
"""simple docstring"""





import copy
from typing import Dict, Optional

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


_a	 =							logging.get_logger(__name__)

_a	 =							{
    '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 A_			(lowercase__   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		int						  =	"""oneformer"""
  SCREAMING_SNAKE_CASE__    :		List[str]						  =	{"""hidden_size""": """hidden_dim"""}







  def __init__(	self       ,     lowercase_ = None       ,     lowercase_ = 255       ,     lowercase_ = 150       ,     lowercase_ = 0.1       ,     lowercase_ = 2.0       ,     lowercase_ = 5.0       ,     lowercase_ = 5.0       ,     lowercase_ = 0.5       ,     lowercase_ = 0.07       ,     lowercase_ = 1_2544       ,     lowercase_ = 3.0       ,     lowercase_ = 0.75       ,     lowercase_ = 0.02       ,     lowercase_ = 1.0       ,     lowercase_ = 1E-0_5       ,     lowercase_ = False       ,     lowercase_ = True       ,     lowercase_ = True       ,     lowercase_ = [4, 8, 16, 32]       ,     lowercase_ = 77       ,     lowercase_ = 256       ,     lowercase_ = 77       ,     lowercase_ = 6       ,     lowercase_ = 4_9408       ,     lowercase_ = 2       ,     lowercase_ = 16       ,     lowercase_ = 256       ,     lowercase_ = 256       ,     lowercase_ = 256       ,     lowercase_ = 1024       ,     lowercase_ = "GN"       ,     lowercase_ = 6       ,     lowercase_ = 10       ,     lowercase_ = True       ,     lowercase_ = 8       ,     lowercase_ = 0.1       ,     lowercase_ = 2048       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = 2       ,     lowercase_ = 4       ,     **lowercase_       ,     ):

     """simple docstring"""
     if backbone_config is None:
        logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."						)
        UpperCAmelCase_      :      List[Any]      =					CONFIG_MAPPING["swin"](
            image_size=224       ,     in_channels=3       ,     patch_size=4       ,     embed_dim=96       ,     depths=[2, 2, 6, 2]       ,     num_heads=[3, 6, 12, 24]       ,     window_size=7       ,     drop_path_rate=0.3       ,     use_absolute_embeddings=lowercase_       ,     out_features=["stage1", "stage2", "stage3", "stage4"]       ,     )
     elif isinstance(lowercase_       ,     lowercase_						):
        UpperCAmelCase_      :      Tuple      =					backbone_config.get("model_type"						)
        UpperCAmelCase_      :      Any      =					CONFIG_MAPPING[backbone_model_type]
        UpperCAmelCase_      :      int      =					config_class.from_dict(lowercase_						)

     UpperCAmelCase_      :      Optional[Any]      =					backbone_config

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

     super().__init__(**lowercase_						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Dict      =					copy.deepcopy(self.__dict__						)
     UpperCAmelCase_      :      Optional[Any]      =					self.backbone_config.to_dict()
     UpperCAmelCase_      :      Dict      =					self.__class__.model_type
     return output




"""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 A_			(unittest.TestCase   ):




  '''simple docstring'''







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					tempfile.mkdtemp()

     UpperCAmelCase_      :      List[Any]      =					CLIPImageProcessor()
     UpperCAmelCase_      :      Any      =					BertTokenizer.from_pretrained(
         "hf-internal-testing/tiny-random-BertModel"       ,     model_input_names=["input_ids", "attention_mask"]						)

     UpperCAmelCase_      :      Tuple      =					GitProcessor(lowercase_       ,     lowercase_						)

     processor.save_pretrained(self.tmpdirname						)







  def  UpperCamelCase__      (	self       ,     **lowercase_						):

     """simple docstring"""
     return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).tokenizer







  def  UpperCamelCase__      (	self       ,     **lowercase_						):

     """simple docstring"""
     return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).image_processor







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     shutil.rmtree(self.tmpdirname						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta						)]

     UpperCAmelCase_      :      Optional[Any]      =					[Image.fromarray(np.moveaxis(lowercase_       ,     0       ,     -1						)						) for x in image_inputs]

     return image_inputs







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      List[Any]      =					GitProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     UpperCAmelCase_      :      Optional[int]      =					self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"						)
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor(do_normalize=lowercase_       ,     padding_value=1.0						)

     UpperCAmelCase_      :      str      =					GitProcessor.from_pretrained(
         self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=lowercase_       ,     padding_value=1.0						)

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

     self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()						)
     self.assertIsInstance(processor.image_processor       ,     lowercase_						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Dict      =					self.get_image_processor()
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      Union[str, Any]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      List[Any]      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Optional[Any]      =					image_processor(lowercase_       ,     return_tensors="np"						)
     UpperCAmelCase_      :      int      =					processor(images=lowercase_       ,     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						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      Tuple      =					self.get_tokenizer()

     UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      Optional[Any]      =					"lower newer"

     UpperCAmelCase_      :      Any      =					processor(text=lowercase_						)

     UpperCAmelCase_      :      int      =					tokenizer(lowercase_       ,     return_token_type_ids=lowercase_						)

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







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					self.get_image_processor()
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      Optional[int]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      List[str]      =					"lower newer"
     UpperCAmelCase_      :      Dict      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Any      =					processor(text=lowercase_       ,     images=lowercase_						)

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

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







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      str      =					self.get_tokenizer()

     UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      Tuple      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase_      :      Any      =					processor.batch_decode(lowercase_						)
     UpperCAmelCase_      :      Optional[Any]      =					tokenizer.batch_decode(lowercase_						)

     self.assertListEqual(lowercase_       ,     lowercase_						)







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
     UpperCAmelCase_      :      Optional[Any]      =					self.get_tokenizer()

     UpperCAmelCase_      :      str      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)

     UpperCAmelCase_      :      int      =					"lower newer"
     UpperCAmelCase_      :      Tuple      =					self.prepare_image_inputs()

     UpperCAmelCase_      :      Optional[Any]      =					processor(text=lowercase_       ,     images=lowercase_						)

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


