


"""simple docstring"""







import copy
from typing import Dict, Optional

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


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """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 		_A    (    __A    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[Any]      =			"""oneformer"""
    UpperCAmelCase			:				Optional[Any]      =			{"""hidden_size""": """hidden_dim"""}






    def __init__(   self							:       Tuple							,					__UpperCAmelCase							:       List[str] = None							,					__UpperCAmelCase							:       Optional[int] = 255							,					__UpperCAmelCase							:       Any = 150							,					__UpperCAmelCase							:       List[str] = 0.1							,					__UpperCAmelCase							:       Dict = 2.0							,					__UpperCAmelCase							:       Dict = 5.0							,					__UpperCAmelCase							:       Dict = 5.0							,					__UpperCAmelCase							:       Optional[Any] = 0.5							,					__UpperCAmelCase							:       Optional[int] = 0.07							,					__UpperCAmelCase							:       Union[str, Any] = 12544							,					__UpperCAmelCase							:       Dict = 3.0							,					__UpperCAmelCase							:       Optional[Any] = 0.75							,					__UpperCAmelCase							:       Optional[int] = 0.02							,					__UpperCAmelCase							:       Optional[Any] = 1.0							,					__UpperCAmelCase							:       List[str] = 1e-05							,					__UpperCAmelCase							:       List[Any] = False							,					__UpperCAmelCase							:       Optional[int] = True							,					__UpperCAmelCase							:       str = True							,					__UpperCAmelCase							:       str = [4, 8, 16, 32]							,					__UpperCAmelCase							:       Optional[int] = 77							,					__UpperCAmelCase							:       Tuple = 256							,					__UpperCAmelCase							:       Dict = 77							,					__UpperCAmelCase							:       Any = 6							,					__UpperCAmelCase							:       List[Any] = 49408							,					__UpperCAmelCase							:       Union[str, Any] = 2							,					__UpperCAmelCase							:       Union[str, Any] = 16							,					__UpperCAmelCase							:       Any = 256							,					__UpperCAmelCase							:       Dict = 256							,					__UpperCAmelCase							:       List[Any] = 256							,					__UpperCAmelCase							:       List[Any] = 1024							,					__UpperCAmelCase							:       Tuple = "GN"							,					__UpperCAmelCase							:       int = 6							,					__UpperCAmelCase							:       Optional[Any] = 10							,					__UpperCAmelCase							:       List[str] = True							,					__UpperCAmelCase							:       List[Any] = 8							,					__UpperCAmelCase							:       Dict = 0.1							,					__UpperCAmelCase							:       List[Any] = 2048							,					__UpperCAmelCase							:       Dict = False							,					__UpperCAmelCase							:       int = False							,					__UpperCAmelCase							:       List[Any] = 2							,					__UpperCAmelCase							:       Optional[Any] = 4							,					**__UpperCAmelCase							:       Any							,					):
      if backbone_config is None:
        logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone.")
        a		:						Union[str, Any] 				=     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__):
        a		:						str 				=     backbone_config.get("model_type")
        a		:						Dict 				=     CONFIG_MAPPING[backbone_model_type]
        a		:						List[Any] 				=     config_class.from_dict(UpperCamelCase__)

      a		:						Dict 				=     backbone_config

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

      super().__init__(**UpperCamelCase__)






    def       __snake_case				(   self							:       List[Any]):
      a		:						Tuple 				=     copy.deepcopy(self.__dict__)
      a		:						Any 				=     self.backbone_config.to_dict()
      a		:						Any 				=     self.__class__.model_type
      return output














import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__a				(	__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    HerbertTokenizer
		UpperCAmelCase__   :					List[Any]					     =    HerbertTokenizerFast
		UpperCAmelCase__   :					Optional[Any]					     =    True


		def 	__snake_case      (    self							):
						super().setUp()

						# Use a simpler test file without japanese/chinese characters
						with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,     encoding='utf-8'							) as f_data:
										SCREAMING_SNAKE_CASE_ :						Dict    				= f_data.read().replace('\n\n'			,     '\n'							).strip()

						SCREAMING_SNAKE_CASE_ :						str    				= [
						    '<s>',
						    '</s>',
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    ',</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_ :						str    				= dict(zip(UpperCamelCase__			,     range(len(UpperCamelCase__							)							)							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file			,     'w'							) as fp:
										fp.write(json.dumps(UpperCamelCase__							)							)
						with open(self.merges_file			,     'w'							) as fp:
										fp.write('\n'.join(UpperCamelCase__							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 'lower newer'
						SCREAMING_SNAKE_CASE_ :						int    				= 'lower newer'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(vocab_file=self.vocab_file			,     merges_file=self.merges_file							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 'lower'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= 'lower,newer'

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.tokenize(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= rust_tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= rust_tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.encode(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= rust_tokenizer.encode(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= tokenizer.encode('konstruowanie sekwencji'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.encode('konstruowanie wielu sekwencji'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + text_a + [2]