'''simple docstring'''





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         UpperCAmelCase_           =   5

         # Realm tok
         UpperCAmelCase_           =   [
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "test",
             "question",
             "this",
             "is",
             "the",
             "first",
             "second",
             "third",
             "fourth",
             "fifth",
             "record",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_tokenizer" )
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   os.path.join(__a   ,			VOCAB_FILES_NAMES["vocab_file"] )
         with open(self.vocab_file   ,			"w"   ,			encoding="utf-8" ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_block_records" )
         os.makedirs(__a   ,			exist_ok=__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_tokenizer" ) )



    def 					_lowercase						(self    :    Any ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   RealmConfig(num_block_records=self.num_block_records )
         return config



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "question": ["foo", "bar"],
                 "answers": [["Foo", "Bar"], ["Bar"]],
             } )
         return dataset



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   np.array(
             [
                 B"This is the first record",
                 B"This is the second record",
                 B"This is the third record",
                 B"This is the fourth record",
                 B"This is the fifth record",
                 B"This is a longer longer longer record",
             ]   ,			dtype=__a   ,			)
         return block_records



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   RealmRetriever(
             block_records=self.get_dummy_block_records()   ,			tokenizer=self.get_tokenizer()   ,			)
         return retriever



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(concat_inputs.input_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.attention_mask.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.token_type_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.special_tokens_mask.shape   ,			(2, 10) )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]   ,			)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]   ,			)



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3, 5]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth", "longer longer"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual([False, True, True]   ,			__a )
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,			__a )
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,			__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )

         # Test local path
         UpperCAmelCase_           =   retriever.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )
         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )

         # Test mocked remote path
         with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
              UpperCAmelCase_           =   os.path.join(
                  os.path.join(self.tmpdirname   ,			"realm_block_records" )   ,			_REALM_BLOCK_RECORDS_FILENAME )
              UpperCAmelCase_           =   RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )

         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )









import copy
from typing import Dict, List, Optional

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


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





