






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


snake_case__      :	str       	=      {
    """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
}

snake_case__      :	Optional[Any]       	=      logging.get_logger(__name__)


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """maskformer"""
  A_						=   {"""hidden_size""": """mask_feature_size"""}
  A_						=   ["""resnet""", """swin"""]
  A_						=   ["""detr"""]




  def __init__(     self		,		_UpperCAmelCase = 256		,		_UpperCAmelCase = 256		,		_UpperCAmelCase = 0.1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 0.0_2		,		_UpperCAmelCase = 1.0		,		_UpperCAmelCase = 1.0		,		_UpperCAmelCase = 1.0		,		_UpperCAmelCase = 2_0.0		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)			-> Union[str, Any]:
     if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        UpperCamelCase_									=     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(_UpperCAmelCase		,		_UpperCAmelCase ):
        UpperCamelCase_									=     backbone_config.pop('model_type' )
        UpperCamelCase_									=     CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase_									=     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 MaskFormer. """
            f"""Supported model types: {",".join(self.backbones_supported )}""" )

     if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        UpperCamelCase_									=     DetrConfig()
     else:
        # verify that the decoder is supported
        UpperCamelCase_									=     (
            decoder_config.pop('model_type' ) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) 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(_UpperCAmelCase		,		_UpperCAmelCase ):
           UpperCamelCase_									=     CONFIG_MAPPING[decoder_type]
           UpperCamelCase_									=     config_class.from_dict(_UpperCAmelCase )

     UpperCamelCase_									=     backbone_config
     UpperCamelCase_									=     decoder_config
     # main feature dimension for the model
     UpperCamelCase_									=     fpn_feature_size
     UpperCamelCase_									=     mask_feature_size
     # initializer
     UpperCamelCase_									=     init_std
     UpperCamelCase_									=     init_xavier_std
     # Hungarian matcher && loss
     UpperCamelCase_									=     cross_entropy_weight
     UpperCamelCase_									=     dice_weight
     UpperCamelCase_									=     mask_weight
     UpperCamelCase_									=     use_auxiliary_loss
     UpperCamelCase_									=     no_object_weight
     UpperCamelCase_									=     output_auxiliary_logits

     UpperCamelCase_									=     self.decoder_config.encoder_attention_heads
     UpperCamelCase_									=     self.decoder_config.num_hidden_layers
     super().__init__(**_UpperCAmelCase )




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> Optional[Any]:
     return cls(
         backbone_config=_UpperCAmelCase		,		decoder_config=_UpperCAmelCase		,		**_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict[str, any]:
     UpperCamelCase_									=     copy.deepcopy(self.__dict__ )
     UpperCamelCase_									=     self.backbone_config.to_dict()
     UpperCamelCase_									=     self.decoder_config.to_dict()
     UpperCamelCase_									=     self.__class__.model_type
     return output













import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     tempfile.mkdtemp()

     # fmt: off
     UpperCamelCase_									=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     UpperCamelCase_									=     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] ) )

     UpperCamelCase_									=     {
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		_UpperCAmelCase )
     with open(self.image_processor_file		,		'w'		,		encoding='utf-8' ) as fp:
        json.dump(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		**_UpperCAmelCase )			-> Optional[int]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     shutil.rmtree(self.tmpdirname )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     [np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta )]

     UpperCamelCase_									=     [Image.fromarray(np.moveaxis(_UpperCAmelCase		,		0		,		-1 ) ) for x in image_inputs]

     return image_inputs




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_tokenizer()
     UpperCamelCase_									=     self.get_image_processor()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     processor.save_pretrained(self.tmpdirname )
     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     UpperCamelCase_									=     self.get_tokenizer(bos_token='(BOS)'		,		eos_token='(EOS)' )
     UpperCamelCase_									=     self.get_image_processor(do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     UpperCamelCase_									=     VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname		,		bos_token='(BOS)'		,		eos_token='(EOS)'		,		do_normalize=_UpperCAmelCase		,		padding_value=1.0 )

     self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer		,		(BertTokenizer, BertTokenizerFast) )

     self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     image_processor(_UpperCAmelCase		,		return_tensors='np' )
     UpperCamelCase_									=     processor(images=_UpperCAmelCase		,		return_tensors='np' )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1e-2 )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'

     UpperCamelCase_									=     processor(text=_UpperCAmelCase )

     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,		encoded_processor[key] )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )

     # test if it raises when no input is passed
     with self.assertRaises(_UpperCAmelCase ):
        processor()




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCamelCase_									=     processor.batch_decode(_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.batch_decode(_UpperCAmelCase )

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.get_image_processor()
     UpperCamelCase_									=     self.get_tokenizer()

     UpperCamelCase_									=     VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase		,		image_processor=_UpperCAmelCase )

     UpperCamelCase_									=     'lower newer'
     UpperCamelCase_									=     self.prepare_image_inputs()

     UpperCamelCase_									=     processor(text=_UpperCAmelCase		,		images=_UpperCAmelCase )

     self.assertListEqual(list(inputs.keys() )		,		processor.model_input_names )





