
"""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


UpperCamelCase      :							Dict          =					get_tests_dir("fixtures/spiece.model")

@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		ErnieMTokenizer
     lowercase		  =		False
     lowercase		  =		True
     lowercase		  =		False
     lowercase		  =		False





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().setUp()

      # We have a SentencePiece fixture for testing
      __UpperCamelCase							    =			ErnieMTokenizer(__UpperCAmelCase      ,	unk_token='<unk>'      ,	pad_token='<pad>' )
      tokenizer.save_pretrained(self.tmpdirname )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			'this is a test'
      __UpperCamelCase							    =			'this is a test'
      return input_text, output_text





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			'<pad>'
      __UpperCamelCase							    =			0

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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			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(__UpperCAmelCase )      ,	3_0000 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size      ,	3_0000 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      if not self.test_rust_tokenizer:
       return

      __UpperCamelCase							    =			self.get_tokenizer()
      __UpperCamelCase							    =			self.get_rust_tokenizer()

      __UpperCamelCase							    =			'I was born in 92000, and this is falsé.'

      __UpperCamelCase							    =			tokenizer.tokenize(__UpperCAmelCase )
      __UpperCamelCase							    =			rust_tokenizer.tokenize(__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			tokenizer.encode(__UpperCAmelCase      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			rust_tokenizer.encode(__UpperCAmelCase      ,	add_special_tokens=__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )

      __UpperCamelCase							    =			self.get_rust_tokenizer()
      __UpperCamelCase							    =			tokenizer.encode(__UpperCAmelCase )
      __UpperCamelCase							    =			rust_tokenizer.encode(__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			ErnieMTokenizer(__UpperCAmelCase      ,	do_lower_case=__UpperCAmelCase      ,	unk_token='<unk>'      ,	pad_token='<pad>' )

      __UpperCamelCase							    =			tokenizer.tokenize('This is a test' )
      self.assertListEqual(__UpperCAmelCase      ,	['▁this', '▁is', '▁a', '▁test'] )

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

      __UpperCamelCase							    =			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(
          __UpperCAmelCase      ,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'] )
      __UpperCamelCase							    =			tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )

      __UpperCamelCase							    =			tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
      self.assertListEqual(
          __UpperCAmelCase      ,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']      ,	)





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			ErnieMTokenizer(__UpperCAmelCase      ,	unk_token='<unk>'      ,	pad_token='<pad>' )

      __UpperCamelCase							    =			tokenizer.encode('sequence builders' )
      __UpperCamelCase							    =			tokenizer.encode('multi-sequence build' )

      __UpperCamelCase							    =			tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase      ,	__UpperCAmelCase )

      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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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=__UpperCAmelCase      ,	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.',
          ]      ,	)





"""simple docstring"""

import copy
from typing import Dict, Optional

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


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "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 __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			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=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



