



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 SCREAMING_SNAKE_CASE__	(						SCREAMING_SNAKE_CASE_       ):




    '''simple docstring'''






    __lowerCamelCase     :					Any   = 42






class SCREAMING_SNAKE_CASE__	(						SCREAMING_SNAKE_CASE_					,		SCREAMING_SNAKE_CASE_       ):




    '''simple docstring'''






    @register_to_config
    def __init__(			self,						lowerCamelCase__ = 16,						lowerCamelCase__ = 88,						lowerCamelCase__ = None,						lowerCamelCase__ = None,						lowerCamelCase__ = 1,						lowerCamelCase__ = 0.0,						lowerCamelCase__ = 32,						lowerCamelCase__ = None,						lowerCamelCase__ = False,						lowerCamelCase__ = None,						lowerCamelCase__ = "geglu",						lowerCamelCase__ = True,						lowerCamelCase__ = True,						):
        super().__init__()
        A					:			int		=  num_attention_heads
        A					:			Tuple		=  attention_head_dim
        A					:			str		=  num_attention_heads * attention_head_dim

        A					:			List[str]		=  in_channels

        A					:			List[str]		=  torch.nn.GroupNorm(num_groups=__snake_case,						num_channels=__snake_case,						eps=1e-6,						affine=__snake_case						)
        A					:			Union[str, Any]		=  nn.Linear(__snake_case,						__snake_case						)

        # 3. Define transformers blocks
        A					:			Union[str, Any]		=  nn.ModuleList(
            [
                BasicTransformerBlock(
                    __snake_case,						__snake_case,						__snake_case,						dropout=__snake_case,						cross_attention_dim=__snake_case,						activation_fn=__snake_case,						attention_bias=__snake_case,						double_self_attention=__snake_case,						norm_elementwise_affine=__snake_case,						)
                for d in range(__snake_case						)
            ]						)

        A					:			Optional[Any]		=  nn.Linear(__snake_case,						__snake_case						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__=1,						lowerCamelCase__=None,						lowerCamelCase__ = True,						):
        A					:			int		=  hidden_states.shape
        A					:			Optional[Any]		=  batch_frames // num_frames

        A					:			int		=  hidden_states

        A					:			str		=  hidden_states[None, :].reshape(__snake_case,						__snake_case,						__snake_case,						__snake_case,						__snake_case						)
        A					:			int		=  hidden_states.permute(0,						2,						1,						3,						4						)

        A					:			Union[str, Any]		=  self.norm(__snake_case						)
        A					:			Dict		=  hidden_states.permute(0,						3,						4,						2,						1						).reshape(batch_size * height * width,						__snake_case,						__snake_case						)

        A					:			Optional[Any]		=  self.proj_in(__snake_case						)

        # 2. Blocks
        for block in self.transformer_blocks:
            A					:			Dict		=  block(
                __snake_case,						encoder_hidden_states=__snake_case,						timestep=__snake_case,						cross_attention_kwargs=__snake_case,						class_labels=__snake_case,						)

        # 3. Output
        A					:			List[str]		=  self.proj_out(__snake_case						)
        A					:			int		=  (
            hidden_states[None, None, :]
            .reshape(__snake_case,						__snake_case,						__snake_case,						__snake_case,						__snake_case						)
            .permute(0,						3,						4,						1,						2						)
            .contiguous()
        )
        A					:			str		=  hidden_states.reshape(__snake_case,						__snake_case,						__snake_case,						__snake_case						)

        A					:			List[str]		=  hidden_states + residual

        if not return_dict:
            return (output,)

        return TransformerTemporalModelOutput(sample=__snake_case						)










"""simple docstring"""





from argparse import ArgumentParser

from .env import EnvironmentCommand



def   a    (     )    ->							Tuple:
						__magic_name__:	Optional[int]							=       ArgumentParser("""Diffusers CLI tool"""      ,   usage="""diffusers-cli <command> [<args>]"""  )
						__magic_name__:	Any							=       parser.add_subparsers(help="""diffusers-cli command helpers"""  )

						# Register commands
						EnvironmentCommand.register_subcommand(__UpperCAmelCase  )

						# Let's go
						__magic_name__:	int							=       parser.parse_args()

						if not hasattr(__UpperCAmelCase      ,   """func"""  ):
												parser.print_help()
												exit(1  )

						# Run
						__magic_name__:	Optional[Any]							=       args.func(__UpperCAmelCase  )
						service.run()


if __name__ == "__main__":
							main()



