"""simple docstring"""






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     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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

       lowercase__:   Dict					=      (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
       lowercase__:   List[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:
          lowercase__:   Optional[Any]					=      AdaLayerNorm(UpperCamelCase__    ,			UpperCamelCase__							)
       elif self.use_ada_layer_norm_zero:
          lowercase__:   Tuple					=      AdaLayerNormZero(UpperCamelCase__    ,			UpperCamelCase__							)
       else:
          lowercase__:   int					=      nn.LayerNorm(UpperCamelCase__    ,			elementwise_affine=UpperCamelCase__							)
       lowercase__:   List[Any]					=      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.
          lowercase__:   Tuple					=      (
              AdaLayerNorm(UpperCamelCase__    ,			UpperCamelCase__							)
              if self.use_ada_layer_norm
              else nn.LayerNorm(UpperCamelCase__    ,			elementwise_affine=UpperCamelCase__							)
          )
          lowercase__:   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:
          lowercase__:   Optional[int]					=      None
          lowercase__:   Any					=      None

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

       # let chunk size default to None
       lowercase__:   Tuple					=      None
       lowercase__:   Optional[Any]					=      0






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      chunk_size
       lowercase__:   Any					=      dim






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):



       if self.use_ada_layer_norm:
          lowercase__:   Union[str, Any]					=      self.norma(UpperCamelCase__    ,			UpperCamelCase__							)
       elif self.use_ada_layer_norm_zero:
          lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      self.norma(
              UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			hidden_dtype=hidden_states.dtype							)
       else:
          lowercase__:   int					=      self.norma(UpperCamelCase__							)

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

       lowercase__:   int					=      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:
          lowercase__:   Tuple					=      gate_msa.unsqueeze(1							) * attn_output
       lowercase__:   Optional[Any]					=      attn_output + hidden_states

       # 2. Cross-Attention
       if self.attna is not None:
          lowercase__:   Tuple					=      (
              self.norma(UpperCamelCase__    ,			UpperCamelCase__							) if self.use_ada_layer_norm else self.norma(UpperCamelCase__							)
          )

          lowercase__:   Tuple					=      self.attna(
              UpperCamelCase__    ,			encoder_hidden_states=UpperCamelCase__    ,			attention_mask=UpperCamelCase__    ,			**UpperCamelCase__    ,			)
          lowercase__:   Optional[int]					=      attn_output + hidden_states

       # 3. Feed-forward
       lowercase__:   Optional[int]					=      self.norma(UpperCamelCase__							)

       if self.use_ada_layer_norm_zero:
          lowercase__:   Dict					=      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__:   str					=      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
          lowercase__:   Dict					=      torch.cat(
              [self.ff(UpperCamelCase__							) for hid_slice in norm_hidden_states.chunk(UpperCamelCase__    ,			dim=self._chunk_dim							)]    ,			dim=self._chunk_dim    ,			)
       else:
          lowercase__:   Optional[int]					=      self.ff(UpperCamelCase__							)

       if self.use_ada_layer_norm_zero:
          lowercase__:   Optional[int]					=      gate_mlp.unsqueeze(1							) * ff_output

       lowercase__:   Any					=      ff_output + hidden_states

       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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

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

       lowercase__:   Union[str, Any]					=      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 _snake_case			(   self    ,			_UpperCAmelCase							):



       for module in self.net:
          lowercase__:   Dict					=      module(UpperCamelCase__							)
       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = "none"							):
       super().__init__()
       lowercase__:   int					=      nn.Linear(UpperCamelCase__    ,			UpperCamelCase__							)
       lowercase__:   Union[str, Any]					=      approximate






    def _snake_case			(   self    ,			_UpperCAmelCase							):



       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 _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Union[str, Any]					=      self.proj(UpperCamelCase__							)
       lowercase__:   List[Any]					=      self.gelu(UpperCamelCase__							)
       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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






    def _snake_case			(   self    ,			_UpperCAmelCase							):



       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 _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__, lowercase__:   List[str]					=      self.proj(UpperCamelCase__							).chunk(2    ,			dim=-1							)
       return hidden_states * self.gelu(UpperCamelCase__							)



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   Any					=      nn.Linear(UpperCamelCase__    ,			UpperCamelCase__							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      self.proj(UpperCamelCase__							)
       return x * torch.sigmoid(1.702 * x							)



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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






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



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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

       lowercase__:   Any					=      CombinedTimestepLabelEmbeddings(UpperCamelCase__    ,			UpperCamelCase__							)

       lowercase__:   Union[str, Any]					=      nn.SiLU()
       lowercase__:   Tuple					=      nn.Linear(UpperCamelCase__    ,			6 * embedding_dim    ,			bias=UpperCamelCase__							)
       lowercase__:   Tuple					=      nn.LayerNorm(UpperCamelCase__    ,			elementwise_affine=UpperCamelCase__    ,			eps=1e-6							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   int					=      self.linear(self.silu(self.emb(UpperCamelCase__    ,			UpperCamelCase__    ,			hidden_dtype=UpperCamelCase__							)							)							)
       lowercase__, lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   List[Any]					=      emb.chunk(6    ,			dim=1							)
       lowercase__:   Dict					=      self.norm(UpperCamelCase__							) * (1 + scale_msa[:, None]) + shift_msa[:, None]
       return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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

       if act_fn is None:
          lowercase__:   List[Any]					=      None
       else:
          lowercase__:   Optional[Any]					=      get_activation(UpperCamelCase__							)

       lowercase__:   Any					=      nn.Linear(UpperCamelCase__    ,			out_dim * 2							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):



       if self.act:
          lowercase__:   Optional[int]					=      self.act(UpperCamelCase__							)
       lowercase__:   List[str]					=      self.linear(UpperCamelCase__							)
       lowercase__:   Tuple					=      emb[:, :, None, None]
       lowercase__, lowercase__:   Union[str, Any]					=      emb.chunk(2    ,			dim=1							)

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






import argparse
from collections import defaultdict

import yaml


_A     =			'''docs/source/en/_toctree.yml'''





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  defaultdict(_A  )
						for doc in model_doc:
												counts[doc["local"]] += 1
						lowerCAmelCase_      			=  [key for key, value in counts.items() if value > 1]

						lowerCAmelCase_      			=  []
						for duplicate_key in duplicates:
												lowerCAmelCase_      			=  list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}  )
												if len(_A  ) > 1:
																		raise ValueError(
																		    f"{duplicate_key} is present several times in the documentation table of content at "
																		    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																		    '''others.'''  )
												# Only add this once
												new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}  )

						# Add none duplicate-keys
						new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]  )

						# Sort
						return sorted(_A ,  key=lambda _A  : s["title"].lower()  )





def __UpperCamelCase			(       _A=False  ):
						with open(_A ,  encoding='''utf-8'''  ) as f:
												lowerCAmelCase_      			=  yaml.safe_load(f.read()  )

						# Get to the API doc
						lowerCAmelCase_      			=  0
						while content[api_idx]["title"] != "API":
												api_idx += 1
						lowerCAmelCase_      			=  content[api_idx]['''sections''']

						# Then to the model doc
						lowerCAmelCase_      			=  0
						while api_doc[model_idx]["title"] != "Models":
												model_idx += 1

						lowerCAmelCase_      			=  api_doc[model_idx]['''sections''']

						lowerCAmelCase_      			=  [(idx, section) for idx, section in enumerate(_A  ) if '''sections''' in section]
						lowerCAmelCase_      			=  False
						for idx, modality_doc in modalities_docs:
												lowerCAmelCase_      			=  modality_doc['''sections''']
												lowerCAmelCase_      			=  clean_model_doc_toc(_A  )

												if old_modality_doc != new_modality_doc:
																		lowerCAmelCase_      			=  True
																		if overwrite:
																								lowerCAmelCase_      			=  new_modality_doc

						if diff:
												if overwrite:
																		lowerCAmelCase_      			=  model_doc
																		lowerCAmelCase_      			=  api_doc
																		with open(_A ,  '''w''' ,  encoding='''utf-8'''  ) as f:
																								f.write(yaml.dump(_A ,  allow_unicode=_A  )  )
												else:
																		raise ValueError(
																		    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						_A     =			parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




