"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_A   					=    logging.get_logger(__name__)

_A   					=    {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"blenderbot-small"
					a   	=		["past_key_values"]
					a   	=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



					def __init__(						self			:				Any   , _snake_case			:				str=50265   , _snake_case			:				int=512   , _snake_case			:				Tuple=8   , _snake_case			:				Optional[int]=2048   , _snake_case			:				List[str]=16   , _snake_case			:				Any=8   , _snake_case			:				Optional[int]=2048   , _snake_case			:				Optional[Any]=16   , _snake_case			:				Any=0.0   , _snake_case			:				Any=0.0   , _snake_case			:				Optional[int]=True   , _snake_case			:				List[Any]=True   , _snake_case			:				Optional[int]="gelu"   , _snake_case			:				Optional[int]=512   , _snake_case			:				Optional[Any]=0.1   , _snake_case			:				List[Any]=0.0   , _snake_case			:				str=0.0   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Any=1   , _snake_case			:				Optional[Any]=False   , _snake_case			:				Any=0   , _snake_case			:				List[str]=1   , _snake_case			:				Tuple=2   , _snake_case			:				Union[str, Any]=2   , **_snake_case			:				Any   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				d_model
									SCREAMING_SNAKE_CASE__		  =				encoder_ffn_dim
									SCREAMING_SNAKE_CASE__		  =				encoder_layers
									SCREAMING_SNAKE_CASE__		  =				encoder_attention_heads
									SCREAMING_SNAKE_CASE__		  =				decoder_ffn_dim
									SCREAMING_SNAKE_CASE__		  =				decoder_layers
									SCREAMING_SNAKE_CASE__		  =				decoder_attention_heads
									SCREAMING_SNAKE_CASE__		  =				dropout
									SCREAMING_SNAKE_CASE__		  =				attention_dropout
									SCREAMING_SNAKE_CASE__		  =				activation_dropout
									SCREAMING_SNAKE_CASE__		  =				activation_function
									SCREAMING_SNAKE_CASE__		  =				init_std
									SCREAMING_SNAKE_CASE__		  =				encoder_layerdrop
									SCREAMING_SNAKE_CASE__		  =				decoder_layerdrop
									SCREAMING_SNAKE_CASE__		  =				use_cache
									SCREAMING_SNAKE_CASE__		  =				encoder_layers
									SCREAMING_SNAKE_CASE__		  =				scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=_snake_case   , bos_token_id=_snake_case   , eos_token_id=_snake_case   , is_encoder_decoder=_snake_case   , decoder_start_token_id=_snake_case   , forced_eos_token_id=_snake_case   , **_snake_case   , )



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]		)

													if self.use_past:
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch"}
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "decoder_sequence"}
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																	self.fill_with_past_key_values_(_snake_case   , direction="inputs"		)
									elif self.task == "causal-lm":
													# TODO: figure this case out.
													SCREAMING_SNAKE_CASE__		  =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]		)
													if self.use_past:
																	SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
																	for i in range(_snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
									else:
													SCREAMING_SNAKE_CASE__		  =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
													        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
													    ]		)

									return common_inputs



					@property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				super().outputs
									else:
													SCREAMING_SNAKE_CASE__		  =				super(_snake_case   , self		).outputs
													if self.use_past:
																	SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
																	for i in range(_snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
									return common_outputs



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)

									# Generate decoder inputs
									SCREAMING_SNAKE_CASE__		  =				seq_length if not self.use_past else 1
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									SCREAMING_SNAKE_CASE__		  =				dict(**_snake_case   , **_snake_case		)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
													else:
																	import torch
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				common_inputs["input_ids"].shape
													SCREAMING_SNAKE_CASE__		  =				common_inputs["decoder_input_ids"].shape[1]
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_attention_heads
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													SCREAMING_SNAKE_CASE__		  =				decoder_seq_length + 3
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(_snake_case   , _snake_case		)]   , dim=1		)

													SCREAMING_SNAKE_CASE__		  =				[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
													SCREAMING_SNAKE_CASE__		  =				min(_snake_case   , _snake_case		)
													SCREAMING_SNAKE_CASE__		  =				max(_snake_case   , _snake_case		) - min_num_layers
													SCREAMING_SNAKE_CASE__		  =				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

													for _ in range(_snake_case		):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(_snake_case		),
																	        torch.zeros(_snake_case		),
																	        torch.zeros(_snake_case		),
																	        torch.zeros(_snake_case		),
																	    )		)
													# TODO: test this.
													SCREAMING_SNAKE_CASE__		  =				encoder_shape if remaining_side_name == "encoder" else decoder_shape
													for _ in range(_snake_case   , _snake_case		):
																	common_inputs["past_key_values"].append((torch.zeros(_snake_case		), torch.zeros(_snake_case		))		)
									return common_inputs



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
													else:
																	import torch
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													SCREAMING_SNAKE_CASE__		  =				seqlen + 2
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_attention_heads
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													SCREAMING_SNAKE_CASE__		  =				common_inputs["attention_mask"].dtype
													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [common_inputs["attention_mask"], torch.ones(_snake_case   , _snake_case   , dtype=_snake_case		)]   , dim=1		)
													SCREAMING_SNAKE_CASE__		  =				[
													    (torch.zeros(_snake_case		), torch.zeros(_snake_case		)) for _ in range(_snake_case		)
													]
									return common_inputs



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE__		  =				compute_effective_axis_dimension(
									    _snake_case   , fixed_dimension=OnnxConfig.default_fixed_batch   , num_token_to_add=0		)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE__		  =				tokenizer.num_special_tokens_to_add(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				compute_effective_axis_dimension(
									    _snake_case   , fixed_dimension=OnnxConfig.default_fixed_sequence   , num_token_to_add=_snake_case		)

									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE__		  =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE__		  =				dict(tokenizer(_snake_case   , return_tensors=_snake_case		)		)
									return common_inputs



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)

									elif self.task == "causal-lm":
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_causal_lm(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)

									return common_inputs



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[int]		)							->       Union[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				super()._flatten_past_key_values_(_snake_case   , _snake_case   , _snake_case   , _snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				super(_snake_case   , self		)._flatten_past_key_values_(
													    _snake_case   , _snake_case   , _snake_case   , _snake_case		)





"""simple docstring"""






from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				Optional[Any]=0.0   , _snake_case			:				Optional[int] = None   , _snake_case			:				str = "geglu"   , _snake_case			:				Optional[int] = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				bool = True   , _snake_case			:				str = "layer_norm"   , _snake_case			:				bool = False   , )							->       Union[str, Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				only_cross_attention

									SCREAMING_SNAKE_CASE__		  =				(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
									SCREAMING_SNAKE_CASE__		  =				(num_embeds_ada_norm is not None) and norm_type == "ada_norm"

									if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
													raise ValueError(
													    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
													    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""		)

									# Define 3 blocks. Each block has its own normalization layer.
									# 1. Self-Attn
									if self.use_ada_layer_norm:
													SCREAMING_SNAKE_CASE__		  =				AdaLayerNorm(_snake_case   , _snake_case		)
									elif self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				AdaLayerNormZero(_snake_case   , _snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				Attention(
									    query_dim=_snake_case   , heads=_snake_case   , dim_head=_snake_case   , dropout=_snake_case   , bias=_snake_case   , cross_attention_dim=cross_attention_dim if only_cross_attention else None   , upcast_attention=_snake_case   , )

									# 2. Cross-Attn
									if cross_attention_dim is not None or double_self_attention:
													# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
													# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
													# the second cross attention block.
													SCREAMING_SNAKE_CASE__		  =				(
													    AdaLayerNorm(_snake_case   , _snake_case		)
													    if self.use_ada_layer_norm
													    else nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
													)
													SCREAMING_SNAKE_CASE__		  =				Attention(
													    query_dim=_snake_case   , cross_attention_dim=cross_attention_dim if not double_self_attention else None   , heads=_snake_case   , dim_head=_snake_case   , dropout=_snake_case   , bias=_snake_case   , upcast_attention=_snake_case   , )  # is self-attn if encoder_hidden_states is none
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

									# 3. Feed-forward
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				FeedForward(_snake_case   , dropout=_snake_case   , activation_fn=_snake_case   , final_dropout=_snake_case		)

									# let chunk size default to None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				0



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				int		)							->       List[str]:
									# Sets chunk feed-forward
									SCREAMING_SNAKE_CASE__		  =				chunk_size
									SCREAMING_SNAKE_CASE__		  =				dim



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.FloatTensor] = None   , _snake_case			:				Optional[torch.LongTensor] = None   , _snake_case			:				Dict[str, Any] = None   , _snake_case			:				Optional[torch.LongTensor] = None   , )							->       Union[str, Any]:
									# Notice that normalization is always applied before the real computation in the following blocks.
									# 1. Self-Attention
									if self.use_ada_layer_norm:
													SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case   , _snake_case		)
									elif self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.norma(
													    _snake_case   , _snake_case   , _snake_case   , hidden_dtype=hidden_states.dtype		)
									else:
													SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				cross_attention_kwargs if cross_attention_kwargs is not None else {}

									SCREAMING_SNAKE_CASE__		  =				self.attna(
									    _snake_case   , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   , attention_mask=_snake_case   , **_snake_case   , )
									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				gate_msa.unsqueeze(1		) * attn_output
									SCREAMING_SNAKE_CASE__		  =				attn_output + hidden_states

									# 2. Cross-Attention
									if self.attna is not None:
													SCREAMING_SNAKE_CASE__		  =				(
													    self.norma(_snake_case   , _snake_case		) if self.use_ada_layer_norm else self.norma(_snake_case		)
													)

													SCREAMING_SNAKE_CASE__		  =				self.attna(
													    _snake_case   , encoder_hidden_states=_snake_case   , attention_mask=_snake_case   , **_snake_case   , )
													SCREAMING_SNAKE_CASE__		  =				attn_output + hidden_states

									# 3. Feed-forward
									SCREAMING_SNAKE_CASE__		  =				self.norma(_snake_case		)

									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

									if self._chunk_size is not None:
													# "feed_forward_chunk_size" can be used to save memory
													if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																	raise ValueError(
																	    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""		)

													SCREAMING_SNAKE_CASE__		  =				norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [self.ff(_snake_case		) for hid_slice in norm_hidden_states.chunk(_snake_case   , dim=self._chunk_dim		)]   , dim=self._chunk_dim   , )
									else:
													SCREAMING_SNAKE_CASE__		  =				self.ff(_snake_case		)

									if self.use_ada_layer_norm_zero:
													SCREAMING_SNAKE_CASE__		  =				gate_mlp.unsqueeze(1		) * ff_output

									SCREAMING_SNAKE_CASE__		  =				ff_output + hidden_states

									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				int   , _snake_case			:				Optional[int] = None   , _snake_case			:				int = 4   , _snake_case			:				float = 0.0   , _snake_case			:				str = "geglu"   , _snake_case			:				bool = False   , )							->       Optional[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				int(dim * mult		)
									SCREAMING_SNAKE_CASE__		  =				dim_out if dim_out is not None else dim

									if activation_fn == "gelu":
													SCREAMING_SNAKE_CASE__		  =				GELU(_snake_case   , _snake_case		)
									if activation_fn == "gelu-approximate":
													SCREAMING_SNAKE_CASE__		  =				GELU(_snake_case   , _snake_case   , approximate="tanh"		)
									elif activation_fn == "geglu":
													SCREAMING_SNAKE_CASE__		  =				GEGLU(_snake_case   , _snake_case		)
									elif activation_fn == "geglu-approximate":
													SCREAMING_SNAKE_CASE__		  =				ApproximateGELU(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.ModuleList([]		)
									# project in
									self.net.append(_snake_case		)
									# project dropout
									self.net.append(nn.Dropout(_snake_case		)		)
									# project out
									self.net.append(nn.Linear(_snake_case   , _snake_case		)		)
									# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
									if final_dropout:
													self.net.append(nn.Dropout(_snake_case		)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[str]		)							->       str:
									for module in self.net:
													SCREAMING_SNAKE_CASE__		  =				module(_snake_case		)
									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				str = "none"		)							->       Dict:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				approximate



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]		)							->       Tuple:
									if gate.device.type != "mps":
													return F.gelu(_snake_case   , approximate=self.approximate		)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa		)   , approximate=self.approximate		).to(dtype=gate.dtype		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.gelu(_snake_case		)
									return hidden_states



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				int		)							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , dim_out * 2		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]		)							->       Optional[Any]:
									if gate.device.type != "mps":
													return F.gelu(_snake_case		)
									# mps: gelu is not implemented for float16
									return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Tuple		)							->       Any:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		).chunk(2   , dim=-1		)
									return hidden_states * self.gelu(_snake_case		)



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				int   , _snake_case			:				int		)							->       List[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Any		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.proj(_snake_case		)
									return x * torch.sigmoid(1.702 * x		)



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Any		)							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				nn.Embedding(_snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				nn.SiLU()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , embedding_dim * 2		)
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.linear(self.silu(self.emb(_snake_case		)		)		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch.chunk(_snake_case   , 2		)
									SCREAMING_SNAKE_CASE__		  =				self.norm(_snake_case		) * (1 + scale) + shift
									return x



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Any:
									super().__init__()

									SCREAMING_SNAKE_CASE__		  =				CombinedTimestepLabelEmbeddings(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.SiLU()
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , 6 * embedding_dim   , bias=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				nn.LayerNorm(_snake_case   , elementwise_affine=_snake_case   , eps=1e-6		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str=None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.linear(self.silu(self.emb(_snake_case   , _snake_case   , hidden_dtype=_snake_case		)		)		)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.chunk(6   , dim=1		)
									SCREAMING_SNAKE_CASE__		  =				self.norm(_snake_case		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
									return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class lowerCamelCase		(nn.Module      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				int   , _snake_case			:				Optional[str] = None   , _snake_case			:				float = 1e-5		)							->       Optional[Any]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				num_groups
									SCREAMING_SNAKE_CASE__		  =				eps

									if act_fn is None:
													SCREAMING_SNAKE_CASE__		  =				None
									else:
													SCREAMING_SNAKE_CASE__		  =				get_activation(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				nn.Linear(_snake_case   , out_dim * 2		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[Any]		)							->       Optional[int]:
									if self.act:
													SCREAMING_SNAKE_CASE__		  =				self.act(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.linear(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				emb[:, :, None, None]
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.chunk(2   , dim=1		)

									SCREAMING_SNAKE_CASE__		  =				F.group_norm(_snake_case   , self.num_groups   , eps=self.eps		)
									SCREAMING_SNAKE_CASE__		  =				x * (1 + scale) + shift
									return x




