"""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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 					UpperCAmelCase_     (   unittest.TestCase):


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

             lowercase__      :		Dict          =     CLIPImageProcessor()
             lowercase__      :		str          =     BertTokenizer.from_pretrained(
                 'hf-internal-testing/tiny-random-BertModel'		,      model_input_names=['input_ids', 'attention_mask']							)

             lowercase__      :		List[Any]          =     GitProcessor(lowerCAmelCase__		,      lowerCAmelCase__							)

             processor.save_pretrained(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Optional[Any]:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							).tokenizer


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Any:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							).image_processor


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


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Tuple          =     [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

             lowercase__      :		Optional[int]          =     [Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

             return image_inputs


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		int          =     GitProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
             processor.save_pretrained(self.tmpdirname							)

             lowercase__      :		Dict          =     self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)'							)
             lowercase__      :		Optional[Any]          =     self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

             lowercase__      :		Tuple          =     GitProcessor.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__							)


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

             lowercase__      :		Union[str, Any]          =     GitProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

             lowercase__      :		Optional[int]          =     self.prepare_image_inputs()

             lowercase__      :		str          =     image_processor(lowerCAmelCase__		,      return_tensors='np'							)
             lowercase__      :		List[Any]          =     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 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		Tuple          =     self.get_image_processor()
             lowercase__      :		List[Any]          =     self.get_tokenizer()

             lowercase__      :		str          =     GitProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

             lowercase__      :		Any          =     'lower newer'

             lowercase__      :		str          =     processor(text=lowerCAmelCase__							)

             lowercase__      :		int          =     tokenizer(lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__							)

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


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		Any          =     self.get_image_processor()
             lowercase__      :		Tuple          =     self.get_tokenizer()

             lowercase__      :		List[str]          =     GitProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

             lowercase__      :		Tuple          =     'lower newer'
             lowercase__      :		Dict          =     self.prepare_image_inputs()

             lowercase__      :		str          =     processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

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

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


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

             lowercase__      :		Dict          =     GitProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

             lowercase__      :		str          =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             lowercase__      :		List[Any]          =     processor.batch_decode(lowerCAmelCase__							)
             lowercase__      :		Optional[int]          =     tokenizer.batch_decode(lowerCAmelCase__							)

             self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)


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

             lowercase__      :		Dict          =     GitProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

             lowercase__      :		Tuple          =     'lower newer'
             lowercase__      :		Any          =     self.prepare_image_inputs()

             lowercase__      :		Optional[int]          =     processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

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


"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


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

