
"""simple docstring"""




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       __magic_name__							(							a__			):
       UpperCamelCase_ =		42


class       __magic_name__							(							a__ ,       a__			):
       @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			,					)  ->						Union[str, Any]:





         """simple docstring"""




         super().__init__()
         _lowercase:      int   			=  num_attention_heads
         _lowercase:      List[str]   			=  attention_head_dim
         _lowercase:      Tuple   			=  num_attention_heads * attention_head_dim

         _lowercase:      Any   			=  in_channels

         _lowercase:      int   			=  torch.nn.GroupNorm(num_groups=__a			,					num_channels=__a			,					eps=1E-6			,					affine=__a							)
         _lowercase:      List[str]   			=  nn.Linear(__a			,					__a							)

         # 3. Define transformers blocks
         _lowercase:      Optional[int]   			=  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							)
             ]							)

         _lowercase:      Tuple   			=  nn.Linear(__a			,					__a							)



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





         """simple docstring"""




         _lowercase:      int   			=  hidden_states.shape
         _lowercase:      Union[str, Any]   			=  batch_frames // num_frames

         _lowercase:      int   			=  hidden_states

         _lowercase:      Union[str, Any]   			=  hidden_states[None, :].reshape(__a			,					__a			,					__a			,					__a			,					__a							)
         _lowercase:      Tuple   			=  hidden_states.permute(0			,					2			,					1			,					3			,					4							)

         _lowercase:      str   			=  self.norm(__a							)
         _lowercase:      List[str]   			=  hidden_states.permute(0			,					3			,					4			,					2			,					1							).reshape(batch_size * height * width			,					__a			,					__a							)

         _lowercase:      str   			=  self.proj_in(__a							)

         # 2. Blocks
         for block in self.transformer_blocks:
           _lowercase:      Optional[Any]   			=  block(
               __a			,					encoder_hidden_states=__a			,					timestep=__a			,					cross_attention_kwargs=__a			,					class_labels=__a			,					)

         # 3. Output
         _lowercase:      List[str]   			=  self.proj_out(__a							)
         _lowercase:      Optional[Any]   			=  (
             hidden_states[None, None, :]
             .reshape(__a			,					__a			,					__a			,					__a			,					__a							)
             .permute(0			,					3			,					4			,					1			,					2							)
             .contiguous()
         )
         _lowercase:      Tuple   			=  hidden_states.reshape(__a			,					__a			,					__a			,					__a							)

         _lowercase:      Union[str, Any]   			=  hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__a							)




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





