





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)




class 					_a		:






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any]=1_3,					lowerCAmelCase__						:       List[str]=7,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       str=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Any=9_9,					lowerCAmelCase__						:       List[str]=3_2,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       str=4,					lowerCAmelCase__						:       Optional[int]=3_7,					lowerCAmelCase__						:       Dict="gelu",					lowerCAmelCase__						:       Optional[int]=0.1,					lowerCAmelCase__						:       Dict=0.1,					lowerCAmelCase__						:       Tuple=5_1_2,					lowerCAmelCase__						:       Optional[int]=1_6,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       Optional[Any]=0.02,					lowerCAmelCase__						:       Dict=3,					lowerCAmelCase__						:       Tuple=4,					lowerCAmelCase__						:       Optional[Any]=None,					)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       parent
										_UpperCamelCase     :   Tuple      =       1_3
										_UpperCamelCase     :   Optional[Any]      =       7
										_UpperCamelCase     :   Optional[Any]      =       True
										_UpperCamelCase     :   str      =       True
										_UpperCamelCase     :   List[str]      =       True
										_UpperCamelCase     :   Union[str, Any]      =       True
										_UpperCamelCase     :   List[str]      =       9_9
										_UpperCamelCase     :   Any      =       3_2
										_UpperCamelCase     :   Dict      =       2
										_UpperCamelCase     :   List[str]      =       4
										_UpperCamelCase     :   List[str]      =       3_7
										_UpperCamelCase     :   List[Any]      =       '''gelu'''
										_UpperCamelCase     :   Optional[Any]      =       0.1
										_UpperCamelCase     :   List[Any]      =       0.1
										_UpperCamelCase     :   Any      =       5_1_2
										_UpperCamelCase     :   Union[str, Any]      =       1_6
										_UpperCamelCase     :   List[str]      =       False
										_UpperCamelCase     :   Any      =       -1
										_UpperCamelCase     :   int      =       True
										_UpperCamelCase     :   Optional[Any]      =       2
										_UpperCamelCase     :   Optional[int]      =       0.02
										_UpperCamelCase     :   Optional[Any]      =       3
										_UpperCamelCase     :   str      =       4
										_UpperCamelCase     :   int      =       None






				def 							snake_case      (					self						:       Optional[int]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       ids_tensor([self.batch_size, self.seq_length],					self.vocab_size				)

										_UpperCamelCase     :   Optional[int]      =       None
										if self.use_input_mask:
																_UpperCamelCase     :   Any      =       random_attention_mask([self.batch_size, self.seq_length]				)

										_UpperCamelCase     :   Union[str, Any]      =       None
										if self.use_token_type_ids:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size				)

										_UpperCamelCase     :   Dict      =       None
										_UpperCamelCase     :   Optional[int]      =       None
										_UpperCamelCase     :   str      =       None
										if self.use_labels:
																_UpperCamelCase     :   Dict      =       ids_tensor([self.batch_size],					self.type_sequence_label_size				)
																_UpperCamelCase     :   Any      =       ids_tensor([self.batch_size, self.seq_length],					self.num_labels				)

										_UpperCamelCase     :   str      =       DebertaConfig(
										    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					relative_attention=self.relative_attention,					max_relative_positions=self.max_relative_positions,					position_biased_input=self.position_biased_input,					initializer_range=self.initializer_range,					return_dict=lowerCAmelCase__,					)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       TFDebertaModel(config=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

										_UpperCamelCase     :   int      =       [input_ids, input_mask]
										_UpperCamelCase     :   Union[str, Any]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)				)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       TFDebertaForMaskedLM(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       str				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.num_labels
										_UpperCamelCase     :   int      =       TFDebertaForSequenceClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   Any      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       int				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.num_labels
										_UpperCamelCase     :   Optional[int]      =       TFDebertaForTokenClassification(config=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Tuple				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       TFDebertaForQuestionAnswering(config=lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       {
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)				)
										self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)				)






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										(
										    (
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				(
										    _UpperCamelCase
										)  ,				
										)     :   List[Any]      =       config_and_inputs
										_UpperCamelCase     :   Dict      =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_tf
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCamelCase								=       (
				    {
				        '''feature-extraction''': TFDebertaModel,
				        '''fill-mask''': TFDebertaForMaskedLM,
				        '''question-answering''': TFDebertaForQuestionAnswering,
				        '''text-classification''': TFDebertaForSequenceClassification,
				        '''token-classification''': TFDebertaForTokenClassification,
				        '''zero-shot''': TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				UpperCamelCase								=       False
				UpperCamelCase								=       False






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       TFDebertaModelTester(self				)
										_UpperCamelCase     :   int      =       ConfigTester(self,					config_class=lowerCAmelCase__,					hidden_size=3_7				)






				def 							snake_case      (					self						:       Optional[int]				)      -> List[Any]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def 							snake_case      (					self						:       Dict				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Tuple				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       Optional[int]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										self.assertIsNotNone(lowerCAmelCase__				)




@require_tf
class 					_a		( unittest.TestCase							):






				@unittest.skip(reason='''Model not available yet'''				)
				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										pass






				@slow
				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''				)
										_UpperCamelCase     :   List[Any]      =       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										_UpperCamelCase     :   str      =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
										_UpperCamelCase     :   Tuple      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__				)[0]

										_UpperCamelCase     :   Union[str, Any]      =       tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]				)
										tf.debugging.assert_near(output[:, 1:4, 1:4],					lowerCAmelCase__,					atol=1e-4				)





'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

  __A		:  List[Any]       				=		F"pretrained_models/{model_name}"
  if not os.path.isfile(__SCREAMING_SNAKE_CASE			):
    os.makedirs("""pretrained_models"""      ,  exist_ok=__SCREAMING_SNAKE_CASE			)
    __A		:  int       				=		F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
    download_url(__SCREAMING_SNAKE_CASE      ,  """pretrained_models"""			)
  __A		:  Optional[Any]       				=		torch.load(__SCREAMING_SNAKE_CASE      ,  map_location=lambda __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			: storage			)
  return model







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Dict:





  """simple docstring"""

  __A		:  Optional[Any]       				=		download_model(pretrained_models[args.image_size]			)

  __A		:  Tuple       				=		state_dict["""x_embedder.proj.weight"""]
  __A		:  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			):
    __A		:  Union[str, Any]       				=		state_dict[
        """t_embedder.mlp.0.weight"""
    ]
    __A		:  int       				=		state_dict[
        """t_embedder.mlp.0.bias"""
    ]
    __A		:  List[Any]       				=		state_dict[
        """t_embedder.mlp.2.weight"""
    ]
    __A		:  str       				=		state_dict[
        """t_embedder.mlp.2.bias"""
    ]
    __A		:  Optional[int]       				=		state_dict[
        """y_embedder.embedding_table.weight"""
    ]

    __A		:  Union[str, Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

    __A				,       __A				,       __A		:  Tuple       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]      ,  3      ,  dim=0			)
    __A				,       __A				,       __A		:  Union[str, Any]       				=		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]      ,  3      ,  dim=0			)

    __A		:  Tuple       				=		q
    __A		:  Tuple       				=		q_bias
    __A		:  Tuple       				=		k
    __A		:  int       				=		k_bias
    __A		:  Union[str, Any]       				=		v
    __A		:  Optional[Any]       				=		v_bias

    __A		:  List[Any]       				=		state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    __A		:  str       				=		state_dict[F"blocks.{depth}.attn.proj.bias"]

    __A		:  str       				=		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __A		:  Union[str, Any]       				=		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __A		:  int       				=		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __A		:  str       				=		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		:  List[str]       				=		state_dict["""final_layer.adaLN_modulation.1.weight"""]
  __A		:  Optional[int]       				=		state_dict["""final_layer.adaLN_modulation.1.bias"""]
  __A		:  List[str]       				=		state_dict["""final_layer.linear.weight"""]
  __A		:  Optional[int]       				=		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		:  Union[str, Any]       				=		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=__SCREAMING_SNAKE_CASE      ,  num_attention_heads=16      ,  activation_fn="""gelu-approximate"""      ,  num_embeds_ada_norm=1000      ,  norm_type="""ada_norm_zero"""      ,  norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,  )
  transformer.load_state_dict(__SCREAMING_SNAKE_CASE      ,  strict=__SCREAMING_SNAKE_CASE			)

  __A		:  str       				=		DDIMScheduler(
      num_train_timesteps=1000      ,  beta_schedule="""linear"""      ,  prediction_type="""epsilon"""      ,  clip_sample=__SCREAMING_SNAKE_CASE      ,  )

  __A		:  List[Any]       				=		AutoencoderKL.from_pretrained(args.vae_model			)

  __A		:  Optional[int]       				=		DiTPipeline(transformer=__SCREAMING_SNAKE_CASE      ,  vae=__SCREAMING_SNAKE_CASE      ,  scheduler=__SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
     A__ :		List[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__ :		Dict					=parser.parse_args()
     main(args)



