

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_a:      List[str]								=     get_logger(__name__)

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

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

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

_a:      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
_a:      List[Any]								=     {"""1""", """ON""", """YES""", """TRUE"""}
_a:      Optional[Any]								=     ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_a:      str								=     version.parse(importlib_metadata.version("""dill"""))
_a:      Dict								=     version.parse(importlib_metadata.version("""pandas"""))
_a:      int								=     version.parse(importlib_metadata.version("""pyarrow"""))

_a:      Union[str, Any]								=     os.environ.get("""USE_TF""", """AUTO""").upper()
_a:      str								=     os.environ.get("""USE_TORCH""", """AUTO""").upper()
_a:      Dict								=     os.environ.get("""USE_JAX""", """AUTO""").upper()

_a:      int								=     """N/A"""
_a:      Dict								=     False

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

_a:      Tuple								=     """N/A"""
_a:      Optional[int]								=     False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							_a:      Optional[int]								=     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:
																												_a:      int								=     version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					_a:      List[str]								=     False
							if TF_AVAILABLE:
														if TF_VERSION.major < 2:
																					logger.info(F'TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.')
																					_a:      List[str]								=     False
														else:
																					logger.info(F'TensorFlow version {TF_VERSION} available.')
else:
							logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_a:      str								=     """N/A"""
_a:      List[Any]								=     False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							_a:      Optional[Any]								=     importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
							if JAX_AVAILABLE:
														try:
																					_a:      Dict								=     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""")


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

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


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

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

_a:      List[Any]								=     os.path.join(HF_CACHE_HOME, """metrics""")
_a:      Dict								=     Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

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

_a:      int								=     """downloads"""
_a:      Optional[Any]								=     os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_a:      Tuple								=     Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

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

# Download count for the website
_a:      Optional[int]								=     (
    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)
_a:      List[Any]								=     1000

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

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

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

# Parquet configuration
_a:      str								=     100
_a:      List[Any]								=     100
_a:      Optional[int]								=     100

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

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

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

_a:      Optional[int]								=     """datasets_modules"""

_a:      int								=     255

# Streaming
_a:      List[Any]								=     20
_a:      Tuple								=     5

# Datasets without script
_a:      Any								=     200
_a:      Optional[Any]								=     10
_a:      List[Any]								=     200

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


import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class   __UpperCamelCase      (   unittest.TestCase       ,       lowercase  ):




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_tool("document-question-answering"							)
		self.tool.setup()
		UpperCAmelCase_    			=     load_tool("document-question-answering"						,		remote=lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		self.tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)