

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 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a						,       a						,       a=0.0						,       a = None						,       a = "geglu"						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = "layer_norm"						,       a = False						,       ):
         super().__init__()
         lowercase__   :       Union[str, Any]				=	only_cross_attention

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

         # 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.
              lowercase__   :       Optional[int]				=	(
                  AdaLayerNorm(a						,       a)
                  if self.use_ada_layer_norm
                  else nn.LayerNorm(a						,       elementwise_affine=a)
              )
              lowercase__   :       Tuple				=	Attention(
                  query_dim=a						,       cross_attention_dim=cross_attention_dim if not double_self_attention else None						,       heads=a						,       dim_head=a						,       dropout=a						,       bias=a						,       upcast_attention=a						,       )  # is self-attn if encoder_hidden_states is none
         else:
              lowercase__   :       Dict				=	None
              lowercase__   :       int				=	None

         # 3. Feed-forward
         lowercase__   :       Union[str, Any]				=	nn.LayerNorm(a						,       elementwise_affine=a)
         lowercase__   :       List[str]				=	FeedForward(a						,       dropout=a						,       activation_fn=a						,       final_dropout=a)

         # let chunk size default to None
         lowercase__   :       Dict				=	None
         lowercase__   :       List[str]				=	0





    def 				snake_case_    (				self						,       a						,       a):
         # Sets chunk feed-forward
         lowercase__   :       List[Any]				=	chunk_size
         lowercase__   :       Union[str, Any]				=	dim





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       ):
         # Notice that normalization is always applied before the real computation in the following blocks.
         # 1. Self-Attention
         if self.use_ada_layer_norm:
              lowercase__   :       List[Any]				=	self.norma(a						,       a)
         elif self.use_ada_layer_norm_zero:
              lowercase__				,			lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	self.norma(
                  a						,       a						,       a						,       hidden_dtype=hidden_states.dtype)
         else:
              lowercase__   :       str				=	self.norma(a)

         lowercase__   :       int				=	cross_attention_kwargs if cross_attention_kwargs is not None else {}

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

         # 2. Cross-Attention
         if self.attna is not None:
              lowercase__   :       Optional[int]				=	(
                  self.norma(a						,       a) if self.use_ada_layer_norm else self.norma(a)
              )

              lowercase__   :       Any				=	self.attna(
                  a						,       encoder_hidden_states=a						,       attention_mask=a						,       **a						,       )
              lowercase__   :       Tuple				=	attn_output + hidden_states

         # 3. Feed-forward
         lowercase__   :       Dict				=	self.norma(a)

         if self.use_ada_layer_norm_zero:
              lowercase__   :       Optional[Any]				=	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`.""")

              lowercase__   :       List[str]				=	norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
              lowercase__   :       Optional[int]				=	torch.cat(
                  [self.ff(a) for hid_slice in norm_hidden_states.chunk(a						,       dim=self._chunk_dim)]						,       dim=self._chunk_dim						,       )
         else:
              lowercase__   :       Optional[Any]				=	self.ff(a)

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

         lowercase__   :       Any				=	ff_output + hidden_states

         return hidden_states



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a = None						,       a = 4						,       a = 0.0						,       a = "geglu"						,       a = False						,       ):
         super().__init__()
         lowercase__   :       Optional[Any]				=	int(dim * mult)
         lowercase__   :       List[Any]				=	dim_out if dim_out is not None else dim

         if activation_fn == "gelu":
              lowercase__   :       str				=	GELU(a						,       a)
         if activation_fn == "gelu-approximate":
              lowercase__   :       Optional[int]				=	GELU(a						,       a						,       approximate='tanh')
         elif activation_fn == "geglu":
              lowercase__   :       Union[str, Any]				=	GEGLU(a						,       a)
         elif activation_fn == "geglu-approximate":
              lowercase__   :       Optional[int]				=	ApproximateGELU(a						,       a)

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





    def 				snake_case_    (				self						,       a):
         for module in self.net:
              lowercase__   :       Any				=	module(a)
         return hidden_states



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a						,       a = "none"):
         super().__init__()
         lowercase__   :       Any				=	nn.Linear(a						,       a)
         lowercase__   :       int				=	approximate





    def 				snake_case_    (				self						,       a):
         if gate.device.type != "mps":
              return F.gelu(a						,       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 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	self.proj(a)
         lowercase__   :       List[Any]				=	self.gelu(a)
         return hidden_states



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a):
         super().__init__()
         lowercase__   :       Optional[Any]				=	nn.Linear(a						,       dim_out * 2)





    def 				snake_case_    (				self						,       a):
         if gate.device.type != "mps":
              return F.gelu(a)
         # mps: gelu is not implemented for float16
         return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)





    def 				snake_case_    (				self						,       a):
         lowercase__				,			lowercase__   :       int				=	self.proj(a).chunk(2						,       dim=-1)
         return hidden_states * self.gelu(a)



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a):
         super().__init__()
         lowercase__   :       Dict				=	nn.Linear(a						,       a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	self.proj(a)
         return x * torch.sigmoid(1.702 * x)



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a):
         super().__init__()
         lowercase__   :       Optional[int]				=	nn.Embedding(a						,       a)
         lowercase__   :       Dict				=	nn.SiLU()
         lowercase__   :       Tuple				=	nn.Linear(a						,       embedding_dim * 2)
         lowercase__   :       List[Any]				=	nn.LayerNorm(a						,       elementwise_affine=a)





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Union[str, Any]				=	self.linear(self.silu(self.emb(a)))
         lowercase__				,			lowercase__   :       str				=	torch.chunk(a						,       2)
         lowercase__   :       Any				=	self.norm(a) * (1 + scale) + shift
         return x



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a):
         super().__init__()

         lowercase__   :       List[Any]				=	CombinedTimestepLabelEmbeddings(a						,       a)

         lowercase__   :       Any				=	nn.SiLU()
         lowercase__   :       Any				=	nn.Linear(a						,       6 * embedding_dim						,       bias=a)
         lowercase__   :       List[Any]				=	nn.LayerNorm(a						,       elementwise_affine=a						,       eps=1e-6)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a=None):
         lowercase__   :       Tuple				=	self.linear(self.silu(self.emb(a						,       a						,       hidden_dtype=a)))
         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Optional[Any]				=	emb.chunk(6						,       dim=1)
         lowercase__   :       Optional[Any]				=	self.norm(a) * (1 + scale_msa[:, None]) + shift_msa[:, None]
         return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class 				SCREAMING_SNAKE_CASE__ (nn.Module					):





    def __init__(				self						,       a						,       a						,       a						,       a = None						,       a = 1e-5):
         super().__init__()
         lowercase__   :       Optional[int]				=	num_groups
         lowercase__   :       Dict				=	eps

         if act_fn is None:
              lowercase__   :       List[str]				=	None
         else:
              lowercase__   :       Dict				=	get_activation(a)

         lowercase__   :       Optional[Any]				=	nn.Linear(a						,       out_dim * 2)





    def 				snake_case_    (				self						,       a						,       a):
         if self.act:
              lowercase__   :       Optional[Any]				=	self.act(a)
         lowercase__   :       Optional[Any]				=	self.linear(a)
         lowercase__   :       Dict				=	emb[:, :, None, None]
         lowercase__				,			lowercase__   :       Union[str, Any]				=	emb.chunk(2						,       dim=1)

         lowercase__   :       List[str]				=	F.group_norm(a						,       self.num_groups						,       eps=self.eps)
         lowercase__   :       int				=	x * (1 + scale) + shift
         return x




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()

       parser.add_argument(
           '''--repo_path''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''image_size''': '''sample_size''',
           '''num_res_blocks''': '''layers_per_block''',
           '''block_channels''': '''block_out_channels''',
           '''down_blocks''': '''down_block_types''',
           '''up_blocks''': '''up_block_types''',
           '''downscale_freq_shift''': '''freq_shift''',
           '''resnet_num_groups''': '''norm_num_groups''',
           '''resnet_act_fn''': '''act_fn''',
           '''resnet_eps''': '''norm_eps''',
           '''num_head_channels''': '''attention_head_dim''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

       with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
              snake_case_        =			reader.read()
              snake_case_        =			json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, '''config.json'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       snake_case_        =			dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            snake_case_        =			config[key]
                            del config[key]

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

