

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class 				a  (	nn.Module				):


						def __init__(       self						,					A_ = 16						,					A_ = 88						,					A_ = None						,					A_ = 1						,					A_ = 0.0						,					A_ = 32						,					A_ = None						,					A_ = False						,					A_ = None						,					A_ = None						,					A_ = "geglu"						,					A_ = None						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[str] 		=    nn.ModuleList(
										    [
										        TransformeraDModel(
										            num_attention_heads=A_						,					attention_head_dim=A_						,					in_channels=A_						,					num_layers=A_						,					dropout=A_						,					norm_num_groups=A_						,					cross_attention_dim=A_						,					attention_bias=A_						,					sample_size=A_						,					num_vector_embeds=A_						,					activation_fn=A_						,					num_embeds_ada_norm=A_						,					)
										        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
										_UpperCAmelCase       :		Optional[Any] 		=    0.5

										# The shape of `encoder_hidden_states` is expected to be
										# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
										_UpperCAmelCase       :		Tuple 		=    [77, 257]

										# 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])`
										_UpperCAmelCase       :		Union[str, Any] 		=    [1, 0]





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_=None						,					A_=None						,					A_=None						,					A_ = True						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    hidden_states

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

										_UpperCAmelCase       :		Dict 		=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
										_UpperCAmelCase       :		Dict 		=    output_states + input_states

										if not return_dict:
														return (output_states,)

										return TransformeraDModelOutput(sample=A_	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"openai-gpt"
						_lowercase    						=			{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=40478						,					A_=512						,					A_=768						,					A_=12						,					A_=12						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_="cls_index"						,					A_=True						,					A_=None						,					A_=True						,					A_=0.1						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		str 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Any 		=    n_head
										_UpperCAmelCase       :		int 		=    afn
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Tuple 		=    embd_pdrop
										_UpperCAmelCase       :		Optional[Any] 		=    attn_pdrop
										_UpperCAmelCase       :		Any 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[str] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_type
										_UpperCAmelCase       :		List[Any] 		=    summary_use_proj
										_UpperCAmelCase       :		Optional[Any] 		=    summary_activation
										_UpperCAmelCase       :		int 		=    summary_first_dropout
										_UpperCAmelCase       :		List[str] 		=    summary_proj_to_labels
										super().__init__(**A_	)


