


'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase  :      str      		=				logging.get_logger(__name__)

__lowercase  :      List[Any]      		=				{
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class _snake_case   ( a__   ):
       """simple docstring"""





       __a												=     '''oneformer'''
       __a												=     {'''hidden_size''': '''hidden_dim'''}







       def __init__(   self			,				__A = None			,				__A = 255			,				__A = 150			,				__A = 0.1			,				__A = 2.0			,				__A = 5.0			,				__A = 5.0			,				__A = 0.5			,				__A = 0.07			,				__A = 12544			,				__A = 3.0			,				__A = 0.75			,				__A = 0.02			,				__A = 1.0			,				__A = 1E-05			,				__A = False			,				__A = True			,				__A = True			,				__A = [4, 8, 16, 32]			,				__A = 77			,				__A = 256			,				__A = 77			,				__A = 6			,				__A = 49408			,				__A = 2			,				__A = 16			,				__A = 256			,				__A = 256			,				__A = 256			,				__A = 1024			,				__A = "GN"			,				__A = 6			,				__A = 10			,				__A = True			,				__A = 8			,				__A = 0.1			,				__A = 2048			,				__A = False			,				__A = False			,				__A = 2			,				__A = 4			,				**__A			,				)     ->List[Any]:



           """simple docstring"""







           if backbone_config is None:
               logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."	)
               __lowerCamelCase		:      str											=					CONFIG_MAPPING["swin"](
                   image_size=224			,				in_channels=3			,				patch_size=4			,				embed_dim=96			,				depths=[2, 2, 6, 2]			,				num_heads=[3, 6, 12, 24]			,				window_size=7			,				drop_path_rate=0.3			,				use_absolute_embeddings=__A			,				out_features=["stage1", "stage2", "stage3", "stage4"]			,				)
           elif isinstance(__A			,				__A	):
               __lowerCamelCase		:      Any											=					backbone_config.get("model_type"	)
               __lowerCamelCase		:      Union[str, Any]											=					CONFIG_MAPPING[backbone_model_type]
               __lowerCamelCase		:      str											=					config_class.from_dict(__A	)

           __lowerCamelCase		:      List[str]											=					backbone_config

           __lowerCamelCase		:      List[Any]											=					ignore_value
           __lowerCamelCase		:      Optional[int]											=					num_queries
           __lowerCamelCase		:      int											=					no_object_weight
           __lowerCamelCase		:      Optional[Any]											=					class_weight
           __lowerCamelCase		:      str											=					mask_weight
           __lowerCamelCase		:      Any											=					dice_weight
           __lowerCamelCase		:      Any											=					contrastive_weight
           __lowerCamelCase		:      Optional[Any]											=					contrastive_temperature
           __lowerCamelCase		:      Tuple											=					train_num_points
           __lowerCamelCase		:      int											=					oversample_ratio
           __lowerCamelCase		:      str											=					importance_sample_ratio
           __lowerCamelCase		:      Optional[int]											=					init_std
           __lowerCamelCase		:      Union[str, Any]											=					init_xavier_std
           __lowerCamelCase		:      Optional[int]											=					layer_norm_eps
           __lowerCamelCase		:      Any											=					is_training
           __lowerCamelCase		:      Union[str, Any]											=					use_auxiliary_loss
           __lowerCamelCase		:      Dict											=					output_auxiliary_logits
           __lowerCamelCase		:      Dict											=					strides
           __lowerCamelCase		:      int											=					task_seq_len
           __lowerCamelCase		:      int											=					text_encoder_width
           __lowerCamelCase		:      Union[str, Any]											=					text_encoder_context_length
           __lowerCamelCase		:      List[Any]											=					text_encoder_num_layers
           __lowerCamelCase		:      Any											=					text_encoder_vocab_size
           __lowerCamelCase		:      List[Any]											=					text_encoder_proj_layers
           __lowerCamelCase		:      Dict											=					text_encoder_n_ctx
           __lowerCamelCase		:      List[Any]											=					conv_dim
           __lowerCamelCase		:      str											=					mask_dim
           __lowerCamelCase		:      str											=					hidden_dim
           __lowerCamelCase		:      Union[str, Any]											=					encoder_feedforward_dim
           __lowerCamelCase		:      Union[str, Any]											=					norm
           __lowerCamelCase		:      Tuple											=					encoder_layers
           __lowerCamelCase		:      Tuple											=					decoder_layers
           __lowerCamelCase		:      Any											=					use_task_norm
           __lowerCamelCase		:      Dict											=					num_attention_heads
           __lowerCamelCase		:      str											=					dropout
           __lowerCamelCase		:      Union[str, Any]											=					dim_feedforward
           __lowerCamelCase		:      Dict											=					pre_norm
           __lowerCamelCase		:      int											=					enforce_input_proj
           __lowerCamelCase		:      Any											=					query_dec_layers
           __lowerCamelCase		:      int											=					common_stride
           __lowerCamelCase		:      Any											=					decoder_layers

           super().__init__(**__A	)







       def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict[str, any]:



           """simple docstring"""







           __lowerCamelCase		:      Union[str, Any]											=					copy.deepcopy(self.__dict__	)
           __lowerCamelCase		:      int											=					self.backbone_config.to_dict()
           __lowerCamelCase		:      Any											=					self.__class__.model_type
           return output









'''simple docstring'''




import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase  :      Any      		=				get_tests_dir("""fixtures/spiece.model""")





@require_sentencepiece
@require_tokenizers
class _snake_case   ( a__    ,   unittest.TestCase   ):
							"""simple docstring"""





							__a												=     ErnieMTokenizer
							__a												=     False
							__a												=     True
							__a												=     False
							__a												=     False







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:



											"""simple docstring"""







											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCamelCase		:      Optional[int]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
											tokenizer.save_pretrained(self.tmpdirname	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					"this is a test"
											__lowerCamelCase		:      List[str]											=					"this is a test"
											return input_text, output_text







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					"<pad>"
											__lowerCamelCase		:      Any											=					0

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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:      Union[str, Any]											=					list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]			,				"<pad>"	)
											self.assertEqual(vocab_keys[1]			,				"<unk>"	)
											self.assertEqual(vocab_keys[-1]			,				"▁eloquent"	)
											self.assertEqual(len(__A	)			,				30000	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







											self.assertEqual(self.get_tokenizer().vocab_size			,				30000	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											if not self.test_rust_tokenizer:
															return

											__lowerCamelCase		:      Union[str, Any]											=					self.get_tokenizer()
											__lowerCamelCase		:      Optional[Any]											=					self.get_rust_tokenizer()

											__lowerCamelCase		:      Dict											=					"I was born in 92000, and this is falsé."

											__lowerCamelCase		:      str											=					tokenizer.tokenize(__A	)
											__lowerCamelCase		:      str											=					rust_tokenizer.tokenize(__A	)
											self.assertListEqual(__A			,				__A	)

											__lowerCamelCase		:      Tuple											=					tokenizer.encode(__A			,				add_special_tokens=__A	)
											__lowerCamelCase		:      Tuple											=					rust_tokenizer.encode(__A			,				add_special_tokens=__A	)
											self.assertListEqual(__A			,				__A	)

											__lowerCamelCase		:      Optional[int]											=					self.get_rust_tokenizer()
											__lowerCamelCase		:      Dict											=					tokenizer.encode(__A	)
											__lowerCamelCase		:      List[str]											=					rust_tokenizer.encode(__A	)
											self.assertListEqual(__A			,				__A	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      Tuple											=					ErnieMTokenizer(__A			,				do_lower_case=__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)

											__lowerCamelCase		:      Optional[Any]											=					tokenizer.tokenize("This is a test"	)
											self.assertListEqual(__A			,				["▁this", "▁is", "▁a", "▁test"]	)

											self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)			,				[48, 25, 21, 1289]	)

											__lowerCamelCase		:      Any											=					tokenizer.tokenize("I was born in 92000, and this is falsé."	)
											# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
											self.assertListEqual(
											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
											__lowerCamelCase		:      Optional[int]											=					tokenizer.convert_tokens_to_ids(__A	)
											self.assertListEqual(__A			,				[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)

											__lowerCamelCase		:      Dict											=					tokenizer.convert_ids_to_tokens(__A	)
											self.assertListEqual(
											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,				)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Union[str, Any]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)

											__lowerCamelCase		:      int											=					tokenizer.encode("sequence builders"	)
											__lowerCamelCase		:      List[Any]											=					tokenizer.encode("multi-sequence build"	)

											__lowerCamelCase		:      Tuple											=					tokenizer.build_inputs_with_special_tokens(__A	)
											__lowerCamelCase		:      Optional[int]											=					tokenizer.build_inputs_with_special_tokens(__A			,				__A	)

											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
											    tokenizer.sep_token_id
											] + text_a + [tokenizer.sep_token_id]







							@slow
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      Tuple											=					{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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, 1, 1, 1]]}
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__A			,				model_name="susnato/ernie-m-base_pytorch"			,				sequences=[
											        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
											        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
											        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
											        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
											        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
											        "conditioning on both left and right context in all layers.",
											        "The quick brown fox jumps over the lazy dog.",
											    ]			,				)





