
'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE=256    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=16    ,__SCREAMING_SNAKE_CASE=16    ,__SCREAMING_SNAKE_CASE=4    ,__SCREAMING_SNAKE_CASE="gelu"    ,__SCREAMING_SNAKE_CASE=1e-6    ,**__SCREAMING_SNAKE_CASE    ,):
    super().__init__(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		hidden_size
    SCREAMING_SNAKE_CASE  :  List[Any]								=		image_size
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		patch_size
    SCREAMING_SNAKE_CASE  :  Any								=		image_size // patch_size
    SCREAMING_SNAKE_CASE  :  int								=		mask_input_channels
    SCREAMING_SNAKE_CASE  :  Tuple								=		num_point_embeddings
    SCREAMING_SNAKE_CASE  :  List[str]								=		hidden_act
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		layer_norm_eps



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE=256    ,__SCREAMING_SNAKE_CASE="relu"    ,__SCREAMING_SNAKE_CASE=2048    ,__SCREAMING_SNAKE_CASE=2    ,__SCREAMING_SNAKE_CASE=8    ,__SCREAMING_SNAKE_CASE=2    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=256    ,__SCREAMING_SNAKE_CASE=1e-6    ,**__SCREAMING_SNAKE_CASE    ,):
    super().__init__(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		hidden_size
    SCREAMING_SNAKE_CASE  :  List[str]								=		hidden_act
    SCREAMING_SNAKE_CASE  :  str								=		mlp_dim
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		num_hidden_layers
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_attention_heads
    SCREAMING_SNAKE_CASE  :  Dict								=		attention_downsample_rate
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		num_multimask_outputs
    SCREAMING_SNAKE_CASE  :  List[str]								=		iou_head_depth
    SCREAMING_SNAKE_CASE  :  int								=		iou_head_hidden_dim
    SCREAMING_SNAKE_CASE  :  List[Any]								=		layer_norm_eps



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=256    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=16    ,__SCREAMING_SNAKE_CASE="gelu"    ,__SCREAMING_SNAKE_CASE=1e-06    ,__SCREAMING_SNAKE_CASE=0.0    ,__SCREAMING_SNAKE_CASE=1e-10    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=4.0    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=14    ,__SCREAMING_SNAKE_CASE=[2, 5, 8, 11]    ,__SCREAMING_SNAKE_CASE=128    ,__SCREAMING_SNAKE_CASE=None    ,**__SCREAMING_SNAKE_CASE    ,):
    super().__init__(**__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Dict								=		hidden_size
    SCREAMING_SNAKE_CASE  :  str								=		output_channels
    SCREAMING_SNAKE_CASE  :  int								=		num_hidden_layers
    SCREAMING_SNAKE_CASE  :  Dict								=		num_attention_heads
    SCREAMING_SNAKE_CASE  :  List[Any]								=		num_channels
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_size
    SCREAMING_SNAKE_CASE  :  Dict								=		patch_size
    SCREAMING_SNAKE_CASE  :  int								=		hidden_act
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		layer_norm_eps
    SCREAMING_SNAKE_CASE  :  Any								=		attention_dropout
    SCREAMING_SNAKE_CASE  :  Dict								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		qkv_bias
    SCREAMING_SNAKE_CASE  :  List[str]								=		mlp_ratio
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		use_abs_pos
    SCREAMING_SNAKE_CASE  :  int								=		use_rel_pos
    SCREAMING_SNAKE_CASE  :  List[str]								=		window_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		global_attn_indexes
    SCREAMING_SNAKE_CASE  :  str								=		num_pos_feats
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		int(hidden_size * mlp_ratio      ) if mlp_dim is None else mlp_dim



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'sam'
   A		= True






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=0.02    ,**__SCREAMING_SNAKE_CASE    ,):
    super().__init__(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		vision_config if vision_config is not None else {}
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prompt_encoder_config if prompt_encoder_config is not None else {}
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		mask_decoder_config if mask_decoder_config is not None else {}

    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  List[Any]								=		vision_config.to_dict()
    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		prompt_encoder_config.to_dict()
    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  List[Any]								=		mask_decoder_config.to_dict()

    SCREAMING_SNAKE_CASE  :  Any								=		SamVisionConfig(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		SamPromptEncoderConfig(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		SamMaskDecoderConfig(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		initializer_range






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		copy.deepcopy(self.__dict__      )
    SCREAMING_SNAKE_CASE  :  Any								=		self.vision_config.to_dict()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.prompt_encoder_config.to_dict()
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.mask_decoder_config.to_dict()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.__class__.model_type
    return output




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






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

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,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]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

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


