import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_lowerCamelCase    :      int	 =						get_logger(__name__)

# Datasets
_lowerCamelCase    :      Tuple	 =						"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase    :      Dict	 =						"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase    :      Union[str, 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    :      Optional[int]	 =						"""https://cdn-datasets.huggingface.co/datasets/metric"""
_lowerCamelCase    :      Dict	 =						"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

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

_lowerCamelCase    :      Optional[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    :      Tuple	 =						{"""1""", """ON""", """YES""", """TRUE"""}
_lowerCamelCase    :      List[str]	 =						ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
_lowerCamelCase    :      int	 =						version.parse(importlib_metadata.version('''dill'''))
_lowerCamelCase    :      Tuple	 =						version.parse(importlib_metadata.version('''pandas'''))
_lowerCamelCase    :      Optional[Any]	 =						version.parse(importlib_metadata.version('''pyarrow'''))

_lowerCamelCase    :      Any	 =						os.environ.get('''USE_TF''', '''AUTO''').upper()
_lowerCamelCase    :      Dict	 =						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    :      Optional[Any]	 =						importlib.util.find_spec('''torch''') is not None
							if TORCH_AVAILABLE:
														try:
																					_lowerCamelCase    :      Optional[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    :      List[str]	 =						"""N/A"""
_lowerCamelCase    :      Optional[int]	 =						False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							_lowerCamelCase    :      Dict	 =						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    :      Optional[int]	 =						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    :      Any	 =						False
														else:
																					logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
							logger.info('''Disabling Tensorflow because USE_TORCH is set''')


_lowerCamelCase    :      List[Any]	 =						"""N/A"""
_lowerCamelCase    :      Any	 =						False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							_lowerCamelCase    :      List[str]	 =						importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
							if JAX_AVAILABLE:
														try:
																					_lowerCamelCase    :      Tuple	 =						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    :      Dict	 =						os.environ.get('''USE_BEAM''', '''AUTO''').upper()
_lowerCamelCase    :      Dict	 =						"""N/A"""
_lowerCamelCase    :      List[Any]	 =						False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														_lowerCamelCase    :      int	 =						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    :      List[str]	 =						importlib.util.find_spec('''sqlalchemy''') is not None

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


# Cache location
_lowerCamelCase    :      Dict	 =						"""~/.cache"""
_lowerCamelCase    :      Tuple	 =						os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
_lowerCamelCase    :      Dict	 =						os.path.join(XDG_CACHE_HOME, '''huggingface''')
_lowerCamelCase    :      Optional[int]	 =						os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

_lowerCamelCase    :      Optional[int]	 =						os.path.join(HF_CACHE_HOME, '''datasets''')
_lowerCamelCase    :      Any	 =						Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

_lowerCamelCase    :      List[Any]	 =						os.path.join(HF_CACHE_HOME, '''metrics''')
_lowerCamelCase    :      Optional[int]	 =						Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

_lowerCamelCase    :      List[str]	 =						os.path.join(HF_CACHE_HOME, '''modules''')
_lowerCamelCase    :      List[str]	 =						Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

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

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

# Download count for the website
_lowerCamelCase    :      List[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    :      Any	 =						10_00

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

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

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

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

# Offline mode
_lowerCamelCase    :      Union[str, Any]	 =						os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
_lowerCamelCase    :      Union[str, Any]	 =						0  # Disabled
_lowerCamelCase    :      Any	 =						float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

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

_lowerCamelCase    :      Union[str, Any]	 =						"""datasets_modules"""

_lowerCamelCase    :      List[Any]	 =						2_55

# Streaming
_lowerCamelCase    :      int	 =						20
_lowerCamelCase    :      int	 =						5

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

# Progress bars
_lowerCamelCase    :      Optional[Any]	 =						0.05  # 20 progress updates per sec










'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,		[0.00_02, 0.0_02, 0.02]     ):
               self.check_over_configs(beta_start=lowerCamelCase__					,		beta_end=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps					,		device=lowerCamelCase__     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




