
"""simple docstring"""




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 	lowerCAmelCase_						:


				"""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    )					->		Union[str, Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Union["FeatureType", Dict[str, "FeatureType"]]:



											"""simple docstring"""




											from .features import Value

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






@dataclass
class 	lowerCAmelCase_						:


				"""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 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   sorted(set(self.languages    )    ) if self.languages else None
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.languages    ) if self.languages else None



				def __call__(self    )					->		List[str]:



											"""simple docstring"""




											return pa.struct({"""language""": pa.list_(pa.string()    ), """translation""": pa.list_(pa.string()    )}    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   set(self.languages    )
											if self.languages and set(__lowercase    ) - lang_set:
																		raise ValueError(
																		    F'''Some languages in example ({', '.join(sorted(set(__lowercase    ) - lang_set    )    )}) are not in valid set ({', '.join(__lowercase    )}).'''    )

											# Convert dictionary into tuples, splitting out cases where there are
											# multiple translations for a single language.
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											for lang, text in translation_dict.items():
																		if isinstance(__lowercase   ,       __lowercase    ):
																									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__						:       Optional[Any]           =   zip(*sorted(__lowercase    )    )

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



				def 	__magic_name__    (self    )					->		Union["FeatureType", Dict[str, "FeatureType"]]:



											"""simple docstring"""




											from .features import Sequence, Value

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








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel