'''simple docstring'''




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_UpperCamelCase				:		Optional[Any]       			=		{5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}



def 						__snake_case (       lowerCAmelCase       :     Tuple			):
 __UpperCAmelCase         =					F"""pretrained_models/{model_name}"""
 if not os.path.isfile(lowerCAmelCase			):
  os.makedirs('pretrained_models'      ,  exist_ok=lowerCAmelCase			)
  __UpperCAmelCase         =					F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
  download_url(lowerCAmelCase      ,  'pretrained_models'			)
 __UpperCAmelCase         =					torch.load(lowerCAmelCase      ,  map_location=lambda lowerCAmelCase      ,  lowerCAmelCase			: storage			)
 return model



def 						__snake_case (       lowerCAmelCase       :     List[Any]			):
 __UpperCAmelCase         =					download_model(pretrained_models[args.image_size]			)

 __UpperCAmelCase         =					state_dict['x_embedder.proj.weight']
 __UpperCAmelCase         =					state_dict['x_embedder.proj.bias']
 state_dict.pop('x_embedder.proj.weight'			)
 state_dict.pop('x_embedder.proj.bias'			)

 for depth in range(28			):
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.0.weight'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.0.bias'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.2.weight'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.2.bias'
  ]
  __UpperCAmelCase         =					state_dict[
      'y_embedder.embedding_table.weight'
  ]

  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.adaLN_modulation.1.weight"""
  ]
  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.adaLN_modulation.1.bias"""
  ]

  __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]      ,  3      ,  dim=0			)
  __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]      ,  3      ,  dim=0			)

  __UpperCAmelCase         =					q
  __UpperCAmelCase         =					q_bias
  __UpperCAmelCase         =					k
  __UpperCAmelCase         =					k_bias
  __UpperCAmelCase         =					v
  __UpperCAmelCase         =					v_bias

  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.attn.proj.weight"""
  ]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.attn.proj.bias"""]

  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc2.bias"""]

  state_dict.pop(F"""blocks.{depth}.attn.qkv.weight"""			)
  state_dict.pop(F"""blocks.{depth}.attn.qkv.bias"""			)
  state_dict.pop(F"""blocks.{depth}.attn.proj.weight"""			)
  state_dict.pop(F"""blocks.{depth}.attn.proj.bias"""			)
  state_dict.pop(F"""blocks.{depth}.mlp.fc1.weight"""			)
  state_dict.pop(F"""blocks.{depth}.mlp.fc1.bias"""			)
  state_dict.pop(F"""blocks.{depth}.mlp.fc2.weight"""			)
  state_dict.pop(F"""blocks.{depth}.mlp.fc2.bias"""			)
  state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.weight"""			)
  state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.bias"""			)

 state_dict.pop('t_embedder.mlp.0.weight'			)
 state_dict.pop('t_embedder.mlp.0.bias'			)
 state_dict.pop('t_embedder.mlp.2.weight'			)
 state_dict.pop('t_embedder.mlp.2.bias'			)
 state_dict.pop('y_embedder.embedding_table.weight'			)

 __UpperCAmelCase         =					state_dict['final_layer.adaLN_modulation.1.weight']
 __UpperCAmelCase         =					state_dict['final_layer.adaLN_modulation.1.bias']
 __UpperCAmelCase         =					state_dict['final_layer.linear.weight']
 __UpperCAmelCase         =					state_dict['final_layer.linear.bias']

 state_dict.pop('final_layer.linear.weight'			)
 state_dict.pop('final_layer.linear.bias'			)
 state_dict.pop('final_layer.adaLN_modulation.1.weight'			)
 state_dict.pop('final_layer.adaLN_modulation.1.bias'			)

 # DiT XL/2
 __UpperCAmelCase         =					TransformeraDModel(
     sample_size=args.image_size // 8      ,  num_layers=28      ,  attention_head_dim=72      ,  in_channels=4      ,  out_channels=8      ,  patch_size=2      ,  attention_bias=lowerCAmelCase      ,  num_attention_heads=16      ,  activation_fn='gelu-approximate'      ,  num_embeds_ada_norm=1000      ,  norm_type='ada_norm_zero'      ,  norm_elementwise_affine=lowerCAmelCase      ,  )
 transformer.load_state_dict(lowerCAmelCase      ,  strict=lowerCAmelCase			)

 __UpperCAmelCase         =					DDIMScheduler(
     num_train_timesteps=1000      ,  beta_schedule='linear'      ,  prediction_type='epsilon'      ,  clip_sample=lowerCAmelCase      ,  )

 __UpperCAmelCase         =					AutoencoderKL.from_pretrained(args.vae_model			)

 __UpperCAmelCase         =					DiTPipeline(transformer=lowerCAmelCase      ,  vae=lowerCAmelCase      ,  scheduler=lowerCAmelCase			)

 if args.save:
  pipeline.save_pretrained(args.checkpoint_path			)


if __name__ == "__main__":
     _UpperCamelCase				:		Dict       			=		argparse.ArgumentParser()

     parser.add_argument(
         '--image_size',
         default=2_56,
         type=int,
         required=False,
         help='Image size of pretrained model, either 256 or 512.',
     )
     parser.add_argument(
         '--vae_model',
         default='stabilityai/sd-vae-ft-ema',
         type=str,
         required=False,
         help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
     )
     parser.add_argument(
         '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
     )
     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
     )

     _UpperCamelCase				:		List[str]       			=		parser.parse_args()
     main(args)




'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Tuple       			=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase				:		int       			=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase				:		List[str]       			=		{
    'allenai/longformer-base-4096': 40_96,
    'allenai/longformer-large-4096': 40_96,
    'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
    'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
    'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  VOCAB_FILES_NAMES
   __lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
   __lowerCamelCase    =  LongformerTokenizer





   def __init__(							self:    Any			,a:    Optional[int]=None			,a:    Optional[int]=None			,a:    int=None			,a:    Any="replace"			,a:    Optional[int]="<s>"			,a:    Any="</s>"			,a:    Optional[int]="</s>"			,a:    Dict="<s>"			,a:    Dict="<unk>"			,a:    int="<pad>"			,a:    List[str]="<mask>"			,a:    Union[str, Any]=False			,a:    Any=True			,**a:    str			,):
    super().__init__(
        a			,a			,tokenizer_file=a			,errors=a			,bos_token=a			,eos_token=a			,sep_token=a			,cls_token=a			,unk_token=a			,pad_token=a			,mask_token=a			,add_prefix_space=a			,trim_offsets=a			,**a			,)

    __UpperCAmelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
    if pre_tok_state.get('add_prefix_space'			,a	) != add_prefix_space:
     __UpperCAmelCase         =					getattr(a			,pre_tok_state.pop('type'	)	)
     __UpperCAmelCase         =					add_prefix_space
     __UpperCAmelCase         =					pre_tok_class(**a	)

    __UpperCAmelCase         =					add_prefix_space

    __UpperCAmelCase         =					'post_processor'
    __UpperCAmelCase         =					getattr(self.backend_tokenizer			,a			,a	)
    if tokenizer_component_instance:
     __UpperCAmelCase         =					json.loads(tokenizer_component_instance.__getstate__()	)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __UpperCAmelCase         =					tuple(state['sep']	)
     if "cls" in state:
      __UpperCAmelCase         =					tuple(state['cls']	)

     __UpperCAmelCase         =					False

     if state.get('add_prefix_space'			,a	) != add_prefix_space:
      __UpperCAmelCase         =					add_prefix_space
      __UpperCAmelCase         =					True

     if state.get('trim_offsets'			,a	) != trim_offsets:
      __UpperCAmelCase         =					trim_offsets
      __UpperCAmelCase         =					True

     if changes_to_apply:
      __UpperCAmelCase         =					getattr(a			,state.pop('type'	)	)
      __UpperCAmelCase         =					component_class(**a	)
      setattr(self.backend_tokenizer			,a			,a	)





   @property
   def 			snake_case	(							self:    List[Any]	):
    if self._mask_token is None:
     if self.verbose:
      logger.error('Using mask_token, but it is not set yet.'	)
     return None
    return str(self._mask_token	)





   @mask_token.setter
   def 			snake_case	(							self:    int			,a:    Optional[int]	):
    __UpperCAmelCase         =					AddedToken(a			,lstrip=a			,rstrip=a	) if isinstance(a			,a	) else value
    __UpperCAmelCase         =					value





   def 			snake_case	(							self:    Optional[int]			,*a:    Dict			,**a:    Dict	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)
    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*a			,**a	)





   def 			snake_case	(							self:    str			,*a:    Dict			,**a:    str	):
    __UpperCAmelCase         =					kwargs.get('is_split_into_words'			,a	)

    assert self.add_prefix_space or not is_split_into_words, (
        f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*a			,**a	)





   def 			snake_case	(							self:    List[str]			,a:    str			,a:    Optional[str] = None	):
    __UpperCAmelCase         =					self._tokenizer.model.save(a			,name=a	)
    return tuple(a	)





   def 			snake_case	(							self:    Tuple			,a:    Dict			,a:    Tuple=None	):
    __UpperCAmelCase         =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 			snake_case	(							self:    int			,a:    List[int]			,a:    Optional[List[int]] = None	):
    __UpperCAmelCase         =					[self.sep_token_id]
    __UpperCAmelCase         =					[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep	) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]



