

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase									= {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			Dict							):





  '''simple docstring'''







  a__  =download_model(pretrained_models[args.image_size]							)

  a__  =state_dict['''x_embedder.proj.weight''']
  a__  =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							):
    a__  =state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    a__  =state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    a__  =state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    a__  =state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    a__  =state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

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

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

    a__  =q
    a__  =q_bias
    a__  =k
    a__  =k_bias
    a__  =v
    a__  =v_bias

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

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

  a__  =state_dict['''final_layer.adaLN_modulation.1.weight''']
  a__  =state_dict['''final_layer.adaLN_modulation.1.bias''']
  a__  =state_dict['''final_layer.linear.weight''']
  a__  =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
  a__  =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=lowercase_     ,				num_attention_heads=16     ,				activation_fn='''gelu-approximate'''     ,				num_embeds_ada_norm=10_00     ,				norm_type='''ada_norm_zero'''     ,				norm_elementwise_affine=lowercase_     ,				)
  transformer.load_state_dict(lowercase_     ,				strict=lowercase_							)

  a__  =DDIMScheduler(
      num_train_timesteps=10_00     ,				beta_schedule='''linear'''     ,				prediction_type='''epsilon'''     ,				clip_sample=lowercase_     ,				)

  a__  =AutoencoderKL.from_pretrained(args.vae_model							)

  a__  =DiTPipeline(transformer=lowercase_     ,				vae=lowercase_     ,				scheduler=lowercase_							)

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


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

       parser.add_argument(
           '''--image_size''',
           default=256,
           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.'''
       )

       lowerCamelCase									= parser.parse_args()
       main(args)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCamelCase									= logging.get_logger(__name__)

lowerCamelCase									= {
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       'blenderbot'
   lowerCamelCase__			:     int    =       ['past_key_values']
   lowerCamelCase__			:     Optional[Any]    =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(    self,   lowercase_=8008,   lowercase_=128,   lowercase_=2,   lowercase_=10240,   lowercase_=32,   lowercase_=24,   lowercase_=10240,   lowercase_=32,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=True,   lowercase_=True,   lowercase_="gelu",   lowercase_=2560,   lowercase_=0.1,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=0.02,   lowercase_=1,   lowercase_=False,   lowercase_=0,   lowercase_=1,   lowercase_=2,   lowercase_=3,   lowercase_=2,   **lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =vocab_size
     a__  =max_position_embeddings
     a__  =d_model
     a__  =encoder_ffn_dim
     a__  =encoder_layers
     a__  =encoder_attention_heads
     a__  =decoder_ffn_dim
     a__  =decoder_layers
     a__  =decoder_attention_heads
     a__  =dropout
     a__  =attention_dropout
     a__  =activation_dropout
     a__  =activation_function
     a__  =init_std
     a__  =encoder_layerdrop
     a__  =decoder_layerdrop
     a__  =use_cache
     a__  =encoder_layers
     a__  =scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=lowercase_,   bos_token_id=lowercase_,   eos_token_id=lowercase_,   is_encoder_decoder=lowercase_,   decoder_start_token_id=lowercase_,   encoder_no_repeat_ngram_size=lowercase_,   forced_eos_token_id=lowercase_,   **lowercase_,   )







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @property
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__  ={0: '''batch'''}
         a__  ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
       if self.use_past:
         self.fill_with_past_key_values_(lowercase_,   direction='''inputs'''	)
     elif self.task == "causal-lm":
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__,				a__  =self.num_layers
         for i in range(lowercase_	):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
           ]	)

     return common_inputs



   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super().outputs
     else:
       a__  =super(lowercase_,   self	).outputs
       if self.use_past:
         a__,				a__  =self.num_layers
         for i in range(lowercase_	):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     # Generate decoder inputs
     a__  =seq_length if not self.use_past else 1
     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     a__  ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__  =dict(**lowercase_,   **lowercase_	)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
       else:
         import torch
       a__,				a__  =common_inputs['''input_ids'''].shape
       a__  =common_inputs['''decoder_input_ids'''].shape[1]
       a__,				a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__  =decoder_seq_length
       a__  =(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )
       a__  =torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_,   lowercase_	)],   dim=1	)
       a__  =[]
       a__,				a__  =self.num_layers

       for _ in range(lowercase_	):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
             )	)
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
       else:
         import torch
       a__,				a__  =common_inputs['''input_ids'''].shape
       a__  =seqlen
       a__,				a__  =self.num_layers
       a__,				a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__  =common_inputs['''attention_mask'''].dtype
       a__  =torch.cat(
           [common_inputs['''attention_mask'''], torch.ones(lowercase_,   lowercase_,   dtype=lowercase_	)],   dim=1	)
       a__  =[
           (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(lowercase_	)
       ]
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_batch,   num_token_to_add=0	)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     a__  =tokenizer.num_special_tokens_to_add(lowercase_	)
     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=lowercase_	)

     # Generate dummy inputs according to compute batch and sequence
     a__  =[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
     a__  =dict(tokenizer(lowercase_,   return_tensors=lowercase_	)	)
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)

     elif self.task == "causal-lm":
       a__  =self._generate_dummy_inputs_for_causal_lm(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)
     else:
       a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)

     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super()._flatten_past_key_values_(lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     else:
       a__  =super(lowercase_,   self	)._flatten_past_key_values_(
           lowercase_,   lowercase_,   lowercase_,   lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Tuple:






     """simple docstring"""






     if direction not in ["inputs", "outputs"]:
       raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

     a__  ='''past_key_values''' if direction == '''inputs''' else '''present'''
     a__,				a__  =self.num_layers

     a__  ='''past_encoder_sequence'''
     a__  ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

     for i in range(lowercase_	):
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
