

import copy
from typing import Dict, Optional

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


__a						:str            = logging.get_logger(__name__)

__a						:List[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      _a				( a__							):




      """simple docstring"""

      _lowerCamelCase					: Optional[Any]					=					'oneformer'
      _lowerCamelCase					: str					=					{'hidden_size': 'hidden_dim'}





      def __init__(      self       :							Tuple	, UpperCAmelCase       :							Optional[Any] = None	, UpperCAmelCase       :							Optional[Any] = 255	, UpperCAmelCase       :							List[str] = 150	, UpperCAmelCase       :							Dict = 0.1	, UpperCAmelCase       :							Union[str, Any] = 2.0	, UpperCAmelCase       :							Optional[int] = 5.0	, UpperCAmelCase       :							Tuple = 5.0	, UpperCAmelCase       :							Optional[int] = 0.5	, UpperCAmelCase       :							Dict = 0.07	, UpperCAmelCase       :							List[Any] = 12544	, UpperCAmelCase       :							List[str] = 3.0	, UpperCAmelCase       :							Optional[Any] = 0.75	, UpperCAmelCase       :							Union[str, Any] = 0.02	, UpperCAmelCase       :							Optional[int] = 1.0	, UpperCAmelCase       :							List[str] = 1E-05	, UpperCAmelCase       :							List[Any] = False	, UpperCAmelCase       :							Union[str, Any] = True	, UpperCAmelCase       :							Any = True	, UpperCAmelCase       :							List[str] = [4, 8, 16, 32]	, UpperCAmelCase       :							List[Any] = 77	, UpperCAmelCase       :							Optional[Any] = 256	, UpperCAmelCase       :							Optional[Any] = 77	, UpperCAmelCase       :							Optional[Any] = 6	, UpperCAmelCase       :							Union[str, Any] = 49408	, UpperCAmelCase       :							Optional[int] = 2	, UpperCAmelCase       :							List[Any] = 16	, UpperCAmelCase       :							Union[str, Any] = 256	, UpperCAmelCase       :							Any = 256	, UpperCAmelCase       :							Optional[Any] = 256	, UpperCAmelCase       :							Dict = 1024	, UpperCAmelCase       :							List[Any] = "GN"	, UpperCAmelCase       :							Tuple = 6	, UpperCAmelCase       :							Dict = 10	, UpperCAmelCase       :							Union[str, Any] = True	, UpperCAmelCase       :							str = 8	, UpperCAmelCase       :							Optional[Any] = 0.1	, UpperCAmelCase       :							Dict = 2048	, UpperCAmelCase       :							List[Any] = False	, UpperCAmelCase       :							int = False	, UpperCAmelCase       :							List[Any] = 2	, UpperCAmelCase       :							str = 4	, **UpperCAmelCase       :							int	, ):







            if backbone_config is None:
                  logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."  )
                  A_	     =							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=__A	, out_features=["stage1", "stage2", "stage3", "stage4"]	, )
            elif isinstance(__A	, __A  ):
                  A_	     =							backbone_config.get("model_type"  )
                  A_	     =							CONFIG_MAPPING[backbone_model_type]
                  A_	     =							config_class.from_dict(__A  )

            A_	     =							backbone_config

            A_	     =							ignore_value
            A_	     =							num_queries
            A_	     =							no_object_weight
            A_	     =							class_weight
            A_	     =							mask_weight
            A_	     =							dice_weight
            A_	     =							contrastive_weight
            A_	     =							contrastive_temperature
            A_	     =							train_num_points
            A_	     =							oversample_ratio
            A_	     =							importance_sample_ratio
            A_	     =							init_std
            A_	     =							init_xavier_std
            A_	     =							layer_norm_eps
            A_	     =							is_training
            A_	     =							use_auxiliary_loss
            A_	     =							output_auxiliary_logits
            A_	     =							strides
            A_	     =							task_seq_len
            A_	     =							text_encoder_width
            A_	     =							text_encoder_context_length
            A_	     =							text_encoder_num_layers
            A_	     =							text_encoder_vocab_size
            A_	     =							text_encoder_proj_layers
            A_	     =							text_encoder_n_ctx
            A_	     =							conv_dim
            A_	     =							mask_dim
            A_	     =							hidden_dim
            A_	     =							encoder_feedforward_dim
            A_	     =							norm
            A_	     =							encoder_layers
            A_	     =							decoder_layers
            A_	     =							use_task_norm
            A_	     =							num_attention_heads
            A_	     =							dropout
            A_	     =							dim_feedforward
            A_	     =							pre_norm
            A_	     =							enforce_input_proj
            A_	     =							query_dec_layers
            A_	     =							common_stride
            A_	     =							decoder_layers

            super().__init__(**__A  )







      def 		__A  (      self       :							Any  ):
            A_	     =							copy.deepcopy(self.__dict__  )
            A_	     =							self.backbone_config.to_dict()
            A_	     =							self.__class__.model_type
            return output








import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()