

"""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


__lowercase		:		List[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
}

__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					'''maskformer'''
    UpperCamelCase_	:					Optional[int]							=					{'''hidden_size''': '''mask_feature_size'''}
    UpperCamelCase_	:					List[str]							=					['''resnet''', '''swin''']
    UpperCamelCase_	:					Dict							=					['''detr''']
    def __init__(		self							:     Optional[Any]	,      A_							:     int = 256	,      A_							:     int = 256	,      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_							:     Optional[Any]	,      )    ->       List[str]:
         if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              __snake_case				=							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_    ):
              __snake_case				=							backbone_config.pop('''model_type'''    )
              __snake_case				=							CONFIG_MAPPING[backbone_model_type]
              __snake_case				=							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
              __snake_case				=							DetrConfig()
         else:
              # verify that the decoder is supported
              __snake_case				=							(
                  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_    ):
                   __snake_case				=							CONFIG_MAPPING[decoder_type]
                   __snake_case				=							config_class.from_dict(A_    )

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

         __snake_case				=							self.decoder_config.encoder_attention_heads
         __snake_case				=							self.decoder_config.num_hidden_layers
         super().__init__(**A_    )
    @classmethod
    def  lowercase					(		cls							:     Dict	,      A_							:     PretrainedConfig	,      A_							:     PretrainedConfig	,      **A_							:     Optional[Any]    )    ->       Union[str, Any]:
         return cls(
             backbone_config=A_	,      decoder_config=A_	,      **A_	,      )







    def  lowercase					(		self							:     Dict    )    ->       Dict[str, any]:
         __snake_case				=							copy.deepcopy(self.__dict__    )
         __snake_case				=							self.backbone_config.to_dict()
         __snake_case				=							self.decoder_config.to_dict()
         __snake_case				=							self.__class__.model_type
         return output
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         __snake_case				=							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]    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': {'''height''': 224, '''width''': 224},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 18, '''width''': 18},
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
             '''do_convert_rgb''': True,
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     str	,      **A_							:     str    )    ->       Dict:
         return BertTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     Optional[int]    )    ->       Tuple:
         return BertTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     Any    )    ->       Any:
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       int:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Dict    )    ->       Dict:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

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

         return image_inputs
    def  lowercase					(		self							:     Tuple    )    ->       Any:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         __snake_case				=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(cls_token='''(CLS)'''	,      sep_token='''(SEP)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_    )

         __snake_case				=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname	,      cls_token='''(CLS)'''	,      sep_token='''(SEP)'''	,      do_normalize=A_    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      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  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

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

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

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

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

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