



'''simple docstring'''

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Dict   				=					LEDTokenizer
							lowerCAmelCase       :    List[str]   				=					LEDTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True







							def 							__lowercase			(							self					:       List[str]		):
									super().setUp()
									_a     :      str        =							[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									_a     :      Tuple        =							dict(zip(_UpperCAmelCase	,range(len(_UpperCAmelCase		)		)		)		)
									_a     :      List[str]        =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									_a     :      Tuple        =							{'unk_token': '<unk>'}

									_a     :      Tuple        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									_a     :      Optional[Any]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write(json.dumps(_UpperCAmelCase		) + '\n'		)
									with open(self.merges_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write('\n'.join(_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       List[Any]	,**_UpperCAmelCase					:       List[Any]		):
									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       str		):
									kwargs.update(self.special_tokens_map		)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Any		):
									return "lower newer", "lower newer"







							@cached_property
							def 							__lowercase			(							self					:       str		):
									return LEDTokenizer.from_pretrained('allenai/led-base-16384'		)







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Optional[Any]        =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

											self.assertEqual((2, 9)	,batch.input_ids.shape		)
											self.assertEqual((2, 9)	,batch.attention_mask.shape		)
											_a     :      int        =							batch.input_ids.tolist()[0]
											self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Dict        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      int        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIn('input_ids'	,_UpperCAmelCase		)
											self.assertIn('attention_mask'	,_UpperCAmelCase		)
											self.assertNotIn('labels'	,_UpperCAmelCase		)
											self.assertNotIn('decoder_attention_mask'	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							[
									    'Summary of the text.',
									    'Another summary.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(text_target=_UpperCAmelCase	,max_length=32	,padding='max_length'	,return_tensors='pt'		)
											self.assertEqual(32	,targets['input_ids'].shape[1]		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Any        =							tokenizer(
											    ['I am a small frog' * 1024, 'I am a small frog']	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)
											self.assertEqual(batch.input_ids.shape	,(2, 5122)		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      int        =							['A long paragraph for summarization.']
									_a     :      int        =							[
									    'Summary of the text.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[str]        =							tokenizer(text_target=_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[Any]        =							inputs['input_ids']
											_a     :      Tuple        =							targets['input_ids']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Optional[Any]        =							['Summary of the text.', 'Another summary.']
											_a     :      int        =							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase		)
											_a     :      int        =							[[0] * len(_UpperCAmelCase		) for x in encoded_output['input_ids']]
											_a     :      List[Any]        =							tokenizer.pad(_UpperCAmelCase		)
											self.assertSequenceEqual(outputs['global_attention_mask']	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									pass







							def 							__lowercase			(							self					:       int		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Dict        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      int        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Optional[Any]        =							'A, <mask> AllenNLP sentence.'
													_a     :      int        =							tokenizer_r.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													_a     :      List[Any]        =							tokenizer_p.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													self.assertEqual(sum(tokens_r['token_type_ids']		)	,sum(tokens_p['token_type_ids']		)		)
													self.assertEqual(
													    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)	,sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)	,)

													_a     :      Any        =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
													_a     :      Optional[Any]        =							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)
													self.assertSequenceEqual(tokens_p['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

													self.assertSequenceEqual(
													    _UpperCAmelCase	,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
													self.assertSequenceEqual(
													    _UpperCAmelCase	,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)









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





