id("zh"				)]				)
								self.assertListEqual(self.tokenizer.suffix_tokens		,				[self.tokenizer.eos_token_id]				)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens		,				[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Dict						 =       self.tokenizer._build_translation_inputs("A test"		,				return_tensors="pt"		,				src_lang="en"		,				tgt_lang="ar"				)

								self.assertEqual(
								    nested_simplify(__SCREAMING_SNAKE_CASE				)		,				{
								        # en_XX, A, test, EOS
								        "input_ids": [[12_8022, 58, 4183, 2]],
								        "attention_mask": [[1, 1, 1, 1]],
								        # ar_AR
								        "forced_bos_token_id": 12_8006,
								    }		,				)









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


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''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_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_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=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[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
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
