



import time
from contextlib import contextmanager
from pathlib import Path

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


SCREAMING_SNAKE_CASE     				=						"__DUMMY_TRANSFORMERS_USER__"
SCREAMING_SNAKE_CASE     				=						"Dummy User"
SCREAMING_SNAKE_CASE     				=						"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"

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







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Dict:
		monkeypatch.setattr(
		    "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"			,     __SCREAMING_SNAKE_CASE )







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Dict:
		monkeypatch.setattr("datasets.config.HF_ENDPOINT"			,     __SCREAMING_SNAKE_CASE )
		monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"			,     __SCREAMING_SNAKE_CASE )







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Any:
		monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"			,     __SCREAMING_SNAKE_CASE )







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:
		HfFolder.save_token(__SCREAMING_SNAKE_CASE )
		yield
		HfFolder.delete_token()







@pytest.fixture(scope="session" )
def snake_case__  (			)		->							Union[str, Any]:
		return HfApi(endpoint=__SCREAMING_SNAKE_CASE )







@pytest.fixture(scope="session" )
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				HfFolder.get_token()
		HfFolder.save_token(__SCREAMING_SNAKE_CASE )
		yield CI_HUB_USER_TOKEN
		if previous_token is not None:
				HfFolder.save_token(__SCREAMING_SNAKE_CASE )







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		def _cleanup_repo(__SCREAMING_SNAKE_CASE ):
				hf_api.delete_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset" )

		return _cleanup_repo







@pytest.fixture
def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		@contextmanager
		def _temporary_repo(__SCREAMING_SNAKE_CASE ):
				try:
						yield repo_id
				finally:
						cleanup_repo(__SCREAMING_SNAKE_CASE )

		return _temporary_repo







@pytest.fixture(scope="session" )
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		UpperCAmelCase_						=				f'''repo_txt_data-{int(time.time() * 10E3 )}'''
		UpperCAmelCase_						=				f'''{CI_HUB_USER}/{repo_name}'''
		hf_api.create_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     private=__SCREAMING_SNAKE_CASE )
		hf_api.upload_file(
		    token=__SCREAMING_SNAKE_CASE			,     path_or_fileobj=str(__SCREAMING_SNAKE_CASE )			,     path_in_repo="data/text_data.txt"			,     repo_id=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     )
		yield repo_id
		try:
				hf_api.delete_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset" )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		return hf_private_dataset_repo_txt_data_







@pytest.fixture(scope="session" )
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		UpperCAmelCase_						=				f'''repo_zipped_txt_data-{int(time.time() * 10E3 )}'''
		UpperCAmelCase_						=				f'''{CI_HUB_USER}/{repo_name}'''
		hf_api.create_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     private=__SCREAMING_SNAKE_CASE )
		hf_api.upload_file(
		    token=__SCREAMING_SNAKE_CASE			,     path_or_fileobj=str(__SCREAMING_SNAKE_CASE )			,     path_in_repo="data.zip"			,     repo_id=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     )
		yield repo_id
		try:
				hf_api.delete_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset" )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
		return hf_private_dataset_repo_zipped_txt_data_







@pytest.fixture(scope="session" )
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				f'''repo_zipped_img_data-{int(time.time() * 10E3 )}'''
		UpperCAmelCase_						=				f'''{CI_HUB_USER}/{repo_name}'''
		hf_api.create_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     private=__SCREAMING_SNAKE_CASE )
		hf_api.upload_file(
		    token=__SCREAMING_SNAKE_CASE			,     path_or_fileobj=str(__SCREAMING_SNAKE_CASE )			,     path_in_repo="data.zip"			,     repo_id=__SCREAMING_SNAKE_CASE			,     repo_type="dataset"			,     )
		yield repo_id
		try:
				hf_api.delete_repo(__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE			,     repo_type="dataset" )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		return hf_private_dataset_repo_zipped_img_data_






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["XLNetTokenizer"]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLNetForMultipleChoice",
			    "XLNetForQuestionAnswering",
			    "XLNetForQuestionAnsweringSimple",
			    "XLNetForSequenceClassification",
			    "XLNetForTokenClassification",
			    "XLNetLMHeadModel",
			    "XLNetModel",
			    "XLNetPreTrainedModel",
			    "load_tf_weights_in_xlnet",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXLNetForMultipleChoice",
			    "TFXLNetForQuestionAnsweringSimple",
			    "TFXLNetForSequenceClassification",
			    "TFXLNetForTokenClassification",
			    "TFXLNetLMHeadModel",
			    "TFXLNetMainLayer",
			    "TFXLNetModel",
			    "TFXLNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

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

