



import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCAmelCase__       =	"__DUMMY_TRANSFORMERS_USER__"
UpperCAmelCase__       =	"Dummy User"
UpperCAmelCase__       =	"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"

UpperCAmelCase__       =	"https://hub-ci.huggingface.co"
UpperCAmelCase__       =	CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCAmelCase__       =	CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCAmelCase__       =	Path("~/.huggingface/hub_ci_token").expanduser()





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       Any   )    ->			List[str]:







			'''simple docstring'''
			monkeypatch.setattr(
			    'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'							,       _UpperCAmelCase   )





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			Optional[int]:







			'''simple docstring'''
			monkeypatch.setattr('datasets.config.HF_ENDPOINT'							,       _UpperCAmelCase   )
			monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'							,       _UpperCAmelCase   )





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			Any:







			'''simple docstring'''
			monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'							,       _UpperCAmelCase   )





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       List[str]   )    ->			Optional[int]:







			'''simple docstring'''
			HfFolder.save_token(_UpperCAmelCase   )
			yield
			HfFolder.delete_token()





@pytest.fixture(scope='session'   )
def 							A     (					)    ->			Union[str, Any]:







			'''simple docstring'''
			return HfApi(endpoint=_UpperCAmelCase   )





@pytest.fixture(scope='session'   )
def 							A     (					_UpperCAmelCase    :       HfApi   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       HfFolder.get_token()
			HfFolder.save_token(_UpperCAmelCase   )
			yield CI_HUB_USER_TOKEN
			if previous_token is not None:
						HfFolder.save_token(_UpperCAmelCase   )





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       Any   )    ->			Dict:







			'''simple docstring'''
			def _cleanup_repo(_UpperCAmelCase    :       int   ):
						hf_api.delete_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'   )

			return _cleanup_repo





@pytest.fixture
def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			Any:







			'''simple docstring'''
			@contextmanager
			def _temporary_repo(_UpperCAmelCase    :       List[str]   ):
						try:
									yield repo_id
						finally:
									cleanup_repo(_UpperCAmelCase   )

			return _temporary_repo





@pytest.fixture(scope='session'   )
def 							A     (					_UpperCAmelCase    :       HfApi							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       int   )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       F"repo_txt_data-{int(time.time() * 10E3   )}"
			_UpperCAmelCase			=       F"{CI_HUB_USER}/{repo_name}"
			hf_api.create_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'							,       private=_UpperCAmelCase   )
			hf_api.upload_file(
			    token=_UpperCAmelCase							,       path_or_fileobj=str(_UpperCAmelCase   )							,       path_in_repo='data/text_data.txt'							,       repo_id=_UpperCAmelCase							,       repo_type='dataset'							,       )
			yield repo_id
			try:
						hf_api.delete_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'   )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass





@pytest.fixture()
def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Any   )    ->			Dict:







			'''simple docstring'''
			return hf_private_dataset_repo_txt_data_





@pytest.fixture(scope='session'   )
def 							A     (					_UpperCAmelCase    :       HfApi							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Optional[Any]   )    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       F"repo_zipped_txt_data-{int(time.time() * 10E3   )}"
			_UpperCAmelCase			=       F"{CI_HUB_USER}/{repo_name}"
			hf_api.create_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'							,       private=_UpperCAmelCase   )
			hf_api.upload_file(
			    token=_UpperCAmelCase							,       path_or_fileobj=str(_UpperCAmelCase   )							,       path_in_repo='data.zip'							,       repo_id=_UpperCAmelCase							,       repo_type='dataset'							,       )
			yield repo_id
			try:
						hf_api.delete_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'   )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass





@pytest.fixture()
def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       Any   )    ->			Dict:







			'''simple docstring'''
			return hf_private_dataset_repo_zipped_txt_data_





@pytest.fixture(scope='session'   )
def 							A     (					_UpperCAmelCase    :       HfApi							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Optional[Any]   )    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       F"repo_zipped_img_data-{int(time.time() * 10E3   )}"
			_UpperCAmelCase			=       F"{CI_HUB_USER}/{repo_name}"
			hf_api.create_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'							,       private=_UpperCAmelCase   )
			hf_api.upload_file(
			    token=_UpperCAmelCase							,       path_or_fileobj=str(_UpperCAmelCase   )							,       path_in_repo='data.zip'							,       repo_id=_UpperCAmelCase							,       repo_type='dataset'							,       )
			yield repo_id
			try:
						hf_api.delete_repo(_UpperCAmelCase							,       token=_UpperCAmelCase							,       repo_type='dataset'   )
			except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
						pass





@pytest.fixture()
def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       str   )    ->			str:







			'''simple docstring'''
			return hf_private_dataset_repo_zipped_img_data_





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDebertaForMaskedLM",
							    "TFDebertaForQuestionAnswering",
							    "TFDebertaForSequenceClassification",
							    "TFDebertaForTokenClassification",
							    "TFDebertaModel",
							    "TFDebertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
