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


lowerCAmelCase      :				Union[str, Any]     			=(30, 487)
lowerCAmelCase      :				Optional[Any]     			=(36, 1024)
lowerCAmelCase      :				Optional[Any]     			=(100, 100)
lowerCAmelCase      :				List[Any]     			=100

lowerCAmelCase      :				Union[str, Any]     			=datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

lowerCAmelCase			,			lowerCAmelCase      :				str     			=os.path.split(__file__)
lowerCAmelCase      :				Union[str, Any]     			=os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    with ArrowWriter(features=__A ,			path=os.path.join(__A ,			"""beta.arrow"""						)						) as writer:
        for key, record in dummy_data:
            _lowerCamelCase  :		Dict			    =    my_features.encode_example(__A						)
            writer.write(__A						)
        _lowerCamelCase    ,				_lowerCamelCase  :		Dict			    =    writer.finalize()







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[Any]			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    for _ in dataset:
        pass







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		int			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    dataset.set_format("""numpy"""						)
    for _ in dataset:
        pass







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    10
    _lowerCamelCase  :		Dict			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    for i in range(0 ,			len(__A						) ,			__A						):
        _lowerCamelCase  :		Optional[Any]			    =    dataset[i : i + batch_size]







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    10
    _lowerCamelCase  :		Dict			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    dataset.set_format("""numpy"""						)
    for i in range(0 ,			len(__A						) ,			__A						):
        _lowerCamelCase  :		List[str]			    =    dataset[i : i + batch_size]







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    for col in feats:
        _lowerCamelCase  :		Dict			    =    dataset[col]







@get_duration
def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		int			    =    datasets.Dataset.from_file(
        filename=os.path.join(__A ,			"""beta.arrow"""						) ,			info=datasets.DatasetInfo(features=__A						)						)
    dataset.set_format("""numpy"""						)
    for col in feats:
        _lowerCamelCase  :		List[Any]			    =    dataset[col]







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    {}
    _lowerCamelCase  :		Optional[int]			    =    (
        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:
        _lowerCamelCase  :		str			    =    datasets.Features({"""image""": ArrayaD(__A ,			dtype="""float32"""						)}						)
        _lowerCamelCase  :		Tuple			    =    generate_examples(features=__A ,			num_examples=__A						)
        _lowerCamelCase  :		Union[str, Any]			    =    write(__A ,			__A ,			__A						)
        for read_func in read_functions:
            _lowerCamelCase  :		Dict			    =    read_func(__A ,			__A						)

    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])}
        # )
        _lowerCamelCase  :		Union[str, Any]			    =    datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""						)						)						)}						)
        _lowerCamelCase  :		Union[str, Any]			    =    generate_examples(
            features=__A ,			num_examples=__A ,			seq_shapes={"""image""": SPEED_TEST_SHAPE}						)
        _lowerCamelCase  :		Union[str, Any]			    =    write(__A ,			__A ,			__A						)
        for read_func in read_functions:
            _lowerCamelCase  :		List[Any]			    =    read_func(__A ,			__A						)

    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])}
        # )
        _lowerCamelCase  :		List[str]			    =    datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""						)						)}						)
        _lowerCamelCase  :		Any			    =    generate_examples(
            features=__A ,			num_examples=__A ,			seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,			)
        _lowerCamelCase  :		Dict			    =    write(__A ,			__A ,			__A						)
        for read_func in read_functions:
            _lowerCamelCase  :		Optional[Any]			    =    read_func(__A ,			__A						)

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


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





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




