from dataclasses import dataclass
from typing import Dict, Optional, Union

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     42




class     lowerCAmelCase_     (       __lowercase,  __lowercase ):







							@register_to_config
							def __init__(   self      :     int ,			_A      :     int = 32 ,			_A      :     int = 64 ,			_A      :     int = 20 ,			_A      :     int = 768 ,			_A      :     Union[str, Any]=77 ,			_A      :     Optional[Any]=4 ,			_A      :     float = 0.0 ,			_A      :     str = "silu" ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = "linear" ,			_A      :     Optional[str] = "prd" ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			_A      :     Optional[int] = None ,			):
												super().__init__()
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     attention_head_dim
												_UpperCamelCase			     =     num_attention_heads * attention_head_dim
												_UpperCamelCase			     =     additional_embeddings

												_UpperCamelCase			     =     time_embed_dim or inner_dim
												_UpperCamelCase			     =     embedding_proj_dim or embedding_dim
												_UpperCamelCase			     =     clip_embed_dim or embedding_dim

												_UpperCamelCase			     =     Timesteps(_A ,			_A ,			0		)
												_UpperCamelCase			     =     TimestepEmbedding(_A ,			_A ,			out_dim=_A ,			act_fn=_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if embedding_proj_norm_type is None:
																	_UpperCamelCase			     =     None
												elif embedding_proj_norm_type == "layer":
																	_UpperCamelCase			     =     nn.LayerNorm(_A		)
												else:
																	raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												if encoder_hid_proj_type is None:
																	_UpperCamelCase			     =     None
												elif encoder_hid_proj_type == "linear":
																	_UpperCamelCase			     =     nn.Linear(_A ,			_A		)
												else:
																	raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			num_embeddings + additional_embeddings ,			_A		)		)

												if added_emb_type == "prd":
																	_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			1 ,			_A		)		)
												elif added_emb_type is None:
																	_UpperCamelCase			     =     None
												else:
																	raise ValueError(
																	    F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""		)

												_UpperCamelCase			     =     nn.ModuleList(
												    [
												        BasicTransformerBlock(
												            _A ,			_A ,			_A ,			dropout=_A ,			activation_fn='''gelu''' ,			attention_bias=_A ,			)
												        for d in range(_A		)
												    ]		)

												if norm_in_type == "layer":
																	_UpperCamelCase			     =     nn.LayerNorm(_A		)
												elif norm_in_type is None:
																	_UpperCamelCase			     =     None
												else:
																	raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""		)

												_UpperCamelCase			     =     nn.LayerNorm(_A		)

												_UpperCamelCase			     =     nn.Linear(_A ,			_A		)

												_UpperCamelCase			     =     torch.full(
												    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] ,			-1_0000.0		)
												causal_attention_mask.triu_(1		)
												_UpperCamelCase			     =     causal_attention_mask[None, ...]
												self.register_buffer('''causal_attention_mask''' ,			_A ,			persistent=_A		)

												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)
												_UpperCamelCase			     =     nn.Parameter(torch.zeros(1 ,			_A		)		)







							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     {}

												def fn_recursive_add_processors(_A      :     str ,			_A      :     torch.nn.Module ,			_A      :     Dict[str, AttentionProcessor]		):
																	if hasattr(_A ,			'''set_processor'''		):
																						_UpperCamelCase			     =     module.processor

																	for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F"""{name}.{sub_name}""" ,			_A ,			_A		)

																	return processors

												for name, module in self.named_children():
																	fn_recursive_add_processors(_A ,			_A ,			_A		)

												return processors







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
												_UpperCamelCase			     =     len(self.attn_processors.keys()		)

												if isinstance(_A ,			_A		) and len(_A		) != count:
																	raise ValueError(
																	    F"""A dict of processors was passed, but the number of processors {len(_A		)} does not match the"""
																	    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""		)

												def fn_recursive_attn_processor(_A      :     str ,			_A      :     torch.nn.Module ,			_A      :     List[str]		):
																	if hasattr(_A ,			'''set_processor'''		):
																						if not isinstance(_A ,			_A		):
																											module.set_processor(_A		)
																						else:
																											module.set_processor(processor.pop(F"""{name}.processor"""		)		)

																	for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F"""{name}.{sub_name}""" ,			_A ,			_A		)

												for name, module in self.named_children():
																	fn_recursive_attn_processor(_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												self.set_attn_processor(AttnProcessor()		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     Union[torch.Tensor, float, int] ,			_A      :     torch.FloatTensor ,			_A      :     Optional[torch.FloatTensor] = None ,			_A      :     Optional[torch.BoolTensor] = None ,			_A      :     bool = True ,			):
												_UpperCamelCase			     =     hidden_states.shape[0]

												_UpperCamelCase			     =     timestep
												if not torch.is_tensor(_A		):
																	_UpperCamelCase			     =     torch.tensor([timesteps] ,			dtype=torch.long ,			device=hidden_states.device		)
												elif torch.is_tensor(_A		) and len(timesteps.shape		) == 0:
																	_UpperCamelCase			     =     timesteps[None].to(hidden_states.device		)

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												_UpperCamelCase			     =     timesteps * torch.ones(_A ,			dtype=timesteps.dtype ,			device=timesteps.device		)

												_UpperCamelCase			     =     self.time_proj(_A		)

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might be fp16, so we need to cast here.
												_UpperCamelCase			     =     timesteps_projected.to(dtype=self.dtype		)
												_UpperCamelCase			     =     self.time_embedding(_A		)

												if self.embedding_proj_norm is not None:
																	_UpperCamelCase			     =     self.embedding_proj_norm(_A		)

												_UpperCamelCase			     =     self.embedding_proj(_A		)
												if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	_UpperCamelCase			     =     self.encoder_hidden_states_proj(_A		)
												elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																	raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''		)

												_UpperCamelCase			     =     self.proj_in(_A		)

												_UpperCamelCase			     =     self.positional_embedding.to(hidden_states.dtype		)

												_UpperCamelCase			     =     []
												_UpperCamelCase			     =     0

												if encoder_hidden_states is not None:
																	additional_embeds.append(_A		)
																	additional_embeddings_len += encoder_hidden_states.shape[1]

												if len(proj_embeddings.shape		) == 2:
																	_UpperCamelCase			     =     proj_embeddings[:, None, :]

												if len(hidden_states.shape		) == 2:
																	_UpperCamelCase			     =     hidden_states[:, None, :]

												_UpperCamelCase			     =     additional_embeds + [
												    proj_embeddings,
												    time_embeddings[:, None, :],
												    hidden_states,
												]

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     self.prd_embedding.to(hidden_states.dtype		).expand(_A ,			-1 ,			-1		)
																	additional_embeds.append(_A		)

												_UpperCamelCase			     =     torch.cat(
												    _A ,			dim=1 ,			)

												# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
												_UpperCamelCase			     =     additional_embeddings_len + proj_embeddings.shape[1] + 1
												if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	_UpperCamelCase			     =     F.pad(
																	    _A ,			(
																	        0,
																	        0,
																	        additional_embeddings_len,
																	        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																	    ) ,			value=0.0 ,			)

												_UpperCamelCase			     =     hidden_states + positional_embeddings

												if attention_mask is not None:
																	_UpperCamelCase			     =     (1 - attention_mask.to(hidden_states.dtype		)) * -1_0000.0
																	_UpperCamelCase			     =     F.pad(_A ,			(0, self.additional_embeddings) ,			value=0.0		)
																	_UpperCamelCase			     =     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
																	_UpperCamelCase			     =     attention_mask.repeat_interleave(self.config.num_attention_heads ,			dim=0		)

												if self.norm_in is not None:
																	_UpperCamelCase			     =     self.norm_in(_A		)

												for block in self.transformer_blocks:
																	_UpperCamelCase			     =     block(_A ,			attention_mask=_A		)

												_UpperCamelCase			     =     self.norm_out(_A		)

												if self.prd_embedding is not None:
																	_UpperCamelCase			     =     hidden_states[:, -1]
												else:
																	_UpperCamelCase			     =     hidden_states[:, additional_embeddings_len:]

												_UpperCamelCase			     =     self.proj_to_clip_embeddings(_A		)

												if not return_dict:
																	return (predicted_image_embedding,)

												return PriorTransformerOutput(predicted_image_embedding=_A		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     (prior_latents * self.clip_std) + self.clip_mean
												return prior_latents




'''simple docstring'''
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class 					__lowerCAmelCase   (   unittest.TestCase				,			lowercase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		load_tool("text-classification"	)
													self.tool.setup()
													A_			    =		load_tool("text-classification" ,  remote=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.remote_tool("That's quite cool" ,  ["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.remote_tool(text="That's quite cool" ,  labels=["positive", "negative"]	)
													self.assertEqual(lowerCAmelCase ,  "positive"	)





