import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Dict     =    BlenderbotSmallTokenizer
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        super().setUp()

        lowercase_					:       List[Any]     				=					['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
        lowercase_					:       Tuple     				=					dict(zip(_a       ,				range(len(_a				)				)				)				)

        lowercase_					:       Union[str, Any]     				=					['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
        lowercase_					:       Dict     				=					{'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

        lowercase_					:       int     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        lowercase_					:       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(_a				) + '\n'				)
        with open(self.merges_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             fp.write('\n'.join(_a				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     List[Any]:
        kwargs.update(self.special_tokens_map				)
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        lowercase_					:       str     				=					'adapt act apte'
        lowercase_					:       Optional[Any]     				=					'adapt act apte'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					BlenderbotSmallTokenizer(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map				)
        lowercase_					:       Any     				=					'adapt act apte'
        lowercase_					:       Union[str, Any]     				=					['adapt', 'act', 'ap@@', 'te']
        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize(_a				)
        self.assertListEqual(_a       ,				_a				)

        lowercase_					:       List[Any]     				=					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase_					:       Tuple     				=					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       str     				=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)
        assert tok('sam'				).input_ids == [1_384]
        lowercase_					:       Any     				=					'I am a small frog.'
        lowercase_					:       Optional[Any]     				=					tok([src_text]       ,				padding=_a       ,				truncation=_a				)['input_ids']
        lowercase_					:       Dict     				=					tok.batch_decode(_a       ,				skip_special_tokens=_a       ,				clean_up_tokenization_spaces=_a				)[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."





   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Dict     				=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)
        lowercase_					:       Dict     				=					'I am a small frog .'
        lowercase_					:       Dict     				=					'.'
        lowercase_					:       List[str]     				=					tok(_a				)['input_ids']
        lowercase_					:       Optional[Any]     				=					tok(_a				)['input_ids']

        assert encoded[-1] == encoded_dot[0]





'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_A								=   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_A								=   logging.get_logger(__name__)


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    '''maskformer'''
   A							:       List[str]     =    {'''hidden_size''': '''mask_feature_size'''}
   A							:       Dict     =    ['''resnet''', '''swin''']
   A							:       Optional[Any]     =    ['''detr''']
   def __init__(self       ,				_a = 256       ,				_a = 256       ,				_a = 0.1       ,				_a = False       ,				_a = None       ,				_a = None       ,				_a = 0.02       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 20.0       ,				_a = None       ,				**_a       ,				)    ->     int:
        if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             lowercase_					:       List[Any]     				=					SwinConfig(
                 image_size=384       ,				in_channels=3       ,				patch_size=4       ,				embed_dim=128       ,				depths=[2, 2, 18, 2]       ,				num_heads=[4, 8, 16, 32]       ,				window_size=12       ,				drop_path_rate=0.3       ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,				)

        if isinstance(_a       ,				_a				):
             lowercase_					:       Dict     				=					backbone_config.pop('model_type'				)
             lowercase_					:       Tuple     				=					CONFIG_MAPPING[backbone_model_type]
             lowercase_					:       Tuple     				=					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 MaskFormer. '''
                 f'''Supported model types: {",".join(self.backbones_supported				)}'''				)

        if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             lowercase_					:       str     				=					DetrConfig()
        else:
             # verify that the decoder is supported
             lowercase_					:       List[Any]     				=					(
                 decoder_config.pop('model_type'				) if isinstance(_a       ,				_a				) else decoder_config.model_type
             )
             if decoder_type not in self.decoders_supported:
                  raise ValueError(
                      f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                      f''' {",".join(self.decoders_supported				)}'''				)
             if isinstance(_a       ,				_a				):
                  lowercase_					:       List[Any]     				=					CONFIG_MAPPING[decoder_type]
                  lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        lowercase_					:       Optional[Any]     				=					backbone_config
        lowercase_					:       int     				=					decoder_config
        # main feature dimension for the model
        lowercase_					:       List[Any]     				=					fpn_feature_size
        lowercase_					:       Tuple     				=					mask_feature_size
        # initializer
        lowercase_					:       Optional[int]     				=					init_std
        lowercase_					:       List[str]     				=					init_xavier_std
        # Hungarian matcher && loss
        lowercase_					:       int     				=					cross_entropy_weight
        lowercase_					:       Optional[int]     				=					dice_weight
        lowercase_					:       Dict     				=					mask_weight
        lowercase_					:       Tuple     				=					use_auxiliary_loss
        lowercase_					:       int     				=					no_object_weight
        lowercase_					:       Union[str, Any]     				=					output_auxiliary_logits

        lowercase_					:       List[Any]     				=					self.decoder_config.encoder_attention_heads
        lowercase_					:       Tuple     				=					self.decoder_config.num_hidden_layers
        super().__init__(**_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     Union[str, Any]:
        return cls(
            backbone_config=_a       ,				decoder_config=_a       ,				**_a       ,				)





   def 		_lowerCamelCase (self				)    ->     Dict[str, any]:
        lowercase_					:       Optional[int]     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Union[str, Any]     				=					self.backbone_config.to_dict()
        lowercase_					:       Optional[Any]     				=					self.decoder_config.to_dict()
        lowercase_					:       Dict     				=					self.__class__.model_type
        return output




