from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 16				,				A_ = 88				,				A_ = None				,				A_ = None				,				A_ = 1				,				A_ = 0.0				,				A_ = 32				,				A_ = None				,				A_ = False				,				A_ = None				,				A_ = "geglu"				,				A_ = True				,				A_ = True				,				) ->				List[str]:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				num_attention_heads
           _lowerCamelCase			    =				attention_head_dim
           _lowerCamelCase			    =				num_attention_heads * attention_head_dim

           _lowerCamelCase			    =				in_channels

           _lowerCamelCase			    =				torch.nn.GroupNorm(num_groups=A_				,				num_channels=A_				,				eps=1E-6				,				affine=A_						)
           _lowerCamelCase			    =				nn.Linear(A_				,				A_						)

           # 3. Define transformers blocks
           _lowerCamelCase			    =				nn.ModuleList(
               [
                   BasicTransformerBlock(
                       A_				,				A_				,				A_				,				dropout=A_				,				cross_attention_dim=A_				,				activation_fn=A_				,				attention_bias=A_				,				double_self_attention=A_				,				norm_elementwise_affine=A_				,				)
                   for d in range(A_						)
               ]						)

           _lowerCamelCase			    =				nn.Linear(A_				,				A_						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=None				,				A_=1				,				A_=None				,				A_ = True				,				) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				hidden_states.shape
           _lowerCamelCase			    =				batch_frames // num_frames

           _lowerCamelCase			    =				hidden_states

           _lowerCamelCase			    =				hidden_states[None, :].reshape(A_				,				A_				,				A_				,				A_				,				A_						)
           _lowerCamelCase			    =				hidden_states.permute(0				,				2				,				1				,				3				,				4						)

           _lowerCamelCase			    =				self.norm(A_						)
           _lowerCamelCase			    =				hidden_states.permute(0				,				3				,				4				,				2				,				1						).reshape(batch_size * height * width				,				A_				,				A_						)

           _lowerCamelCase			    =				self.proj_in(A_						)

           # 2. Blocks
           for block in self.transformer_blocks:
                 _lowerCamelCase			    =				block(
                     A_				,				encoder_hidden_states=A_				,				timestep=A_				,				cross_attention_kwargs=A_				,				class_labels=A_				,				)

           # 3. Output
           _lowerCamelCase			    =				self.proj_out(A_						)
           _lowerCamelCase			    =				(
               hidden_states[None, None, :]
               .reshape(A_				,				A_				,				A_				,				A_				,				A_						)
               .permute(0				,				3				,				4				,				1				,				2						)
               .contiguous()
           )
           _lowerCamelCase			    =				hidden_states.reshape(A_				,				A_				,				A_				,				A_						)

           _lowerCamelCase			    =				hidden_states + residual

           if not return_dict:
                 return (output,)

           return TransformerTemporalModelOutput(sample=A_						)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)