




"""simple docstring"""




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


UpperCAmelCase__   :					List[str]												=				get_logger(__name__)


UpperCAmelCase__   :					Optional[Any]												=				r'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'



class 			lowerCAmelCase_		:


				"""simple docstring"""



				@add_start_docstrings(SCREAMING_SNAKE_CASE__					)
				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)






class 			lowerCAmelCase_		:


				"""simple docstring"""



				@add_start_docstrings(SCREAMING_SNAKE_CASE__					)
				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				@add_start_docstrings(SCREAMING_SNAKE_CASE__					)
				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											for processor in self:
																		SCREAMING_SNAKE_CASE__		: str     =     inspect.signature(processor.__call__					).parameters
																		if len(SCREAMING_SNAKE_CASE__					) > 3:
																									if not all(arg in kwargs for arg in list(function_args.keys()					)[2:]					):
																																raise ValueError(
																																    F'''Make sure that all the required parameters: {list(function_args.keys()					)} for '''
																																    F'''{processor.__class__} are passed to the logits processor.'''					)
																									SCREAMING_SNAKE_CASE__		: Tuple     =     processor(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)
																		else:
																									SCREAMING_SNAKE_CASE__		: int     =     processor(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''					)

											SCREAMING_SNAKE_CASE__		: Any     =     temperature



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     scores / self.temperature
											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = -float("""Inf"""					)     , SCREAMING_SNAKE_CASE__ = 1					)      ->	List[str]:



											"""simple docstring"""




											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or (top_p < 0 or top_p > 1.0):
																		raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''					)
											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or (min_tokens_to_keep < 1):
																		raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''					)

											SCREAMING_SNAKE_CASE__		: str     =     top_p
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     filter_value
											SCREAMING_SNAKE_CASE__		: str     =     min_tokens_to_keep



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     lax.top_k(SCREAMING_SNAKE_CASE__     , scores.shape[-1]					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     jnp.full_like(SCREAMING_SNAKE_CASE__     , self.filter_value					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     jax.nn.softmax(SCREAMING_SNAKE_CASE__     , axis=-1					).cumsum(axis=-1					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     cumulative_probs < self.top_p

											# include the token that is higher than top_p as well
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     jnp.roll(SCREAMING_SNAKE_CASE__     , 1					)
											score_mask |= score_mask.at[:, 0].set(SCREAMING_SNAKE_CASE__					)

											# min tokens to keep
											SCREAMING_SNAKE_CASE__		: Tuple     =     score_mask.at[:, : self.min_tokens_to_keep].set(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     jnp.where(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     jax.lax.sort_key_val(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)[-1]

											return next_scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = -float("""Inf"""					)     , SCREAMING_SNAKE_CASE__ = 1					)      ->	int:



											"""simple docstring"""




											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     max(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     filter_value



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     scores.shape
											SCREAMING_SNAKE_CASE__		: int     =     jnp.full(batch_size * vocab_size     , self.filter_value					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     min(self.top_k     , scores.shape[-1]					)  # Safety check
											SCREAMING_SNAKE_CASE__		: List[str]     =     lax.top_k(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     jnp.broadcast_to((jnp.arange(SCREAMING_SNAKE_CASE__					) * vocab_size)[:, None]     , (batch_size, topk)					).flatten()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     topk_scores.flatten()
											SCREAMING_SNAKE_CASE__		: str     =     topk_indices.flatten() + shift

											SCREAMING_SNAKE_CASE__		: Tuple     =     next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     next_scores_flat.reshape(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											return next_scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     bos_token_id



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     jnp.full(scores.shape     , -float("""inf"""					)					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     1 - jnp.bool_(cur_len - 1					)

											SCREAMING_SNAKE_CASE__		: int     =     jnp.where(SCREAMING_SNAKE_CASE__     , new_scores.at[:, self.bos_token_id].set(0					)     , SCREAMING_SNAKE_CASE__					)

											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     max_length
											SCREAMING_SNAKE_CASE__		: int     =     eos_token_id



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     jnp.full(scores.shape     , -float("""inf"""					)					)

											SCREAMING_SNAKE_CASE__		: List[Any]     =     1 - jnp.bool_(cur_len - self.max_length + 1					)

											SCREAMING_SNAKE_CASE__		: List[Any]     =     jnp.where(SCREAMING_SNAKE_CASE__     , new_scores.at[:, self.eos_token_id].set(0					)     , SCREAMING_SNAKE_CASE__					)

											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''					)

											if not isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) or eos_token_id < 0:
																		raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     min_length
											SCREAMING_SNAKE_CASE__		: str     =     eos_token_id



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     1 - jnp.clip(cur_len - self.min_length     , 0     , 1					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     jnp.where(SCREAMING_SNAKE_CASE__     , scores.at[:, self.eos_token_id].set(-float("""inf"""					)					)     , SCREAMING_SNAKE_CASE__					)

											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     begin_index



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     1 - jnp.bool_(cur_len - self.begin_index					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     jnp.where(SCREAMING_SNAKE_CASE__     , scores.at[:, self.begin_suppress_tokens].set(-float("""inf"""					)					)     , SCREAMING_SNAKE_CASE__					)

											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     list(SCREAMING_SNAKE_CASE__					)



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     scores.at[..., self.suppress_tokens].set(-float("""inf"""					)					)

											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dict(SCREAMING_SNAKE_CASE__					)
											# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
											# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
											# Indexes without forced tokens will have a negative value.
											SCREAMING_SNAKE_CASE__		: int     =     jnp.ones((max(force_token_map.keys()					) + 1)     , dtype=jnp.intaa					) * -1
											for index, token in force_token_map.items():
																		if token is not None:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     force_token_array.at[index].set(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     jnp.intaa(SCREAMING_SNAKE_CASE__					)



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	jnp.ndarray:



											"""simple docstring"""




											def _force_token(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: List[str]     =     scores.shape[0]
																		SCREAMING_SNAKE_CASE__		: Dict     =     self.force_token_array[generation_idx]

																		SCREAMING_SNAKE_CASE__		: int     =     jnp.ones_like(SCREAMING_SNAKE_CASE__     , dtype=scores.dtype					) * -float("""inf"""					)
																		SCREAMING_SNAKE_CASE__		: str     =     jnp.zeros((batch_size, 1)     , dtype=scores.dtype					)
																		SCREAMING_SNAKE_CASE__		: Tuple     =     lax.dynamic_update_slice(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , (0, current_token)					)
																		return new_scores

											SCREAMING_SNAKE_CASE__		: List[Any]     =     lax.cond(
											    cur_len >= self.force_token_array.shape[0]     , lambda: scores     , lambda: lax.cond(
											        self.force_token_array[cur_len] >= 0     , lambda: _force_token(SCREAMING_SNAKE_CASE__					)     , lambda: scores     , )     , )
											return scores






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     generate_config.eos_token_id
											SCREAMING_SNAKE_CASE__		: Any     =     generate_config.no_timestamps_token_id
											SCREAMING_SNAKE_CASE__		: List[str]     =     generate_config.no_timestamps_token_id + 1

											SCREAMING_SNAKE_CASE__		: Any     =     decoder_input_length + 1

											if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
											if hasattr(SCREAMING_SNAKE_CASE__     , """max_initial_timestamp_index"""					):
																		SCREAMING_SNAKE_CASE__		: str     =     generate_config.max_initial_timestamp_index
											else:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model_config.vocab_size
											if self.max_initial_timestamp_index is None:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     model_config.vocab_size



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     scores.at[:, self.no_timestamps_token_id].set(-float("""inf"""					)					)

											def handle_pairs(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: int     =     jnp.where((cur_len - self.begin_index) >= 1     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin     , True and last_was_timestamp     , SCREAMING_SNAKE_CASE__     , )

																		SCREAMING_SNAKE_CASE__		: int     =     jnp.where((cur_len - self.begin_index) < 2     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , )

																		return jnp.where(
																		    SCREAMING_SNAKE_CASE__     , jnp.where(
																		        penultimate_was_timestamp > 0     , scores_k.at[self.timestamp_begin :].set(-float("""inf"""					)					)     , scores_k.at[: self.eos_token_id].set(-float("""inf"""					)					)     , )     , SCREAMING_SNAKE_CASE__     , )

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     jax.vmap(SCREAMING_SNAKE_CASE__					)(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: List[str]     =     jnp.where(cur_len == self.begin_index     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     jnp.where(
											    self.max_initial_timestamp_index is not None     , True and apply_max_initial_timestamp     , SCREAMING_SNAKE_CASE__     , )

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.timestamp_begin + self.max_initial_timestamp_index

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     jnp.where(
											    SCREAMING_SNAKE_CASE__     , scores.at[:, last_allowed + 1 :].set(-float("""inf"""					)					)     , SCREAMING_SNAKE_CASE__     , )

											# if sum of probability over timestamps is above any other token, sample timestamp
											SCREAMING_SNAKE_CASE__		: int     =     jax.nn.log_softmax(SCREAMING_SNAKE_CASE__     , axis=-1					)

											def handle_cumulative_probs(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Any     =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]     , axis=-1					)
																		SCREAMING_SNAKE_CASE__		: str     =     jnp.max(logprobs_k[: self.timestamp_begin]					)
																		return jnp.where(
																		    timestamp_logprob > max_text_token_logprob     , scores_k.at[: self.timestamp_begin].set(-float("""inf"""					)					)     , SCREAMING_SNAKE_CASE__     , )

											SCREAMING_SNAKE_CASE__		: int     =     jax.vmap(SCREAMING_SNAKE_CASE__					)(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											return scores






"""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__					)




