




"""simple docstring"""





import time
from contextlib import contextmanager
from pathlib import Path

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


A				=							"""__DUMMY_TRANSFORMERS_USER__"""
A				=							"""Dummy User"""
A				=							"""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"""

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

@pytest.fixture
def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			monkeypatch.setattr(
			    "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"					,							UpperCamelCase				)

@pytest.fixture
def        _UpperCamelCase     (      UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			monkeypatch.setattr("datasets.config.HF_ENDPOINT"					,							UpperCamelCase				)
			monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"					,							UpperCamelCase				)

@pytest.fixture
def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"					,							UpperCamelCase				)

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


			"""simple docstring"""
			HfFolder.save_token(UpperCamelCase				)
			yield
			HfFolder.delete_token()

@pytest.fixture(scope="session"				)
def        _UpperCamelCase     (      )      ->      Tuple:


			"""simple docstring"""
			return HfApi(endpoint=UpperCamelCase				)

@pytest.fixture(scope="session"				)
def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       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        _UpperCamelCase     (      UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			def _cleanup_repo(UpperCamelCase				):
						hf_api.delete_repo(UpperCamelCase					,							token=UpperCamelCase					,							repo_type="dataset"				)

			return _cleanup_repo

@pytest.fixture
def        _UpperCamelCase     (      UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			@contextmanager
			def _temporary_repo(UpperCamelCase				):
						try:
									yield repo_id
						finally:
									cleanup_repo(UpperCamelCase				)

			return _temporary_repo

@pytest.fixture(scope="session"				)
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			str												=       f"repo_txt_data-{int(time.time() * 1_0e3				)}"
			__UpperCAmelCase						:			List[Any]												=       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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope="session"				)
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       f"repo_zipped_txt_data-{int(time.time() * 1_0e3				)}"
			__UpperCAmelCase						:			Dict												=       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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope="session"				)
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       f"repo_zipped_img_data-{int(time.time() * 1_0e3				)}"
			__UpperCAmelCase						:			List[Any]												=       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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			return hf_private_dataset_repo_zipped_img_data_









'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






