





import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


UpperCAmelCase_       : Union[str, Any]							=    get_logger(__name__)

# Datasets
UpperCAmelCase_       : int							=    """https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
UpperCAmelCase_       : int							=    """https://cdn-datasets.huggingface.co/datasets/datasets"""
UpperCAmelCase_       : Optional[int]							=    """https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

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

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

UpperCAmelCase_       : Tuple							=    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
UpperCAmelCase_       : Optional[int]							=    {"""1""", """ON""", """YES""", """TRUE"""}
UpperCAmelCase_       : Dict							=    ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
UpperCAmelCase_       : Dict							=    version.parse(importlib_metadata.version("""dill"""))
UpperCAmelCase_       : int							=    version.parse(importlib_metadata.version("""pandas"""))
UpperCAmelCase_       : Union[str, Any]							=    version.parse(importlib_metadata.version("""pyarrow"""))

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

UpperCAmelCase_       : List[str]							=    """N/A"""
UpperCAmelCase_       : Dict							=    False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      UpperCAmelCase_       : Dict							=    importlib.util.find_spec("""torch""") is not None
      if TORCH_AVAILABLE:
            try:
                  UpperCAmelCase_       : Optional[int]							=    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""")

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

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      UpperCAmelCase_       : 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:
                        UpperCAmelCase_       : List[Any]							=    version.parse(importlib_metadata.version(package))
                  except importlib_metadata.PackageNotFoundError:
                        continue
                  else:
                        break
            else:
                  UpperCAmelCase_       : 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.""")
                  UpperCAmelCase_       : Union[str, Any]							=    False
            else:
                  logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
      logger.info("""Disabling Tensorflow because USE_TORCH is set""")


UpperCAmelCase_       : Optional[int]							=    """N/A"""
UpperCAmelCase_       : List[str]							=    False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      UpperCAmelCase_       : Union[str, Any]							=    importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
      if JAX_AVAILABLE:
            try:
                  UpperCAmelCase_       : List[str]							=    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""")


UpperCAmelCase_       : Optional[int]							=    os.environ.get("""USE_BEAM""", """AUTO""").upper()
UpperCAmelCase_       : Any							=    """N/A"""
UpperCAmelCase_       : Optional[Any]							=    False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            UpperCAmelCase_       : Union[str, Any]							=    version.parse(importlib_metadata.version("""apache_beam"""))
            UpperCAmelCase_       : 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
UpperCAmelCase_       : Optional[Any]							=    importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
UpperCAmelCase_       : Dict							=    importlib.util.find_spec("""PIL""") is not None
UpperCAmelCase_       : List[str]							=    importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
UpperCAmelCase_       : Tuple							=    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
UpperCAmelCase_       : Optional[int]							=    importlib.util.find_spec("""rarfile""") is not None
UpperCAmelCase_       : List[Any]							=    importlib.util.find_spec("""zstandard""") is not None
UpperCAmelCase_       : Optional[Any]							=    importlib.util.find_spec("""lz4""") is not None
UpperCAmelCase_       : List[str]							=    importlib.util.find_spec("""py7zr""") is not None


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

UpperCAmelCase_       : str							=    os.path.join(HF_CACHE_HOME, """datasets""")
UpperCAmelCase_       : List[Any]							=    Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

UpperCAmelCase_       : Any							=    os.path.join(HF_CACHE_HOME, """metrics""")
UpperCAmelCase_       : Dict							=    Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

UpperCAmelCase_       : str							=    os.path.join(HF_CACHE_HOME, """modules""")
UpperCAmelCase_       : Any							=    Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

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

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

# Download count for the website
UpperCAmelCase_       : Union[str, 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)
UpperCAmelCase_       : Optional[Any]							=    10_00

# Size of the preloaded record batch in `Dataset.__iter__`
UpperCAmelCase_       : List[str]							=    10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
UpperCAmelCase_       : Optional[int]							=    4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
UpperCAmelCase_       : List[Any]							=    """500MB"""

# Parquet configuration
UpperCAmelCase_       : Optional[Any]							=    1_00
UpperCAmelCase_       : Any							=    1_00
UpperCAmelCase_       : Dict							=    1_00

# Offline mode
UpperCAmelCase_       : List[Any]							=    os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
UpperCAmelCase_       : List[Any]							=    0  # Disabled
UpperCAmelCase_       : Optional[Any]							=    float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

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

UpperCAmelCase_       : int							=    """datasets_modules"""

UpperCAmelCase_       : Optional[int]							=    2_55

# Streaming
UpperCAmelCase_       : Any							=    20
UpperCAmelCase_       : Any							=    5

# Datasets without script
UpperCAmelCase_       : Union[str, Any]							=    2_00
UpperCAmelCase_       : str							=    10
UpperCAmelCase_       : str							=    2_00

# Progress bars
UpperCAmelCase_       : List[str]							=    0.05  # 20 progress updates per sec






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
