



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     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Any											=						LEDTokenizer
       UpperCamelCase__  :    str											=						LEDTokenizerFast
       UpperCamelCase__  :    List[Any]											=						True



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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

           __a						       =				os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''vocab_file'''])
           __a						       =				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(__SCREAMING_SNAKE_CASE) + '''\n''')
           with open(self.merges_file     ,		'''w'''     ,		encoding='''utf-8''') as fp:
               fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           return "lower newer", "lower newer"



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return LEDTokenizer.from_pretrained('''allenai/led-base-16384''')



       @cached_property
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''')



       @require_torch
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           __a						       =				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		max_length=len(__SCREAMING_SNAKE_CASE)     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               self.assertEqual((2, 9)     ,		batch.input_ids.shape)
               self.assertEqual((2, 9)     ,		batch.attention_mask.shape)
               __a						       =				batch.input_ids.tolist()[0]
               self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       @require_torch
       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIn('''input_ids'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertIn('''attention_mask'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''labels'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''decoder_attention_mask'''     ,		__SCREAMING_SNAKE_CASE)



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		max_length=32     ,		padding='''max_length'''     ,		return_tensors='''pt''')
               self.assertEqual(32     ,		targets['''input_ids'''].shape[1])



       @require_torch
       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(
                   ['''I am a small frog''' * 1_024, '''I am a small frog''']     ,		padding=__SCREAMING_SNAKE_CASE     ,		truncation=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(batch.input_ids.shape     ,		(2, 5_122))



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''']
           __a						       =				[
               '''Summary of the text.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				inputs['''input_ids''']
               __a						       =				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 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				['''Summary of the text.''', '''Another summary.''']
               __a						       =				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE)
               __a						       =				[[0] * len(__SCREAMING_SNAKE_CASE) for x in encoded_output['''input_ids''']]
               __a						       =				tokenizer.pad(__SCREAMING_SNAKE_CASE)
               self.assertSequenceEqual(outputs['''global_attention_mask''']     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           pass





       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                   __a						       =				self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				'''A, <mask> AllenNLP sentence.'''
                   __a						       =				tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   __a						       =				tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   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						       =				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                   __a						       =				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])
                   self.assertSequenceEqual(tokens_p['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                   self.assertSequenceEqual(tokens_r['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

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


UpperCAmelCase_      : Optional[int]  					=       {
    '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_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  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=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

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

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

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


