

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        A_		(       nn.Module				):
							def __init__(		self		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			str=0.0   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			str = "geglu"   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			str = "layer_norm"   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,):
								super().__init__()
								__lowerCamelCase				:				List[str]					       = only_cross_attention

								__lowerCamelCase				:				Union[str, Any]					       = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
								__lowerCamelCase				:				str					       = (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:
									__lowerCamelCase				:				List[str]					       = AdaLayerNorm(_UpperCamelCase   ,_UpperCamelCase)
								elif self.use_ada_layer_norm_zero:
									__lowerCamelCase				:				str					       = AdaLayerNormZero(_UpperCamelCase   ,_UpperCamelCase)
								else:
									__lowerCamelCase				:				Optional[int]					       = nn.LayerNorm(_UpperCamelCase   ,elementwise_affine=_UpperCamelCase)
								__lowerCamelCase				:				List[str]					       = Attention(
								    query_dim=_UpperCamelCase   ,heads=_UpperCamelCase   ,dim_head=_UpperCamelCase   ,dropout=_UpperCamelCase   ,bias=_UpperCamelCase   ,cross_attention_dim=cross_attention_dim if only_cross_attention else None   ,upcast_attention=_UpperCamelCase   ,)

								# 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.
									__lowerCamelCase				:				Any					       = (
									    AdaLayerNorm(_UpperCamelCase   ,_UpperCamelCase)
									    if self.use_ada_layer_norm
									    else nn.LayerNorm(_UpperCamelCase   ,elementwise_affine=_UpperCamelCase)
									)
									__lowerCamelCase				:				Optional[Any]					       = Attention(
									    query_dim=_UpperCamelCase   ,cross_attention_dim=cross_attention_dim if not double_self_attention else None   ,heads=_UpperCamelCase   ,dim_head=_UpperCamelCase   ,dropout=_UpperCamelCase   ,bias=_UpperCamelCase   ,upcast_attention=_UpperCamelCase   ,)  # is self-attn if encoder_hidden_states is none
								else:
									__lowerCamelCase				:				Union[str, Any]					       = None
									__lowerCamelCase				:				str					       = None

								# 3. Feed-forward
								__lowerCamelCase				:				Tuple					       = nn.LayerNorm(_UpperCamelCase   ,elementwise_affine=_UpperCamelCase)
								__lowerCamelCase				:				Optional[Any]					       = FeedForward(_UpperCamelCase   ,dropout=_UpperCamelCase   ,activation_fn=_UpperCamelCase   ,final_dropout=_UpperCamelCase)

								# let chunk size default to None
								__lowerCamelCase				:				Any					       = None
								__lowerCamelCase				:				Optional[Any]					       = 0
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			int):
								# Sets chunk feed-forward
								__lowerCamelCase				:				Tuple					       = chunk_size
								__lowerCamelCase				:				int					       = dim


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

								__lowerCamelCase				:				List[Any]					       = cross_attention_kwargs if cross_attention_kwargs is not None else {}

								__lowerCamelCase				:				Tuple					       = self.attna(
								    _UpperCamelCase   ,encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   ,attention_mask=_UpperCamelCase   ,**_UpperCamelCase   ,)
								if self.use_ada_layer_norm_zero:
									__lowerCamelCase				:				List[Any]					       = gate_msa.unsqueeze(1) * attn_output
								__lowerCamelCase				:				Dict					       = attn_output + hidden_states

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

									__lowerCamelCase				:				str					       = self.attna(
									    _UpperCamelCase   ,encoder_hidden_states=_UpperCamelCase   ,attention_mask=_UpperCamelCase   ,**_UpperCamelCase   ,)
									__lowerCamelCase				:				Optional[Any]					       = attn_output + hidden_states

								# 3. Feed-forward
								__lowerCamelCase				:				List[str]					       = self.norma(_UpperCamelCase)

								if self.use_ada_layer_norm_zero:
									__lowerCamelCase				:				str					       = 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`.")

									__lowerCamelCase				:				Tuple					       = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
									__lowerCamelCase				:				Optional[Any]					       = torch.cat(
									    [self.ff(_UpperCamelCase) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase   ,dim=self._chunk_dim)]   ,dim=self._chunk_dim   ,)
								else:
									__lowerCamelCase				:				Optional[int]					       = self.ff(_UpperCamelCase)

								if self.use_ada_layer_norm_zero:
									__lowerCamelCase				:				List[str]					       = gate_mlp.unsqueeze(1) * ff_output

								__lowerCamelCase				:				Optional[int]					       = ff_output + hidden_states

								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			int = 4   ,SCREAMING_SNAKE_CASE__		:			float = 0.0   ,SCREAMING_SNAKE_CASE__		:			str = "geglu"   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,):
								super().__init__()
								__lowerCamelCase				:				str					       = int(dim * mult)
								__lowerCamelCase				:				Dict					       = dim_out if dim_out is not None else dim

								if activation_fn == "gelu":
									__lowerCamelCase				:				Tuple					       = GELU(_UpperCamelCase   ,_UpperCamelCase)
								if activation_fn == "gelu-approximate":
									__lowerCamelCase				:				Dict					       = GELU(_UpperCamelCase   ,_UpperCamelCase   ,approximate='tanh')
								elif activation_fn == "geglu":
									__lowerCamelCase				:				int					       = GEGLU(_UpperCamelCase   ,_UpperCamelCase)
								elif activation_fn == "geglu-approximate":
									__lowerCamelCase				:				Optional[int]					       = ApproximateGELU(_UpperCamelCase   ,_UpperCamelCase)

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


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								for module in self.net:
									__lowerCamelCase				:				int					       = module(_UpperCamelCase)
								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			str = "none"):
								super().__init__()
								__lowerCamelCase				:				Dict					       = nn.Linear(_UpperCamelCase   ,_UpperCamelCase)
								__lowerCamelCase				:				int					       = approximate
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int):
								if gate.device.type != "mps":
									return F.gelu(_UpperCamelCase   ,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		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								__lowerCamelCase				:				Dict					       = self.proj(_UpperCamelCase)
								__lowerCamelCase				:				List[str]					       = self.gelu(_UpperCamelCase)
								return hidden_states






class        A_		(       nn.Module				):
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								super().__init__()
								__lowerCamelCase				:				Any					       = nn.Linear(_UpperCamelCase   ,dim_out * 2)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Dict):
								if gate.device.type != "mps":
									return F.gelu(_UpperCamelCase)
								# mps: gelu is not implemented for float16
								return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Dict):
								__lowerCamelCase       , __lowerCamelCase				:				Tuple					       = self.proj(_UpperCamelCase).chunk(2   ,dim=-1)
								return hidden_states * self.gelu(_UpperCamelCase)






class        A_		(       nn.Module				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								super().__init__()
								__lowerCamelCase				:				Union[str, Any]					       = nn.Linear(_UpperCamelCase   ,_UpperCamelCase)


							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								__lowerCamelCase				:				Tuple					       = self.proj(_UpperCamelCase)
								return x * torch.sigmoid(1.702 * x)






class        A_		(       nn.Module				):
							def __init__(		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								super().__init__()
								__lowerCamelCase				:				int					       = nn.Embedding(_UpperCamelCase   ,_UpperCamelCase)
								__lowerCamelCase				:				int					       = nn.SiLU()
								__lowerCamelCase				:				Union[str, Any]					       = nn.Linear(_UpperCamelCase   ,embedding_dim * 2)
								__lowerCamelCase				:				Optional[Any]					       = nn.LayerNorm(_UpperCamelCase   ,elementwise_affine=_UpperCamelCase)


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								__lowerCamelCase				:				Optional[Any]					       = self.linear(self.silu(self.emb(_UpperCamelCase)))
								__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = torch.chunk(_UpperCamelCase   ,2)
								__lowerCamelCase				:				Any					       = self.norm(_UpperCamelCase) * (1 + scale) + shift
								return x






class        A_		(       nn.Module				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Any):
								super().__init__()

								__lowerCamelCase				:				Union[str, Any]					       = CombinedTimestepLabelEmbeddings(_UpperCamelCase   ,_UpperCamelCase)

								__lowerCamelCase				:				List[Any]					       = nn.SiLU()
								__lowerCamelCase				:				Union[str, Any]					       = nn.Linear(_UpperCamelCase   ,6 * embedding_dim   ,bias=_UpperCamelCase)
								__lowerCamelCase				:				Union[str, Any]					       = nn.LayerNorm(_UpperCamelCase   ,elementwise_affine=_UpperCamelCase   ,eps=1E-6)


							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=None):
								__lowerCamelCase				:				Union[str, Any]					       = self.linear(self.silu(self.emb(_UpperCamelCase   ,_UpperCamelCase   ,hidden_dtype=_UpperCamelCase)))
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = emb.chunk(6   ,dim=1)
								__lowerCamelCase				:				str					       = self.norm(_UpperCamelCase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
								return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class        A_		(       nn.Module				):
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[str] = None   ,SCREAMING_SNAKE_CASE__		:			float = 1E-5):
								super().__init__()
								__lowerCamelCase				:				Optional[Any]					       = num_groups
								__lowerCamelCase				:				Union[str, Any]					       = eps

								if act_fn is None:
									__lowerCamelCase				:				Any					       = None
								else:
									__lowerCamelCase				:				List[Any]					       = get_activation(_UpperCamelCase)

								__lowerCamelCase				:				str					       = nn.Linear(_UpperCamelCase   ,out_dim * 2)


							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Any):
								if self.act:
									__lowerCamelCase				:				Union[str, Any]					       = self.act(_UpperCamelCase)
								__lowerCamelCase				:				Optional[int]					       = self.linear(_UpperCamelCase)
								__lowerCamelCase				:				Union[str, Any]					       = emb[:, :, None, None]
								__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = emb.chunk(2   ,dim=1)

								__lowerCamelCase				:				List[str]					       = F.group_norm(_UpperCamelCase   ,self.num_groups   ,eps=self.eps)
								__lowerCamelCase				:				Dict					       = x * (1 + scale) + shift
								return x







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)