




"""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=__lowercase				,      repr=__lowercase						)



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



											"""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=__lowercase				,      repr=__lowercase						)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     sorted(set(self.languages					)					) if self.languages else None
											SCREAMING_SNAKE_CASE__		: List[Any]     =     len(self.languages					) if self.languages else None



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



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




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

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

											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"""					)					),
											}






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 16     , SCREAMING_SNAKE_CASE__ = 88     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "geglu"     , SCREAMING_SNAKE_CASE__ = None     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     nn.ModuleList(
											    [
											        TransformeraDModel(
											            num_attention_heads=SCREAMING_SNAKE_CASE__     , attention_head_dim=SCREAMING_SNAKE_CASE__     , in_channels=SCREAMING_SNAKE_CASE__     , num_layers=SCREAMING_SNAKE_CASE__     , dropout=SCREAMING_SNAKE_CASE__     , norm_num_groups=SCREAMING_SNAKE_CASE__     , cross_attention_dim=SCREAMING_SNAKE_CASE__     , attention_bias=SCREAMING_SNAKE_CASE__     , sample_size=SCREAMING_SNAKE_CASE__     , num_vector_embeds=SCREAMING_SNAKE_CASE__     , activation_fn=SCREAMING_SNAKE_CASE__     , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__     , )
											        for _ in range(2					)
											    ]					)

											# Variables that can be set by a pipeline:

											# The ratio of transformer1 to transformer2's output states to be combined during inference
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

											# The shape of `encoder_hidden_states` is expected to be
											# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [77, 2_57]

											# Which transformer to use to encode which condition.
											# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
											SCREAMING_SNAKE_CASE__		: int     =     [1, 0]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.transformers[transformer_index](
																		    SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , timestep=SCREAMING_SNAKE_CASE__     , cross_attention_kwargs=SCREAMING_SNAKE_CASE__     , return_dict=SCREAMING_SNAKE_CASE__     , )[0]
																		encoded_states.append(encoded_state - input_states					)
																		tokens_start += self.condition_lengths[i]

											SCREAMING_SNAKE_CASE__		: Tuple     =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output_states + input_states

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




