

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType



@dataclass
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]
			_UpperCAmelCase   :							Optional[str]										=     None
			# Automatically constructed
			_UpperCAmelCase   :							ClassVar[str]										=     "dict"
			_UpperCAmelCase   :							ClassVar[Any]										=     None
			_UpperCAmelCase   :							str										=     field(default='''Translation'''		,       init=UpperCAmelCase_		,       repr=UpperCAmelCase_	)



			def __call__( self  :     str):
									return pa.struct({lang: pa.string() for lang in sorted(self.languages)})




			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									from .features import Value

									return {k: Value("string") for k in sorted(self.languages)}







@dataclass
class   __lowercase				:




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[List]										=     None
			_UpperCAmelCase   :							Optional[int]										=     None
			_UpperCAmelCase   :							Optional[str]										=     None
			# Automatically constructed
			_UpperCAmelCase   :							ClassVar[str]										=     "dict"
			_UpperCAmelCase   :							ClassVar[Any]										=     None
			_UpperCAmelCase   :							str										=     field(default='''TranslationVariableLanguages'''		,       init=UpperCAmelCase_		,       repr=UpperCAmelCase_	)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: List[str]  		=       sorted(set(self.languages)) if self.languages else None
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(self.languages) if self.languages else None



			def __call__( self  :     Any):
									return pa.struct({"language": pa.list_(pa.string()), "translation": pa.list_(pa.string())})



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       set(self.languages)
									if self.languages and set(lowerCAmelCase__) - lang_set:
															raise ValueError(
															    F"Some languages in example ({', '.join(sorted(set(lowerCAmelCase__) - lang_set))}) are not in valid set ({', '.join(lowerCAmelCase__)}).")

									# Convert dictionary into tuples, splitting out cases where there are
									# multiple translations for a single language.
									SCREAMING_SNAKE_CASE_: Tuple  		=       []
									for lang, text in translation_dict.items():
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					translation_tuples.append((lang, text))
															else:
																					translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       zip(*sorted(lowerCAmelCase__))

									return {"language": languages, "translation": translations}




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									from .features import Sequence, Value

									return {
									    "language": Sequence(Value("string")),
									    "translation": Sequence(Value("string")),
									}









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

lowerCAmelCase							:  Any   	=						TaTokenizer

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

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

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

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""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

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






