"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )




@require_vision
class  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					tempfile.mkdtemp()

            snake_case_					       =					BlipImageProcessor()
            snake_case_					       =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
            snake_case_					       =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

            snake_case_					       =					InstructBlipProcessor(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)

            processor.save_pretrained(self.tmpdirname)







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).tokenizer







       def 		a_						( self,							**lowerCAmelCase__)      ->      Dict:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).image_processor







       def 		a_						( self,							**lowerCAmelCase__)      ->      Optional[Any]:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).qformer_tokenizer







       def 		a_						( self)      ->      Dict:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      str:
            snake_case_					       =					[np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta)]

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

            return image_inputs







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					InstructBlipProcessor(
                tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor(),							qformer_tokenizer=self.get_qformer_tokenizer(),							)
            processor.save_pretrained(self.tmpdirname)

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

            snake_case_					       =					InstructBlipProcessor.from_pretrained(
                self.tmpdirname,							bos_token='(BOS)',							eos_token='(EOS)',							do_normalize=lowerCAmelCase__,							padding_value=1.0)

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

            self.assertEqual(processor.image_processor.to_json_string(),							image_processor_add_kwargs.to_json_string())
            self.assertIsInstance(processor.image_processor,							lowerCAmelCase__)
            self.assertIsInstance(processor.qformer_tokenizer,							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					image_processor(lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					processor(images=lowerCAmelCase__,							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 		a_						( self)      ->      str:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					'lower newer'

            snake_case_					       =					processor(text=lowerCAmelCase__)

            snake_case_					       =					tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)
            snake_case_					       =					qformer_tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)

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

            for key in encoded_tokens_qformer.keys():
                 self.assertListEqual(encoded_tokens_qformer[key],							encoded_processor['qformer_' + key])







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

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

            # test if it raises when no input is passed
            with pytest.raises(lowerCAmelCase__):
                 processor()







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor.batch_decode(lowerCAmelCase__)
            snake_case_					       =					tokenizer.batch_decode(lowerCAmelCase__)

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

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

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

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









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		:       Optional[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
}

a		:       Optional[Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'maskformer'
      a  : Dict							       =	{'hidden_size': 'mask_feature_size'}
      a  : Optional[Any]							       =	['resnet', 'swin']
      a  : List[Any]							       =	['detr']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int = 256		,      __lowercase					:			int = 256		,      __lowercase					:			float = 0.1		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			float = 0.02		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 20.0		,      __lowercase					:			Optional[bool] = None		,      **__lowercase					:			Tuple		,      )							->      str:
        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase							:	List[str]           =  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(__lowercase		,      __lowercase					):
          __UpperCAmelCase							:	Dict           =  backbone_config.pop("""model_type"""					)
          __UpperCAmelCase							:	Tuple           =  CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase							:	List[str]           =  config_class.from_dict(__lowercase					)

        # 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							:	List[Any]           =  DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase							:	List[Any]           =  (
              decoder_config.pop("""model_type"""					) if isinstance(__lowercase		,      __lowercase					) 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(__lowercase		,      __lowercase					):
            __UpperCAmelCase							:	Dict           =  CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase							:	Union[str, Any]           =  config_class.from_dict(__lowercase					)

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

        __UpperCAmelCase							:	Optional[Any]           =  self.decoder_config.encoder_attention_heads
        __UpperCAmelCase							:	Dict           =  self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase					)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			int		,      __lowercase					:			PretrainedConfig		,      __lowercase					:			PretrainedConfig		,      **__lowercase					:			str					)							->      Tuple:
        return cls(
            backbone_config=__lowercase		,      decoder_config=__lowercase		,      **__lowercase		,      )




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


