
"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_a   :      List[Any]       =				get_logger(__name__)

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

# Metrics
_a   :      Tuple       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_a   :      int       =				"""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   :      Dict       =				"""main"""

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


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

_a   :      Tuple       =				os.environ.get("""USE_TF""", """AUTO""").upper()
_a   :      Optional[Any]       =				os.environ.get("""USE_TORCH""", """AUTO""").upper()
_a   :      Tuple       =				os.environ.get("""USE_JAX""", """AUTO""").upper()

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

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    _a   :      List[str]       =				importlib.util.find_spec("""torch""") is not None
    if TORCH_AVAILABLE:
        try:
            _a   :      str       =				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   :      Optional[int]       =				"""N/A"""
_a   :      str       =				False

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


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

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _a   :      int       =				importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
    if JAX_AVAILABLE:
        try:
            _a   :      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""")


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

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


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

_a   :      Optional[Any]       =				os.path.join(HF_CACHE_HOME, """datasets""")
_a   :      Tuple       =				Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

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

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

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

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

# Download count for the website
_a   :      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)
_a   :      Optional[int]       =				1_000

# Size of the preloaded record batch in `Dataset.__iter__`
_a   :      Dict       =				10

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

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

# Parquet configuration
_a   :      Optional[Any]       =				100
_a   :      Any       =				100
_a   :      Tuple       =				100

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

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

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

_a   :      str       =				"""datasets_modules"""

_a   :      List[Any]       =				255

# Streaming
_a   :      Any       =				20
_a   :      Union[str, Any]       =				5

# Datasets without script
_a   :      Union[str, Any]       =				200
_a   :      List[str]       =				10
_a   :      Dict       =				200

# Progress bars
_a   :      Dict       =				0.05  # 20 progress updates per sec





'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer







@require_torch
class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					Tuple						)->	Optional[int]:
          snake_case__					:	Any          =					XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(lowerCamelCase						)

          # encoder-decoder outputs
          snake_case__					:	Optional[int]          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          snake_case__					:	Optional[int]          =					torch.tensor(
              [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]						).to(lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # encoder outputs
          snake_case__					:	Dict          =					model.prophetnet.encoder(lowerCamelCase						)[0]
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]						).to(lowerCamelCase						)
          snake_case__					:	str          =					torch.Size((1, 4, 1_024)						)
          self.assertEqual(encoder_outputs.shape					, lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # decoder outputs
          snake_case__					:	int          =					model.prophetnet.decoder(
              lowerCamelCase					, encoder_hidden_states=lowerCamelCase					, )
          snake_case__					:	Any          =					decoder_outputs[1].view(1					, model.config.ngram					, 14					, -1						)
          snake_case__					:	Optional[Any]          =					model.lm_head(lowerCamelCase						)
          snake_case__					:	str          =					predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Tuple          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)

          snake_case__					:	str          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	List[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[str]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case__					:	List[str]          =					torch.tensor(
              [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]						).to(lowerCamelCase						)

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          snake_case__					:	Dict          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					512

          snake_case__					:	Any          =					XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

          snake_case__					:	Optional[int]          =					(
              """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
              """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
              """ system will not be able to receive security updates, which could make their computers vulnerable to"""
              """ cyber attacks."""
          )
          snake_case__					:	Union[str, Any]          =					(
              """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
              """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
              """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
              """ к кибератакам."""
          )
          snake_case__					:	int          =					(
              """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
          )

          snake_case__					:	int          =					tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					, padding=lowerCamelCase					, max_length=255					, return_tensors="""pt"""						).input_ids
          snake_case__					:	Optional[int]          =					input_ids.to(lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					model.generate(
              lowerCamelCase					, num_beams=10					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					[tokenizer.decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids]
          snake_case__					:	Optional[Any]          =					"""Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case__					:	Dict          =					"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case__					:	Optional[int]          =					"""微软打算终止对Windows 7操作系统的免费支持"""
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]					, lowerCamelCase					, )

          snake_case__					:	Optional[int]          =					model.generate(
              lowerCamelCase					, num_beams=1					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Tuple          =					[
              tokenizer.convert_ids_to_tokens(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids_beama
          ]
          snake_case__					:	Any          =					"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
          snake_case__					:	Union[str, Any]          =					"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """						)
          snake_case__					:	Tuple          =					"""微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """						)
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]					, lowerCamelCase					, )




