



"""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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Optional[Any]								=			tempfile.mkdtemp()

        lowerCAmelCase_       :    int								=			BlipImageProcessor()
        lowerCAmelCase_       :    List[str]								=			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'	)

        lowerCAmelCase_       :    Tuple								=			BlipProcessor(_A   ,      _A	)

        processor.save_pretrained(self.tmpdirname	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      **SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        return AutoProcessor.from_pretrained(self.tmpdirname   ,      **_A	).tokenizer



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]	):
        return AutoProcessor.from_pretrained(self.tmpdirname   ,      **_A	).image_processor



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        shutil.rmtree(self.tmpdirname	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[Any]								=			[np.random.randint(2_5_5   ,      size=(3, 3_0, 4_0_0)   ,      dtype=np.uinta	)]

        lowerCAmelCase_       :    str								=			[Image.fromarray(np.moveaxis(_A   ,      0   ,      -1	)	) for x in image_inputs]

        return image_inputs



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    List[str]								=			BlipProcessor(tokenizer=self.get_tokenizer()   ,      image_processor=self.get_image_processor()	)
        processor.save_pretrained(self.tmpdirname	)

        lowerCAmelCase_       :    str								=			self.get_tokenizer(bos_token='(BOS)'   ,      eos_token='(EOS)'	)
        lowerCAmelCase_       :    Optional[Any]								=			self.get_image_processor(do_normalize=_A   ,      padding_value=1.0	)

        lowerCAmelCase_       :    int								=			BlipProcessor.from_pretrained(
            self.tmpdirname   ,      bos_token='(BOS)'   ,      eos_token='(EOS)'   ,      do_normalize=_A   ,      padding_value=1.0	)

        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 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.get_image_processor()
        lowerCAmelCase_       :    Tuple								=			self.get_tokenizer()

        lowerCAmelCase_       :    Optional[int]								=			BlipProcessor(tokenizer=_A   ,      image_processor=_A	)

        lowerCAmelCase_       :    Tuple								=			self.prepare_image_inputs()

        lowerCAmelCase_       :    Dict								=			image_processor(_A   ,      return_tensors='np'	)
        lowerCAmelCase_       :    str								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.get_image_processor()
        lowerCAmelCase_       :    Union[str, Any]								=			self.get_tokenizer()

        lowerCAmelCase_       :    int								=			BlipProcessor(tokenizer=_A   ,      image_processor=_A	)

        lowerCAmelCase_       :    int								=			'lower newer'

        lowerCAmelCase_       :    List[Any]								=			processor(text=_A	)

        lowerCAmelCase_       :    Optional[int]								=			tokenizer(_A   ,      return_token_type_ids=_A	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Dict								=			self.get_image_processor()
        lowerCAmelCase_       :    List[Any]								=			self.get_tokenizer()

        lowerCAmelCase_       :    str								=			BlipProcessor(tokenizer=_A   ,      image_processor=_A	)

        lowerCAmelCase_       :    List[str]								=			'lower newer'
        lowerCAmelCase_       :    Any								=			self.prepare_image_inputs()

        lowerCAmelCase_       :    Any								=			processor(text=_A   ,      images=_A	)

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

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.get_image_processor()
        lowerCAmelCase_       :    int								=			self.get_tokenizer()

        lowerCAmelCase_       :    Union[str, Any]								=			BlipProcessor(tokenizer=_A   ,      image_processor=_A	)

        lowerCAmelCase_       :    Optional[Any]								=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowerCAmelCase_       :    int								=			processor.batch_decode(_A	)
        lowerCAmelCase_       :    List[Any]								=			tokenizer.batch_decode(_A	)

        self.assertListEqual(_A   ,      _A	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			self.get_image_processor()
        lowerCAmelCase_       :    List[str]								=			self.get_tokenizer()

        lowerCAmelCase_       :    Optional[Any]								=			BlipProcessor(tokenizer=_A   ,      image_processor=_A	)

        lowerCAmelCase_       :    Optional[int]								=			'lower newer'
        lowerCAmelCase_       :    Union[str, Any]								=			self.prepare_image_inputs()

        lowerCAmelCase_       :    int								=			processor(text=_A   ,      images=_A	)

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys()	)   ,      ['pixel_values', 'input_ids', 'attention_mask']	)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_, lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""maskformer-swin"""

      _SCREAMING_SNAKE_CASE      =					{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }



      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_2_4   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4   ,      SCREAMING_SNAKE_CASE_    :				int=3   ,      SCREAMING_SNAKE_CASE_    :				Dict=9_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=[2, 2, 6, 2]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=[3, 6, 1_2, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=7   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Dict=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				int=1E-5   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			patch_size
        lowerCAmelCase_       :    Optional[int]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			embed_dim
        lowerCAmelCase_       :    Dict								=			depths
        lowerCAmelCase_       :    Optional[Any]								=			len(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			num_heads
        lowerCAmelCase_       :    List[str]								=			window_size
        lowerCAmelCase_       :    Any								=			mlp_ratio
        lowerCAmelCase_       :    Any								=			qkv_bias
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Tuple								=			drop_path_rate
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    Any								=			use_absolute_embeddings
        lowerCAmelCase_       :    Optional[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			initializer_range
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowerCAmelCase_       :    List[str]								=			int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_	) - 1)	)
        lowerCAmelCase_       :    List[Any]								=			['stem'] + [F"stage{idx}" for idx in range(1   ,      len(SCREAMING_SNAKE_CASE_	) + 1	)]
        lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			get_aligned_output_features_output_indices(
            out_features=SCREAMING_SNAKE_CASE_   ,      out_indices=SCREAMING_SNAKE_CASE_   ,      stage_names=self.stage_names	)





