



import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_lowerCamelCase         =	get_logger(__name__)

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

# Metrics
_lowerCamelCase         =	"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_lowerCamelCase         =	"""https://cdn-datasets.huggingface.co/datasets/metric"""
_lowerCamelCase         =	"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

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

_lowerCamelCase         =	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         =	{"""1""", """ON""", """YES""", """TRUE"""}
_lowerCamelCase         =	ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_lowerCamelCase         =	version.parse(importlib_metadata.version("""dill"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pandas"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pyarrow"""))

_lowerCamelCase         =	os.environ.get("""USE_TF""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_TORCH""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_JAX""", """AUTO""").upper()

_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""torch""") is not None
      if TORCH_AVAILABLE:
            try:
                  _lowerCamelCase         =	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         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	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         =	version.parse(importlib_metadata.version(package))
                  except importlib_metadata.PackageNotFoundError:
                        continue
                  else:
                        break
            else:
                  _lowerCamelCase         =	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         =	False
            else:
                  logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
      logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
      if JAX_AVAILABLE:
            try:
                  _lowerCamelCase         =	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         =	os.environ.get("""USE_BEAM""", """AUTO""").upper()
_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            _lowerCamelCase         =	version.parse(importlib_metadata.version("""apache_beam"""))
            _lowerCamelCase         =	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         =	importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
_lowerCamelCase         =	importlib.util.find_spec("""PIL""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_lowerCamelCase         =	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         =	importlib.util.find_spec("""rarfile""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""zstandard""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""lz4""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""py7zr""") is not None


# Cache location
_lowerCamelCase         =	"""~/.cache"""
_lowerCamelCase         =	os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
_lowerCamelCase         =	os.path.join(XDG_CACHE_HOME, """huggingface""")
_lowerCamelCase         =	os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """datasets""")
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """metrics""")
_lowerCamelCase         =	Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """modules""")
_lowerCamelCase         =	Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_lowerCamelCase         =	"""downloads"""
_lowerCamelCase         =	os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_lowerCamelCase         =	"""extracted"""
_lowerCamelCase         =	os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_lowerCamelCase         =	(
    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         =	1000

# Size of the preloaded record batch in `Dataset.__iter__`
_lowerCamelCase         =	10

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

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

# Parquet configuration
_lowerCamelCase         =	100
_lowerCamelCase         =	100
_lowerCamelCase         =	100

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

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

# File names
_lowerCamelCase         =	"""dataset.arrow"""
_lowerCamelCase         =	"""indices.arrow"""
_lowerCamelCase         =	"""state.json"""
_lowerCamelCase         =	"""dataset_info.json"""
_lowerCamelCase         =	"""dataset_infos.json"""
_lowerCamelCase         =	"""LICENSE"""
_lowerCamelCase         =	"""metric_info.json"""
_lowerCamelCase         =	"""dataset_dict.json"""

_lowerCamelCase         =	"""datasets_modules"""

_lowerCamelCase         =	255

# Streaming
_lowerCamelCase         =	20
_lowerCamelCase         =	5

# Datasets without script
_lowerCamelCase         =	200
_lowerCamelCase         =	10
_lowerCamelCase         =	200

# Progress bars
_lowerCamelCase         =	0.0_5  # 20 progress updates per sec











from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
      import pyximport

      pyximport.install(setup_args={"""include_dirs""": np.get_include()})
      from . import algos_graphormer  # noqa E402






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  int = 512  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						x.shape[1] if len(x.shape  ) > 1 else 1
 __SCREAMING_SNAKE_CASE							:     int							=						1 + np.arange(0		,						feature_num * offset		,						__lowerCamelCase		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						x + feature_offset
 return x






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  int=True  ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,						["cython"]  )

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __SCREAMING_SNAKE_CASE							:     List[str]							=						np.asarray(item["edge_attr"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						np.ones((len(item["edge_index"][0]  ), 1)		,						dtype=np.intaa  )  # same embedding for all

 if keep_features and "node_feat" in item.keys():  # input_nodes
  __SCREAMING_SNAKE_CASE							:     Dict							=						np.asarray(item["node_feat"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						np.ones((item["num_nodes"], 1)		,						dtype=np.intaa  )  # same embedding for all

 __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(item["edge_index"]		,						dtype=np.intaa  )

 __SCREAMING_SNAKE_CASE							:     Any							=						convert_to_single_emb(__lowerCamelCase  ) + 1
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item["num_nodes"]

 if len(edge_attr.shape  ) == 1:
  __SCREAMING_SNAKE_CASE							:     Any							=						edge_attr[:, None]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_to_single_emb(__lowerCamelCase  ) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 __SCREAMING_SNAKE_CASE							:     int							=						np.zeros([num_nodes, num_nodes]		,						dtype=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						True

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.floyd_warshall(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.amax(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.gen_edge_input(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single  )  # with graph token

 # combine
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						input_nodes + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     Dict							=						attn_bias
 __SCREAMING_SNAKE_CASE							:     Tuple							=						attn_edge_type
 __SCREAMING_SNAKE_CASE							:     Any							=						shortest_path_result.astype(np.intaa  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     int							=						np.sum(__lowerCamelCase		,						axis=1  ).reshape(-1  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						item["in_degree"]  # for undirected graph
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						input_edges + 1  # we shift all indices by one for padding
 if "labels" not in item:
  __SCREAMING_SNAKE_CASE							:     Any							=						item["y"]

 return item






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[str]=2_0  ,	UpperCamelCase			:				Dict=False					)->Any:
      if not is_cython_available():
       raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						spatial_pos_max
      __SCREAMING_SNAKE_CASE							:     Dict							=						on_the_fly_processing






     def __call__(							self			:				Optional[int]  ,	UpperCamelCase			:				List[dict]					)->Dict[str, Any]:
      if self.on_the_fly_processing:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[preprocess_item(UpperCamelCase					) for i in features]

      if not isinstance(features[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[vars(UpperCamelCase					) for f in features]
      __SCREAMING_SNAKE_CASE							:     str							=						{}

      __SCREAMING_SNAKE_CASE							:     int							=						max(len(i["input_nodes"]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						len(features[0]["input_nodes"][0]					)
      __SCREAMING_SNAKE_CASE							:     Any							=						len(features[0]["attn_edge_type"][0][0]					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						max(len(i["input_edges"][0][0]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						len(features[0]["input_edges"][0][0][0]					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						len(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.zeros(UpperCamelCase  ,	max_node_num + 1  ,	max_node_num + 1  ,	dtype=torch.float					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.zeros(
          UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)

      for ix, f in enumerate(UpperCamelCase					):
       for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
        __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor(f[k]					)

       if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
        __SCREAMING_SNAKE_CASE							:     int							=						float("-inf"					)

       __SCREAMING_SNAKE_CASE							:     str							=						f["attn_bias"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f[
           "attn_edge_type"
       ]
       __SCREAMING_SNAKE_CASE							:     Dict							=						f["spatial_pos"]
       __SCREAMING_SNAKE_CASE							:     int							=						f["in_degree"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f["input_nodes"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						f["input_edges"]

      __SCREAMING_SNAKE_CASE							:     Any							=						batch["in_degree"]

      __SCREAMING_SNAKE_CASE							:     int							=						features[0]["labels"]
      if len(UpperCamelCase					) == 1:  # one task
       if isinstance(sample[0]  ,	UpperCamelCase					):  # regression
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
       else:  # binary classification
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
      else:  # multi task classification, left to float to keep the NaNs
       __SCREAMING_SNAKE_CASE							:     int							=						torch.from_numpy(np.stack([i["labels"] for i in features]  ,	axis=0					)					)

      return batch






