
"""simple docstring"""





from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__magic_name__       =			TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__magic_name__       =			TaTokenizerFast

__magic_name__       =			{"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			[
							    "MT5EncoderModel",
							    "MT5ForConditionalGeneration",
							    "MT5ForQuestionAnswering",
							    "MT5Model",
							    "MT5PreTrainedModel",
							    "MT5Stack",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__magic_name__       =			_LazyModule(
							    __name__,
							    globals()["__file__"],
							    _import_structure,
							    extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
							    module_spec=__spec__,
							)






"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__magic_name__       =			logging.get_logger(__name__)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				warnings.warn(
				    """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
				    """ use YolosImageProcessor instead."""							,  lowerCAmelCase__							,  )
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




