

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 					__UpperCAmelCase     (       SCREAMING_SNAKE_CASE_						):
   UpperCamelCase        =     4_2






class 					__UpperCAmelCase     (       SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_						):



   @register_to_config
   def __init__(							self							:     Optional[int],		__A							:     int = 3_2,		__A							:     int = 6_4,		__A							:     int = 2_0,		__A							:     int = 7_6_8,		__A							:     int=7_7,		__A							:     Tuple=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       :							Union[str, Any]  = num_attention_heads
     UpperCAmelCase       :							Any  = attention_head_dim
     UpperCAmelCase       :							List[str]  = num_attention_heads * attention_head_dim
     UpperCAmelCase       :							int  = additional_embeddings

     UpperCAmelCase       :							Optional[Any]  = time_embed_dim or inner_dim
     UpperCAmelCase       :							Dict  = embedding_proj_dim or embedding_dim
     UpperCAmelCase       :							Tuple  = clip_embed_dim or embedding_dim

     UpperCAmelCase       :							Union[str, Any]  = Timesteps(_a,		_a,		0						)
     UpperCAmelCase       :							Tuple  = TimestepEmbedding(_a,		_a,		out_dim=_a,		act_fn=_a						)

     UpperCAmelCase       :							int  = nn.Linear(_a,		_a						)

     if embedding_proj_norm_type is None:
       UpperCAmelCase       :							Optional[Any]  = None
     elif embedding_proj_norm_type == "layer":
       UpperCAmelCase       :							Dict  = nn.LayerNorm(_a						)
     else:
       raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''						)

     UpperCAmelCase       :							List[str]  = nn.Linear(_a,		_a						)

     if encoder_hid_proj_type is None:
       UpperCAmelCase       :							List[Any]  = None
     elif encoder_hid_proj_type == "linear":
       UpperCAmelCase       :							List[Any]  = nn.Linear(_a,		_a						)
     else:
       raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''						)

     UpperCAmelCase       :							str  = nn.Parameter(torch.zeros(1,		num_embeddings + additional_embeddings,		_a						)						)

     if added_emb_type == "prd":
       UpperCAmelCase       :							Optional[Any]  = nn.Parameter(torch.zeros(1,		1,		_a						)						)
     elif added_emb_type is None:
       UpperCAmelCase       :							List[Any]  = None
     else:
       raise ValueError(
           F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''						)

     UpperCAmelCase       :							int  = 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       :							List[Any]  = nn.LayerNorm(_a						)
     elif norm_in_type is None:
       UpperCAmelCase       :							Dict  = None
     else:
       raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.'''						)

     UpperCAmelCase       :							List[Any]  = nn.LayerNorm(_a						)

     UpperCAmelCase       :							Tuple  = nn.Linear(_a,		_a						)

     UpperCAmelCase       :							int  = torch.full(
         [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings],		-1_0_0_0_0.0						)
     causal_attention_mask.triu_(1						)
     UpperCAmelCase       :							List[str]  = causal_attention_mask[None, ...]
     self.register_buffer('''causal_attention_mask''',		_a,		persistent=_a						)

     UpperCAmelCase       :							Optional[Any]  = nn.Parameter(torch.zeros(1,		_a						)						)
     UpperCAmelCase       :							List[str]  = nn.Parameter(torch.zeros(1,		_a						)						)



   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def        __magic_name__    (							self							:     Optional[int]						):
     UpperCAmelCase       :							Union[str, Any]  = {}

     def fn_recursive_add_processors(__A							:     str,		__A							:     torch.nn.Module,		__A							:     Dict[str, AttentionProcessor]						):
       if hasattr(_a,		'''set_processor'''						):
         UpperCAmelCase       :							Union[str, Any]  = 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        __magic_name__    (							self							:     int,		__A							:     Union[AttentionProcessor, Dict[str, AttentionProcessor]]						):
     UpperCAmelCase       :							List[str]  = 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							:     int						):
       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        __magic_name__    (							self							:     Tuple						):
     self.set_attn_processor(AttnProcessor()						)



   def        __magic_name__    (							self							:     int,		__A							:     Any,		__A							:     Union[torch.Tensor, float, int],		__A							:     torch.FloatTensor,		__A							:     Optional[torch.FloatTensor] = None,		__A							:     Optional[torch.BoolTensor] = None,		__A							:     bool = True,		):
     UpperCAmelCase       :							List[Any]  = hidden_states.shape[0]

     UpperCAmelCase       :							List[Any]  = timestep
     if not torch.is_tensor(_a						):
       UpperCAmelCase       :							Dict  = torch.tensor([timesteps],		dtype=torch.long,		device=hidden_states.device						)
     elif torch.is_tensor(_a						) and len(timesteps.shape						) == 0:
       UpperCAmelCase       :							Optional[int]  = timesteps[None].to(hidden_states.device						)

     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
     UpperCAmelCase       :							Optional[Any]  = timesteps * torch.ones(_a,		dtype=timesteps.dtype,		device=timesteps.device						)

     UpperCAmelCase       :							List[str]  = 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       :							List[str]  = timesteps_projected.to(dtype=self.dtype						)
     UpperCAmelCase       :							Dict  = self.time_embedding(_a						)

     if self.embedding_proj_norm is not None:
       UpperCAmelCase       :							List[str]  = self.embedding_proj_norm(_a						)

     UpperCAmelCase       :							Optional[int]  = self.embedding_proj(_a						)
     if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
       UpperCAmelCase       :							Any  = 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       :							Optional[Any]  = self.proj_in(_a						)

     UpperCAmelCase       :							Union[str, Any]  = self.positional_embedding.to(hidden_states.dtype						)

     UpperCAmelCase       :							Any  = []
     UpperCAmelCase       :							str  = 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       :							int  = proj_embeddings[:, None, :]

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

     UpperCAmelCase       :							Any  = additional_embeds + [
         proj_embeddings,
         time_embeddings[:, None, :],
         hidden_states,
     ]

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

     UpperCAmelCase       :							Optional[Any]  = 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       :							Union[str, Any]  = additional_embeddings_len + proj_embeddings.shape[1] + 1
     if positional_embeddings.shape[1] < hidden_states.shape[1]:
       UpperCAmelCase       :							Any  = 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       :							Tuple  = hidden_states + positional_embeddings

     if attention_mask is not None:
       UpperCAmelCase       :							Any  = (1 - attention_mask.to(hidden_states.dtype						)) * -1_0_0_0_0.0
       UpperCAmelCase       :							List[Any]  = F.pad(_a,		(0, self.additional_embeddings),		value=0.0						)
       UpperCAmelCase       :							int  = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype						)
       UpperCAmelCase       :							List[str]  = attention_mask.repeat_interleave(self.config.num_attention_heads,		dim=0						)

     if self.norm_in is not None:
       UpperCAmelCase       :							List[str]  = self.norm_in(_a						)

     for block in self.transformer_blocks:
       UpperCAmelCase       :							Union[str, Any]  = block(_a,		attention_mask=_a						)

     UpperCAmelCase       :							Union[str, Any]  = self.norm_out(_a						)

     if self.prd_embedding is not None:
       UpperCAmelCase       :							Union[str, Any]  = hidden_states[:, -1]
     else:
       UpperCAmelCase       :							Optional[Any]  = hidden_states[:, additional_embeddings_len:]

     UpperCAmelCase       :							int  = self.proj_to_clip_embeddings(_a						)

     if not return_dict:
       return (predicted_image_embedding,)

     return PriorTransformerOutput(predicted_image_embedding=_a						)





   def        __magic_name__    (							self							:     List[Any],		__A							:     str						):
     UpperCAmelCase       :							Optional[int]  = (prior_latents * self.clip_std) + self.clip_mean
     return prior_latents










'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






