






import pytest

import datasets


# Import fixture modules as plugins
_UpperCAmelCase			:		Tuple   				=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       int       )		->    Any:


				'''simple docstring'''




				for item in items:
								if any(marker in item.keywords for marker in ['''integration''', '''unit''']       ):
												continue
								item.add_marker(pytest.mark.unit       )





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    List[str]:


				'''simple docstring'''




				config.addinivalue_line('''markers'''     ,					'''torchaudio_latest: mark test to run with torchaudio>=0.12'''       )





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Dict       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						tmp_path_factory.getbasetemp() / '''cache'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''datasets'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''metrics'''
				lowerCAmelCase__      			=						test_hf_cache_home / '''modules'''
				monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''     ,					str(lowercase__       )       )
				monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads'''
				monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''     ,					str(lowercase__       )       )
				lowerCAmelCase__      			=						test_hf_datasets_cache / '''downloads''' / '''extracted'''
				monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''     ,					str(lowercase__       )       )





@pytest.fixture(autouse=lowercase__     ,					scope='''session'''       )
def 	lowerCAmelCase_		()		->    int:


				'''simple docstring'''




				datasets.disable_progress_bar()





@pytest.fixture(autouse=lowercase__       )
def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    List[str]:


				'''simple docstring'''




				monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''     ,					lowercase__       )





@pytest.fixture
def 	lowerCAmelCase_		(lowercase__						:       List[str]       )		->    List[Any]:


				'''simple docstring'''




				monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''     ,					lowercase__       )













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





