



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
  lowerCAmelCase_    :  Optional[int]             =				f"""pretrained_models/{model_name}"""
  if not os.path.isfile(__UpperCamelCase				):
    os.makedirs('''pretrained_models'''  ,exist_ok=__UpperCamelCase				)
    lowerCAmelCase_    :  Tuple             =				f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
    download_url(__UpperCamelCase  ,'''pretrained_models'''				)
  lowerCAmelCase_    :  List[Any]             =				torch.load(__UpperCamelCase  ,map_location=lambda __UpperCamelCase  ,__UpperCamelCase				: storage				)
  return model


def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]				):
  lowerCAmelCase_    :  Optional[Any]             =				download_model(pretrained_models[args.image_size]				)

  lowerCAmelCase_    :  Dict             =				state_dict['''x_embedder.proj.weight''']
  lowerCAmelCase_    :  Dict             =				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				):
    lowerCAmelCase_    :  Optional[Any]             =				state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    lowerCAmelCase_    :  List[str]             =				state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    lowerCAmelCase_    :  List[str]             =				state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    lowerCAmelCase_    :  List[Any]             =				state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    lowerCAmelCase_    :  List[str]             =				state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    lowerCAmelCase_    :  str             =				state_dict[
        f"""blocks.{depth}.adaLN_modulation.1.weight"""
    ]
    lowerCAmelCase_    :  Optional[Any]             =				state_dict[
        f"""blocks.{depth}.adaLN_modulation.1.bias"""
    ]

    lowerCAmelCase_    :  List[str]             =				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]  ,3  ,dim=0				)
    lowerCAmelCase_    :  List[Any]             =				torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]  ,3  ,dim=0				)

    lowerCAmelCase_    :  Optional[Any]             =				q
    lowerCAmelCase_    :  int             =				q_bias
    lowerCAmelCase_    :  Any             =				k
    lowerCAmelCase_    :  Optional[Any]             =				k_bias
    lowerCAmelCase_    :  Optional[Any]             =				v
    lowerCAmelCase_    :  Optional[Any]             =				v_bias

    lowerCAmelCase_    :  List[Any]             =				state_dict[
        f"""blocks.{depth}.attn.proj.weight"""
    ]
    lowerCAmelCase_    :  Tuple             =				state_dict[f"""blocks.{depth}.attn.proj.bias"""]

    lowerCAmelCase_    :  Any             =				state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
    lowerCAmelCase_    :  Union[str, Any]             =				state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
    lowerCAmelCase_    :  Optional[Any]             =				state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
    lowerCAmelCase_    :  Union[str, Any]             =				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'''				)

  lowerCAmelCase_    :  Dict             =				state_dict['''final_layer.adaLN_modulation.1.weight''']
  lowerCAmelCase_    :  Optional[Any]             =				state_dict['''final_layer.adaLN_modulation.1.bias''']
  lowerCAmelCase_    :  List[str]             =				state_dict['''final_layer.linear.weight''']
  lowerCAmelCase_    :  Dict             =				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
  lowerCAmelCase_    :  Optional[int]             =				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=__UpperCamelCase  ,num_attention_heads=16  ,activation_fn='''gelu-approximate'''  ,num_embeds_ada_norm=1000  ,norm_type='''ada_norm_zero'''  ,norm_elementwise_affine=__UpperCamelCase  ,)
  transformer.load_state_dict(__UpperCamelCase  ,strict=__UpperCamelCase				)

  lowerCAmelCase_    :  str             =				DDIMScheduler(
      num_train_timesteps=1000  ,beta_schedule='''linear'''  ,prediction_type='''epsilon'''  ,clip_sample=__UpperCamelCase  ,)

  lowerCAmelCase_    :  Dict             =				AutoencoderKL.from_pretrained(args.vae_model				)

  lowerCAmelCase_    :  List[str]             =				DiTPipeline(transformer=__UpperCamelCase  ,vae=__UpperCamelCase  ,scheduler=__UpperCamelCase				)

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


if __name__ == "__main__":
      A__						:						str          =      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.'''
      )

      A__						:						Any          =      parser.parse_args()
      main(args)










from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class        __snake_case		:
							_a       =				MarianConfig
							_a       =				{}
							_a       =				'''gelu'''




							def __init__( self  : Dict       ,       A_  : Optional[int]       ,       A_  : Any=1_3       ,       A_  : Any=7       ,       A_  : int=True       ,       A_  : int=False       ,       A_  : str=9_9       ,       A_  : Tuple=3_2       ,       A_  : Dict=2       ,       A_  : Union[str, Any]=4       ,       A_  : Optional[int]=3_7       ,       A_  : Any=0.1       ,       A_  : str=0.1       ,       A_  : Optional[int]=2_0       ,       A_  : Any=2       ,       A_  : Any=1       ,       A_  : Any=0       ,       ):
									lowerCAmelCase_    :  Any             =				parent
									lowerCAmelCase_    :  Dict             =				batch_size
									lowerCAmelCase_    :  Any             =				seq_length
									lowerCAmelCase_    :  Union[str, Any]             =				is_training
									lowerCAmelCase_    :  Any             =				use_labels
									lowerCAmelCase_    :  Tuple             =				vocab_size
									lowerCAmelCase_    :  Optional[int]             =				hidden_size
									lowerCAmelCase_    :  Optional[int]             =				num_hidden_layers
									lowerCAmelCase_    :  Union[str, Any]             =				num_attention_heads
									lowerCAmelCase_    :  List[str]             =				intermediate_size

									lowerCAmelCase_    :  Union[str, Any]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Union[str, Any]             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  Optional[Any]             =				max_position_embeddings
									lowerCAmelCase_    :  List[str]             =				eos_token_id
									lowerCAmelCase_    :  Optional[Any]             =				pad_token_id
									lowerCAmelCase_    :  Dict             =				bos_token_id




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  str             =				ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size)
									lowerCAmelCase_    :  Optional[int]             =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)       ,       1)
									lowerCAmelCase_    :  List[str]             =				tf.concat([input_ids, eos_tensor]       ,       axis=1)

									lowerCAmelCase_    :  Optional[Any]             =				ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size)

									lowerCAmelCase_    :  Optional[int]             =				self.config_cls(
									    vocab_size=self.vocab_size       ,       d_model=self.hidden_size       ,       encoder_layers=self.num_hidden_layers       ,       decoder_layers=self.num_hidden_layers       ,       encoder_attention_heads=self.num_attention_heads       ,       decoder_attention_heads=self.num_attention_heads       ,       encoder_ffn_dim=self.intermediate_size       ,       decoder_ffn_dim=self.intermediate_size       ,       dropout=self.hidden_dropout_prob       ,       attention_dropout=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       eos_token_ids=[2]       ,       bos_token_id=self.bos_token_id       ,       pad_token_id=self.pad_token_id       ,       decoder_start_token_id=self.pad_token_id       ,       **self.config_updates       ,       )
									lowerCAmelCase_    :  int             =				prepare_marian_inputs_dict(A_       ,       A_       ,       A_)
									return config, inputs_dict







							def      UpperCAmelCase__    ( self  : int       ,       A_  : int       ,       A_  : str):
									lowerCAmelCase_    :  Any             =				TFMarianModel(config=A_).get_decoder()
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''input_ids''']

									lowerCAmelCase_    :  str             =				input_ids[:1, :]
									lowerCAmelCase_    :  List[Any]             =				inputs_dict['''attention_mask'''][:1, :]
									lowerCAmelCase_    :  Dict             =				inputs_dict['''head_mask''']
									lowerCAmelCase_    :  Optional[int]             =				1

									# first forward pass
									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_       ,       head_mask=A_       ,       use_cache=A_)

									lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									lowerCAmelCase_    :  int             =				ids_tensor((self.batch_size, 3)       ,       config.vocab_size)
									lowerCAmelCase_    :  str             =				tf.cast(ids_tensor((self.batch_size, 3)       ,       2)       ,       tf.inta)

									# append to next input_ids and
									lowerCAmelCase_    :  Optional[int]             =				tf.concat([input_ids, next_tokens]       ,       axis=-1)
									lowerCAmelCase_    :  int             =				tf.concat([attention_mask, next_attn_mask]       ,       axis=-1)

									lowerCAmelCase_    :  str             =				model(A_       ,       attention_mask=A_)[0]
									lowerCAmelCase_    :  List[str]             =				model(A_       ,       attention_mask=A_       ,       past_key_values=A_)[0]

									self.parent.assertEqual(next_tokens.shape[1]       ,       output_from_past.shape[1])

									# select random slice
									lowerCAmelCase_    :  Optional[Any]             =				int(ids_tensor((1,)       ,       output_from_past.shape[-1]))
									lowerCAmelCase_    :  Tuple             =				output_from_no_past[:, -3:, random_slice_idx]
									lowerCAmelCase_    :  Optional[Any]             =				output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(A_       ,       A_       ,       rtol=1e-3)





def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	List[str]=None  ,__UpperCamelCase							:	Union[str, Any]=None  ,__UpperCamelCase							:	List[Any]=None  ,__UpperCamelCase							:	str=None  ,__UpperCamelCase							:	Optional[int]=None  ,):
		if attention_mask is None:
				lowerCAmelCase_    :  Optional[int]             =				tf.cast(tf.math.not_equal(__UpperCamelCase  ,config.pad_token_id				)  ,tf.inta				)
		if decoder_attention_mask is None:
				lowerCAmelCase_    :  List[str]             =				tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape  ,dtype=tf.inta				),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,config.pad_token_id				)  ,tf.inta				),
				    ]  ,axis=-1  ,)
		if head_mask is None:
				lowerCAmelCase_    :  Union[str, Any]             =				tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
		if decoder_head_mask is None:
				lowerCAmelCase_    :  List[str]             =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		if cross_attn_head_mask is None:
				lowerCAmelCase_    :  Union[str, Any]             =				tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}



@require_tf
class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		,unittest.TestCase		):
							_a       =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							_a       =				(TFMarianMTModel,) if is_tf_available() else ()
							_a       =				(
							    {
							        '''conversational''': TFMarianMTModel,
							        '''feature-extraction''': TFMarianModel,
							        '''summarization''': TFMarianMTModel,
							        '''text2text-generation''': TFMarianMTModel,
							        '''translation''': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							_a       =				True
							_a       =				False
							_a       =				False




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Optional[Any]             =				TFMarianModelTester(self)
									lowerCAmelCase_    :  List[str]             =				ConfigTester(self       ,       config_class=A_)




							def      UpperCAmelCase__    ( self  : List[Any]):
									self.config_tester.run_common_tests()







							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  List[Any]             =				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*A_)





@require_tf
class        __snake_case		(  unittest.TestCase		):
							_a       =				1_000  # show more chars for failing integration tests




							@classmethod
							def      UpperCAmelCase__    ( cls  : Optional[Any]):
									lowerCAmelCase_    :  Union[str, Any]             =				F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls




							@cached_property
							def      UpperCAmelCase__    ( self  : List[str]):
									return AutoTokenizer.from_pretrained(self.model_name)




							@property
							def      UpperCAmelCase__    ( self  : Dict):
									return self.tokenizer.eos_token_id




							@cached_property
							def      UpperCAmelCase__    ( self  : int):
									warnings.simplefilter('''error''')
									lowerCAmelCase_    :  TFMarianMTModel             =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
									assert isinstance(A_       ,       A_)
									lowerCAmelCase_    :  int             =				model.config
									self.assertListEqual(c.bad_words_ids       ,       [[c.pad_token_id]])
									self.assertEqual(c.max_length       ,       5_1_2)
									self.assertEqual(c.decoder_start_token_id       ,       c.pad_token_id)
									return model




							def      UpperCAmelCase__    ( self  : Dict       ,       **A_  : Tuple):
									lowerCAmelCase_    :  Optional[int]             =				self.translate_src_text(**A_)
									self.assertListEqual(self.expected_text       ,       A_)







							def      UpperCAmelCase__    ( self  : Any       ,       **A_  : List[str]):
									lowerCAmelCase_    :  List[Any]             =				self.tokenizer(self.src_text       ,       **A_       ,       padding=A_       ,       return_tensors='''tf''')
									lowerCAmelCase_    :  List[str]             =				self.model.generate(
									    model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       max_length=1_2_8)
									lowerCAmelCase_    :  Any             =				self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=A_)
									return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''mt'''
							_a       =				'''en'''
							_a       =				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							_a       =				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Any):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''zh'''
							_a       =				['''My name is Wolfgang and I live in Berlin''']
							_a       =				['''我叫沃尔夫冈 我住在柏林''']




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : str):
									self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''en'''
							_a       =				'''ROMANCE'''
							_a       =				[
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							_a       =				[
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]




							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									self._assert_generated_batch_equal_expected()







							@unittest.skip('''Skipping until #12647 is resolved.''')
							@slow
							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  Dict             =				TranslationPipeline(self.model       ,       self.tokenizer       ,       framework='''tf''')
									lowerCAmelCase_    :  Union[str, Any]             =				pipeline(self.src_text)
									self.assertEqual(self.expected_text       ,       [x['''translation_text'''] for x in output])





