import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowerCAmelCase      :				Optional[Any]     			=get_logger(__name__)

# Datasets
lowerCAmelCase      :				Tuple     			="https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
lowerCAmelCase      :				Any     			="https://cdn-datasets.huggingface.co/datasets/datasets"
lowerCAmelCase      :				Any     			="https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
lowerCAmelCase      :				Optional[int]     			="https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
lowerCAmelCase      :				int     			="https://cdn-datasets.huggingface.co/datasets/metric"
lowerCAmelCase      :				str     			="https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
lowerCAmelCase      :				Any     			=os.environ.get("HF_ENDPOINT", "https://huggingface.co")
lowerCAmelCase      :				Dict     			=HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
lowerCAmelCase      :				Union[str, Any]     			="main"

lowerCAmelCase      :				List[Any]     			=version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowerCAmelCase      :				Dict     			={"1", "ON", "YES", "TRUE"}
lowerCAmelCase      :				Any     			=ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
lowerCAmelCase      :				int     			=version.parse(importlib_metadata.version("dill"))
lowerCAmelCase      :				Dict     			=version.parse(importlib_metadata.version("pandas"))
lowerCAmelCase      :				List[str]     			=version.parse(importlib_metadata.version("pyarrow"))

lowerCAmelCase      :				Optional[int]     			=os.environ.get("USE_TF", "AUTO").upper()
lowerCAmelCase      :				List[Any]     			=os.environ.get("USE_TORCH", "AUTO").upper()
lowerCAmelCase      :				Dict     			=os.environ.get("USE_JAX", "AUTO").upper()

lowerCAmelCase      :				List[Any]     			="N/A"
lowerCAmelCase      :				Dict     			=False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    lowerCAmelCase      :				Dict     			=importlib.util.find_spec("torch") is not None
    if TORCH_AVAILABLE:
        try:
            lowerCAmelCase      :				Any     			=version.parse(importlib_metadata.version("torch"))
            logger.info(F"""PyTorch version {TORCH_VERSION} available.""")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("Disabling PyTorch because USE_TF is set")

lowerCAmelCase      :				Tuple     			="N/A"
lowerCAmelCase      :				Tuple     			=False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    lowerCAmelCase      :				List[Any]     			=importlib.util.find_spec("tensorflow") is not None
    if TF_AVAILABLE:
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for package in [
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "tensorflow-rocm",
            "tensorflow-macos",
        ]:
            try:
                lowerCAmelCase      :				Optional[Any]     			=version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            lowerCAmelCase      :				Tuple     			=False
    if TF_AVAILABLE:
        if TF_VERSION.major < 2:
            logger.info(F"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
            lowerCAmelCase      :				Tuple     			=False
        else:
            logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
    logger.info("Disabling Tensorflow because USE_TORCH is set")


lowerCAmelCase      :				Union[str, Any]     			="N/A"
lowerCAmelCase      :				Union[str, Any]     			=False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    lowerCAmelCase      :				Optional[Any]     			=importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
    if JAX_AVAILABLE:
        try:
            lowerCAmelCase      :				Optional[Any]     			=version.parse(importlib_metadata.version("jax"))
            logger.info(F"""JAX version {JAX_VERSION} available.""")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("Disabling JAX because USE_JAX is set to False")


lowerCAmelCase      :				Any     			=os.environ.get("USE_BEAM", "AUTO").upper()
lowerCAmelCase      :				Optional[int]     			="N/A"
lowerCAmelCase      :				Tuple     			=False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        lowerCAmelCase      :				List[str]     			=version.parse(importlib_metadata.version("apache_beam"))
        lowerCAmelCase      :				Any     			=True
        logger.info(F"""Apache Beam version {BEAM_VERSION} available.""")
    except importlib_metadata.PackageNotFoundError:
        pass
else:
    logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
lowerCAmelCase      :				str     			=importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
lowerCAmelCase      :				Union[str, Any]     			=importlib.util.find_spec("PIL") is not None
lowerCAmelCase      :				List[str]     			=importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
lowerCAmelCase      :				Union[str, Any]     			=importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
lowerCAmelCase      :				Any     			=importlib.util.find_spec("rarfile") is not None
lowerCAmelCase      :				Tuple     			=importlib.util.find_spec("zstandard") is not None
lowerCAmelCase      :				List[Any]     			=importlib.util.find_spec("lz4") is not None
lowerCAmelCase      :				Tuple     			=importlib.util.find_spec("py7zr") is not None


# Cache location
lowerCAmelCase      :				List[Any]     			="~/.cache"
lowerCAmelCase      :				Optional[int]     			=os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
lowerCAmelCase      :				Dict     			=os.path.join(XDG_CACHE_HOME, "huggingface")
lowerCAmelCase      :				Union[str, Any]     			=os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

lowerCAmelCase      :				Dict     			=os.path.join(HF_CACHE_HOME, "datasets")
lowerCAmelCase      :				Dict     			=Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

lowerCAmelCase      :				str     			=os.path.join(HF_CACHE_HOME, "metrics")
lowerCAmelCase      :				Union[str, Any]     			=Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

lowerCAmelCase      :				List[Any]     			=os.path.join(HF_CACHE_HOME, "modules")
lowerCAmelCase      :				int     			=Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

lowerCAmelCase      :				List[Any]     			="downloads"
lowerCAmelCase      :				List[Any]     			=os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowerCAmelCase      :				List[str]     			=Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

lowerCAmelCase      :				str     			="extracted"
lowerCAmelCase      :				Optional[Any]     			=os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowerCAmelCase      :				int     			=Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowerCAmelCase      :				Optional[Any]     			=(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowerCAmelCase      :				str     			=1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowerCAmelCase      :				int     			=10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowerCAmelCase      :				str     			=4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowerCAmelCase      :				str     			="500MB"

# Parquet configuration
lowerCAmelCase      :				Dict     			=100
lowerCAmelCase      :				List[str]     			=100
lowerCAmelCase      :				List[Any]     			=100

# Offline mode
lowerCAmelCase      :				Dict     			=os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowerCAmelCase      :				int     			=0  # Disabled
lowerCAmelCase      :				int     			=float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowerCAmelCase      :				int     			="dataset.arrow"
lowerCAmelCase      :				List[str]     			="indices.arrow"
lowerCAmelCase      :				Optional[int]     			="state.json"
lowerCAmelCase      :				Any     			="dataset_info.json"
lowerCAmelCase      :				Tuple     			="dataset_infos.json"
lowerCAmelCase      :				Tuple     			="LICENSE"
lowerCAmelCase      :				Optional[Any]     			="metric_info.json"
lowerCAmelCase      :				str     			="dataset_dict.json"

lowerCAmelCase      :				List[Any]     			="datasets_modules"

lowerCAmelCase      :				str     			=255

# Streaming
lowerCAmelCase      :				Optional[Any]     			=20
lowerCAmelCase      :				str     			=5

# Datasets without script
lowerCAmelCase      :				Tuple     			=200
lowerCAmelCase      :				str     			=10
lowerCAmelCase      :				Union[str, Any]     			=200

# Progress bars
lowerCAmelCase      :				List[Any]     			=0.05  # 20 progress updates per sec





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




