




'''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


UpperCAmelCase_       :   Union[str, Any]   =       {
    '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
}

UpperCAmelCase_       :   List[str]   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'maskformer'
    lowerCAmelCase_				    =		{'hidden_size': 'mask_feature_size'}
    lowerCAmelCase_				    =		['resnet', 'swin']
    lowerCAmelCase_				    =		['detr']



    def __init__(	self      :       str,__A      :       int = 2_5_6,__A      :       int = 2_5_6,__A      :       float = 0.1,__A      :       bool = False,__A      :       Optional[Dict] = None,__A      :       Optional[Dict] = None,__A      :       float = 0.02,__A      :       float = 1.0,__A      :       float = 1.0,__A      :       float = 1.0,__A      :       float = 20.0,__A      :       Optional[bool] = None,**__A      :       List[str],):
          if backbone_config is None:
                # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                _lowerCamelCase				:   int								=   SwinConfig(
                    image_size=3_8_4,in_channels=3,patch_size=4,embed_dim=1_2_8,depths=[2, 2, 1_8, 2],num_heads=[4, 8, 1_6, 3_2],window_size=1_2,drop_path_rate=0.3,out_features=["stage1", "stage2", "stage3", "stage4"],)

          if isinstance(__A,__A     ):
                _lowerCamelCase				:   Optional[Any]								=   backbone_config.pop("model_type"     )
                _lowerCamelCase				:   str								=   CONFIG_MAPPING[backbone_model_type]
                _lowerCamelCase				:   Union[str, 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 MaskFormer. '
                    f'Supported model types: {",".join(self.backbones_supported     )}'     )

          if decoder_config is None:
                # fall back to https://huggingface.co/facebook/detr-resnet-50
                _lowerCamelCase				:   Optional[Any]								=   DetrConfig()
          else:
                # verify that the decoder is supported
                _lowerCamelCase				:   Dict								=   (
                    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     ):
                      _lowerCamelCase				:   str								=   CONFIG_MAPPING[decoder_type]
                      _lowerCamelCase				:   List[str]								=   config_class.from_dict(__A     )

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

          _lowerCamelCase				:   int								=   self.decoder_config.encoder_attention_heads
          _lowerCamelCase				:   Optional[Any]								=   self.decoder_config.num_hidden_layers
          super().__init__(**__A     )



    @classmethod
    def   lowerCamelCase_  (	cls      :       Any,__A      :       PretrainedConfig,__A      :       PretrainedConfig,**__A      :       Optional[int]     ):
          return cls(
              backbone_config=__A,decoder_config=__A,**__A,)



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Any								=   copy.deepcopy(self.__dict__     )
          _lowerCamelCase				:   Dict								=   self.backbone_config.to_dict()
          _lowerCamelCase				:   Union[str, Any]								=   self.decoder_config.to_dict()
          _lowerCamelCase				:   Tuple								=   self.__class__.model_type
          return output

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2     )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )