






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class     lowerCamelCase_					(   lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	CanineTokenizer
				a__       							=	False







				def       A						(    self  ):
								"""simple docstring"""
								super().setUp()
								__magic_name__		:Any   =   CanineTokenizer()
								tokenizer.save_pretrained(self.tmpdirname  )







				@cached_property
				def       A						(    self  ):
								"""simple docstring"""
								return CanineTokenizer.from_pretrained('''google/canine-s'''  )







				def       A						(    self							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   self.tokenizer_class.from_pretrained(self.tmpdirname							,       **__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   1_0_2_4
								return tokenizer







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.canine_tokenizer
								__magic_name__		:int   =   ['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
								# fmt: off
								__magic_name__		:Tuple   =   [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
								# fmt: on
								__magic_name__		:int   =   tokenizer(__lowerCAmelCase							,       padding=__lowerCAmelCase							,       return_tensors='''pt'''  )
								self.assertIsInstance(__lowerCAmelCase							,       __lowerCAmelCase  )

								__magic_name__		:Union[str, Any]   =   list(batch.input_ids.numpy()[0]  )

								self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )

								self.assertEqual((2, 3_9)							,       batch.input_ids.shape  )
								self.assertEqual((2, 3_9)							,       batch.attention_mask.shape  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.canine_tokenizer
								__magic_name__		:Optional[int]   =   ['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
								__magic_name__		:Any   =   tokenizer(__lowerCAmelCase							,       padding=__lowerCAmelCase							,       return_tensors='''pt'''  )
								# check if input_ids, attention_mask and token_type_ids are returned
								self.assertIn('''input_ids'''							,       __lowerCAmelCase  )
								self.assertIn('''attention_mask'''							,       __lowerCAmelCase  )
								self.assertIn('''token_type_ids'''							,       __lowerCAmelCase  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.canine_tokenizer
								__magic_name__		:Dict   =   [
								    '''What\'s the weater?''',
								    '''It\'s about 25 degrees.''',
								]
								__magic_name__		:Tuple   =   tokenizer(
								    text_target=__lowerCAmelCase							,       max_length=3_2							,       padding='''max_length'''							,       truncation=__lowerCAmelCase							,       return_tensors='''pt'''  )
								self.assertEqual(3_2							,       targets['''input_ids'''].shape[1]  )







				def       A						(    self  ):
								"""simple docstring"""
								# safety check on max_len default value so we are sure the test works
								__magic_name__		:Any   =   self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																self.assertNotEqual(tokenizer.model_max_length							,       4_2  )

        # Now let's start the test
								__magic_name__		:Optional[Any]   =   self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																# Isolate this from the other tests because we save additional tokens/etc
																__magic_name__		:List[Any]   =   tempfile.mkdtemp()

																__magic_name__		:Optional[int]   =   ''' He is very happy, UNwant\u00E9d,running'''
																__magic_name__		:Optional[Any]   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																tokenizer.save_pretrained(__lowerCAmelCase  )

																__magic_name__		:Union[str, Any]   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
																__magic_name__		:List[Any]   =   after_tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )

																shutil.rmtree(__lowerCAmelCase  )

								__magic_name__		:int   =   self.get_tokenizers(model_max_length=4_2  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																# Isolate this from the other tests because we save additional tokens/etc
																__magic_name__		:Any   =   tempfile.mkdtemp()

																__magic_name__		:List[Any]   =   ''' He is very happy, UNwant\u00E9d,running'''

																__magic_name__		:Union[str, Any]   =   tokenizer.additional_special_tokens

																# We can add a new special token for Canine as follows:
																__magic_name__		:Optional[Any]   =   chr(0XE007  )
																additional_special_tokens.append(__lowerCAmelCase  )
																tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}  )
																__magic_name__		:str   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																tokenizer.save_pretrained(__lowerCAmelCase  )

																__magic_name__		:List[str]   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
																__magic_name__		:Optional[int]   =   after_tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )
																self.assertIn(__lowerCAmelCase							,       after_tokenizer.additional_special_tokens  )
																self.assertEqual(after_tokenizer.model_max_length							,       4_2  )

																__magic_name__		:Any   =   tokenizer.__class__.from_pretrained(__lowerCAmelCase							,       model_max_length=4_3  )
																self.assertEqual(tokenizer.model_max_length							,       4_3  )

																shutil.rmtree(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.get_tokenizers(do_lower_case=__lowerCAmelCase  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																__magic_name__  ,					__magic_name__		:Any   =   self.get_clean_sequence(__lowerCAmelCase  )

																# a special token for Canine can be defined as follows:
																__magic_name__		:str   =   0XE005
																__magic_name__		:Dict   =   chr(__lowerCAmelCase  )

																tokenizer.add_special_tokens({'''cls_token''': special_token}  )
																__magic_name__		:Dict   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertEqual(len(__lowerCAmelCase  )							,       1  )

																__magic_name__		:Tuple   =   tokenizer.decode(ids + encoded_special_token							,       clean_up_tokenization_spaces=__lowerCAmelCase  )
																__magic_name__		:Any   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )

																__magic_name__		:Any   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																__magic_name__		:Optional[Any]   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																self.assertEqual(__lowerCAmelCase							,       input_encoded + special_token_id  )

																__magic_name__		:List[str]   =   tokenizer.decode(__lowerCAmelCase							,       skip_special_tokens=__lowerCAmelCase  )
																self.assertTrue(special_token not in decoded  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.get_tokenizers(do_lower_case=__lowerCAmelCase  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																__magic_name__		:Dict   =   chr(0XE005  )
																__magic_name__		:Optional[Any]   =   chr(0XE006  )

																# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																tokenizer.add_tokens([SPECIAL_TOKEN_1]							,       special_tokens=__lowerCAmelCase  )
																# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}  )

																__magic_name__		:Union[str, Any]   =   tokenizer.tokenize(__lowerCAmelCase  )
																__magic_name__		:Optional[int]   =   tokenizer.tokenize(__lowerCAmelCase  )

																self.assertEqual(len(__lowerCAmelCase  )							,       1  )
																self.assertEqual(len(__lowerCAmelCase  )							,       1  )
																self.assertEqual(token_a[0]							,       __lowerCAmelCase  )
																self.assertEqual(token_a[0]							,       __lowerCAmelCase  )







				@require_tokenizers
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.get_tokenizers(do_lower_case=__lowerCAmelCase  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																# a special token for Canine can be defined as follows:
																__magic_name__		:int   =   0XE006
																__magic_name__		:Dict   =   chr(__lowerCAmelCase  )

																__magic_name__		:Union[str, Any]   =   AddedToken(__lowerCAmelCase							,       lstrip=__lowerCAmelCase  )
																tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}  )

																with tempfile.TemporaryDirectory() as tmp_dir_name:
																				tokenizer.save_pretrained(__lowerCAmelCase  )
																				tokenizer.from_pretrained(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   []
								if self.test_slow_tokenizer:
												tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

								if self.test_rust_tokenizer:
												tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

								for tokenizer_class, tokenizer_utils in tokenizer_list:
												with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(__lowerCAmelCase  )

																with open(os.path.join(__lowerCAmelCase							,       '''special_tokens_map.json'''  )							,       encoding='''utf-8'''  ) as json_file:
																				__magic_name__		:Union[str, Any]   =   json.load(__lowerCAmelCase  )

																with open(os.path.join(__lowerCAmelCase							,       '''tokenizer_config.json'''  )							,       encoding='''utf-8'''  ) as json_file:
																				__magic_name__		:Union[str, Any]   =   json.load(__lowerCAmelCase  )

																# a special token for Canine can be defined as follows:
																__magic_name__		:int   =   0XE006
																__magic_name__		:Union[str, Any]   =   chr(__lowerCAmelCase  )

																__magic_name__		:Optional[int]   =   [new_token_a]
																__magic_name__		:List[str]   =   [new_token_a]

																with open(os.path.join(__lowerCAmelCase							,       '''special_tokens_map.json'''  )							,       '''w'''							,       encoding='''utf-8'''  ) as outfile:
																				json.dump(__lowerCAmelCase							,       __lowerCAmelCase  )
																with open(os.path.join(__lowerCAmelCase							,       '''tokenizer_config.json'''  )							,       '''w'''							,       encoding='''utf-8'''  ) as outfile:
																				json.dump(__lowerCAmelCase							,       __lowerCAmelCase  )

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																__magic_name__		:Optional[Any]   =   tokenizer_class.from_pretrained(__lowerCAmelCase							,       extra_ids=0  )
																self.assertIn(__lowerCAmelCase							,       tokenizer_without_change_in_init.additional_special_tokens  )
																# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																self.assertEqual(
																    [new_token_a]							,       tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]  )  )							,       )

																__magic_name__		:Union[str, Any]   =   0XE007
																__magic_name__		:Tuple   =   chr(__lowerCAmelCase  )
																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																__magic_name__		:Dict   =   [AddedToken(__lowerCAmelCase							,       lstrip=__lowerCAmelCase  )]
																__magic_name__		:Optional[int]   =   tokenizer_class.from_pretrained(
																    __lowerCAmelCase							,       additional_special_tokens=__lowerCAmelCase							,       extra_ids=0  )

																self.assertIn(__lowerCAmelCase							,       tokenizer.additional_special_tokens  )
																# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																self.assertEqual(
																    [new_token_a]							,       tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]  )  )  )







				@require_tokenizers
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.get_tokenizers(do_lower_case=__lowerCAmelCase  )
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																__magic_name__		:Optional[int]   =   '''hello world'''
																if self.space_between_special_tokens:
																				__magic_name__		:Optional[Any]   =   '''[CLS] hello world [SEP]'''
																else:
																				__magic_name__		:Dict   =   input
																__magic_name__		:List[Any]   =   tokenizer.encode(__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase  )
																__magic_name__		:List[Any]   =   tokenizer.decode(__lowerCAmelCase							,       spaces_between_special_tokens=self.space_between_special_tokens  )
																self.assertIn(__lowerCAmelCase							,       [output, output.lower()]  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.get_tokenizers()
								for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																__magic_name__		:str   =   [
																    '''bos_token''',
																    '''eos_token''',
																    '''unk_token''',
																    '''sep_token''',
																    '''pad_token''',
																    '''cls_token''',
																    '''mask_token''',
																]

								__magic_name__		:Tuple   =   '''a'''
								__magic_name__		:str   =   ord(__lowerCAmelCase  )

								for attr in attributes_list:
												setattr(__lowerCAmelCase							,       attr + '''_id'''							,       __lowerCAmelCase  )
												self.assertEqual(getattr(__lowerCAmelCase							,       __lowerCAmelCase  )							,       __lowerCAmelCase  )
												self.assertEqual(getattr(__lowerCAmelCase							,       attr + '''_id'''  )							,       __lowerCAmelCase  )

												setattr(__lowerCAmelCase							,       attr + '''_id'''							,       __lowerCAmelCase  )
												self.assertEqual(getattr(__lowerCAmelCase							,       __lowerCAmelCase  )							,       __lowerCAmelCase  )
												self.assertEqual(getattr(__lowerCAmelCase							,       attr + '''_id'''  )							,       __lowerCAmelCase  )

								setattr(__lowerCAmelCase							,       '''additional_special_tokens_ids'''							,       []  )
								self.assertListEqual(getattr(__lowerCAmelCase							,       '''additional_special_tokens'''  )							,       []  )
								self.assertListEqual(getattr(__lowerCAmelCase							,       '''additional_special_tokens_ids'''  )							,       []  )

								__magic_name__		:str   =   0XE006
								__magic_name__		:Optional[Any]   =   chr(__lowerCAmelCase  )
								setattr(__lowerCAmelCase							,       '''additional_special_tokens_ids'''							,       [additional_special_token_id]  )
								self.assertListEqual(getattr(__lowerCAmelCase							,       '''additional_special_tokens'''  )							,       [additional_special_token]  )
								self.assertListEqual(getattr(__lowerCAmelCase							,       '''additional_special_tokens_ids'''  )							,       [additional_special_token_id]  )







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								pass














import copy
from typing import Dict, List, Optional

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


UpperCAmelCase__      			=						{
    '''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
}

UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """mask2former"""
						snake_case__        =      ["""swin"""]
						snake_case__        =      {"""hidden_size""": """hidden_dim"""}



						def __init__(self:					Any ,					__UpperCAmelCase:					Optional[Dict] = None ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 1024 ,					__UpperCAmelCase:					str = "relu" ,					__UpperCAmelCase:					int = 6 ,					__UpperCAmelCase:					int = 10 ,					__UpperCAmelCase:					int = 8 ,					__UpperCAmelCase:					float = 0.0 ,					__UpperCAmelCase:					int = 2048 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					int = 4 ,					__UpperCAmelCase:					int = 255 ,					__UpperCAmelCase:					int = 100 ,					__UpperCAmelCase:					float = 0.1 ,					__UpperCAmelCase:					float = 2.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					int = 12544 ,					__UpperCAmelCase:					float = 3.0 ,					__UpperCAmelCase:					float = 0.75 ,					__UpperCAmelCase:					float = 0.02 ,					__UpperCAmelCase:					float = 1.0 ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					List[int] = [4, 8, 16, 32] ,					__UpperCAmelCase:					bool = None ,					**__UpperCAmelCase:					Any ,					)    ->     int:




											'''simple docstring'''



											if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
																__a					:						str      =					CONFIG_MAPPING["swin"](
																    image_size=224 ,					in_channels=3 ,					patch_size=4 ,					embed_dim=96 ,					depths=[2, 2, 18, 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"] ,					)

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[Any]      =					backbone_config.pop("model_type"   )
																__a					:						List[str]      =					CONFIG_MAPPING[backbone_model_type]
																__a					:						Dict      =					config_class.from_dict(__UpperCAmelCase   )

											# 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   )}'   )

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

											super().__init__(**__UpperCAmelCase   )



						@classmethod
						def        UpperCAmelCase__					(cls:					Tuple ,					__UpperCAmelCase:					PretrainedConfig ,					**__UpperCAmelCase:					List[str]   )    ->     Any:




											'''simple docstring'''



											return cls(
											    backbone_config=__UpperCAmelCase ,					**__UpperCAmelCase ,					)



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




											'''simple docstring'''



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





