




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


a__		    =    random.Random()





def 							A__   (snake_case      :		Union[str, Any]		,  snake_case      :		str=1.0		,  snake_case      :		Union[str, Any]=None		,  snake_case      :		Any=None							)    ->	Union[str, Any]:
     if rng is None:
          __UpperCamelCase				:  Dict								=			global_rng

     __UpperCamelCase				:  List[Any]								=			[]
     for batch_idx in range(shape[0]							):
          values.append([]							)
          for _ in range(shape[1]							):
               values[-1].append(rng.random() * scale							)

     return values


@require_torch
class       SCREAMING_SNAKE_CASE_							(					unittest.TestCase      ):



 """simple docstring"""


 def __init__(				self		:	Union[str, Any]				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	List[Any]=7				,    lowerCAmelCase		:	List[str]=400				,    lowerCAmelCase		:	Dict=2000				,    lowerCAmelCase		:	List[str]=1				,    lowerCAmelCase		:	Optional[Any]=0.0				,    lowerCAmelCase		:	Union[str, Any]=24000				,    lowerCAmelCase		:	int=True				,    )  ->					Tuple:







      """simple docstring"""







      __UpperCamelCase				:  int								=			parent
      __UpperCamelCase				:  Optional[int]								=			batch_size
      __UpperCamelCase				:  int								=			min_seq_length
      __UpperCamelCase				:  Dict								=			max_seq_length
      __UpperCamelCase				:  Dict								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __UpperCamelCase				:  str								=			feature_size
      __UpperCamelCase				:  List[Any]								=			padding_value
      __UpperCamelCase				:  List[Any]								=			sampling_rate
      __UpperCamelCase				:  List[str]								=			return_attention_mask






 def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Optional[int]:







      """simple docstring"""







      return {
          "feature_size": self.feature_size,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
      }






 def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	str=False				,    lowerCAmelCase		:	Any=False  )  ->					Optional[Any]:







      """simple docstring"""







      def _flatten(lowerCAmelCase		:	Dict  ):
           return list(itertools.chain(*lowerCAmelCase  )  )

      if equal_length:
           __UpperCamelCase				:  Optional[int]								=			floats_list((self.batch_size, self.max_seq_length)  )
      else:
           # make sure that inputs increase in size
           __UpperCamelCase				:  int								=			[
               _flatten(floats_list((x, self.feature_size)  )  )
               for x in range(self.min_seq_length				,    self.max_seq_length				,    self.seq_length_diff  )
           ]

      if numpify:
           __UpperCamelCase				:  Dict								=			[np.asarray(lowerCAmelCase  ) for x in audio_inputs]

      return audio_inputs






@require_torch
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     unittest.TestCase      ):



 """simple docstring"""


 __magic_name__      :					List[Any]			  =		EncodecFeatureExtractor






 def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Optional[int]:







      """simple docstring"""







      __UpperCamelCase				:  str								=			EnCodecFeatureExtractionTester(self  )






 def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Any:







      """simple docstring"""







      __UpperCamelCase				:  Any								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      # create three inputs of length 800, 1000, and 1200
      __UpperCamelCase				:  Dict								=			[floats_list((1, x)  )[0] for x in range(800				,    1400				,    200  )]
      __UpperCamelCase				:  str								=			[np.asarray(lowerCAmelCase  ) for audio_input in audio_inputs]

      # Test not batched input
      __UpperCamelCase				:  str								=			feat_extract(audio_inputs[0]				,    return_tensors="""np"""  ).input_values
      __UpperCamelCase				:  Optional[Any]								=			feat_extract(np_audio_inputs[0]				,    return_tensors="""np"""  ).input_values
      self.assertTrue(np.allclose(lowerCAmelCase				,    lowerCAmelCase				,    atol=1E-3  )  )

      # Test batched
      __UpperCamelCase				:  List[Any]								=			feat_extract(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""np"""  ).input_values
      __UpperCamelCase				:  int								=			feat_extract(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""np"""  ).input_values
      for enc_seq_a, enc_seq_a in zip(lowerCAmelCase				,    lowerCAmelCase  ):
           self.assertTrue(np.allclose(lowerCAmelCase				,    lowerCAmelCase				,    atol=1E-3  )  )






 def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Tuple:







      """simple docstring"""







      __UpperCamelCase				:  int								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      __UpperCamelCase				:  Dict								=			np.random.rand(100  ).astype(np.floataa  )
      __UpperCamelCase				:  int								=			np_audio_inputs.tolist()

      for inputs in [py_audio_inputs, np_audio_inputs]:
           __UpperCamelCase				:  List[Any]								=			feature_extractor.pad([{"""input_values""": inputs}]				,    return_tensors="""np"""  )
           self.assertTrue(np_processed.input_values.dtype == np.floataa  )
           __UpperCamelCase				:  Tuple								=			feature_extractor.pad([{"""input_values""": inputs}]				,    return_tensors="""pt"""  )
           self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )






 def 				lowerCamelCase__    (				self		:	List[str]				,    lowerCAmelCase		:	int  )  ->					List[str]:







      """simple docstring"""







      from datasets import load_dataset

      __UpperCamelCase				:  Optional[int]								=			load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,    """clean"""				,    split="""validation"""  )
      # automatic decoding with librispeech
      __UpperCamelCase				:  str								=			ds.sort("""id"""  ).select(range(lowerCAmelCase  )  )[:num_samples]["""audio"""]

      return [x["array"] for x in audio_samples]






 def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Optional[Any]:







      """simple docstring"""







      __UpperCamelCase				:  Optional[int]								=			torch.tensor(
          [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
           3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
           2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
           4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
           7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
           4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]  )
      # fmt: on
      __UpperCamelCase				:  Any								=			self._load_datasamples(1  )
      __UpperCamelCase				:  List[str]								=			EncodecFeatureExtractor()
      __UpperCamelCase				:  Any								=			feature_extractor(lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(input_values.shape				,    (1, 1, 93680)  )
      self.assertTrue(torch.allclose(input_values[0, 0, :30]				,    lowerCAmelCase				,    atol=1E-6  )  )






 def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Union[str, Any]:







      """simple docstring"""







      __UpperCamelCase				:  Any								=			torch.tensor(
          [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
           3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
           2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
           4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
           7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
           4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]  )
      # fmt: on
      __UpperCamelCase				:  Union[str, Any]								=			self._load_datasamples(1  )
      __UpperCamelCase				:  List[str]								=			[np.tile(input_audio[0][None]				,    reps=(2, 1)  )]
      input_audio[0][1] *= 0.5
      __UpperCamelCase				:  Optional[int]								=			EncodecFeatureExtractor(feature_size=2  )
      __UpperCamelCase				:  str								=			feature_extractor(lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(input_values.shape				,    (1, 2, 93680)  )
      self.assertTrue(torch.allclose(input_values[0, 0, :30]				,    lowerCAmelCase				,    atol=1E-6  )  )
      self.assertTrue(torch.allclose(input_values[0, 1, :30]				,    EXPECTED_INPUT_VALUES * 0.5				,    atol=1E-6  )  )






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







      """simple docstring"""







      __UpperCamelCase				:  Tuple								=			self._load_datasamples(2  )
      # would be easier if the stride was like
      __UpperCamelCase				:  List[str]								=			EncodecFeatureExtractor(feature_size=1				,    chunk_length_s=1				,    overlap=0.01  )

      # pad and trunc raise an error ?
      with self.assertRaisesRegex(
          lowerCAmelCase				,    """^Both padding and truncation were set. Make sure you only set one.$"""				,    ):
           __UpperCamelCase				:  List[Any]								=			feature_extractor(
               lowerCAmelCase				,    padding="""max_length"""				,    truncation=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values

      # truncate to chunk
      __UpperCamelCase				:  Dict								=			feature_extractor(lowerCAmelCase				,    truncation=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (2, 1, 71520)  )  # 2 chunks

      # force truncate to max_length
      __UpperCamelCase				:  Tuple								=			feature_extractor(
          lowerCAmelCase				,    truncation=lowerCAmelCase				,    max_length=48000				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (2, 1, 48000)  )

      # pad to chunk
      __UpperCamelCase				:  str								=			feature_extractor(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(padded_outputs.shape				,    (2, 1, 95280)  )

      # pad to chunk
      __UpperCamelCase				:  List[Any]								=			feature_extractor(lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (2, 1, 95280)  )

      # force pad to max length
      __UpperCamelCase				:  Union[str, Any]								=			feature_extractor(
          lowerCAmelCase				,    padding="""max_length"""				,    max_length=100000				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (2, 1, 100000)  )

      # force no pad
      with self.assertRaisesRegex(
          lowerCAmelCase				,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""				,    ):
           __UpperCamelCase				:  int								=			feature_extractor(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values

      __UpperCamelCase				:  Dict								=			feature_extractor(input_audio[0]				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (1, 1, 93680)  )

      # no pad if no chunk_length_s
      __UpperCamelCase				:  List[Any]								=			None
      with self.assertRaisesRegex(
          lowerCAmelCase				,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""				,    ):
           __UpperCamelCase				:  List[Any]								=			feature_extractor(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values

      __UpperCamelCase				:  Dict								=			feature_extractor(input_audio[0]				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (1, 1, 93680)  )

      # no pad if no overlap
      __UpperCamelCase				:  List[Any]								=			2
      __UpperCamelCase				:  Dict								=			None
      with self.assertRaisesRegex(
          lowerCAmelCase				,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""				,    ):
           __UpperCamelCase				:  int								=			feature_extractor(lowerCAmelCase				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values

      __UpperCamelCase				:  Union[str, Any]								=			feature_extractor(input_audio[0]				,    padding=lowerCAmelCase				,    return_tensors="""pt"""  ).input_values
      self.assertEquals(truncated_outputs.shape				,    (1, 1, 93680)  )












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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





def 							A__   (snake_case      :		List[Any]							)    ->	Dict:
					__UpperCamelCase				:  Optional[int]								=			F'''pretrained_models/{model_name}'''
					if not os.path.isfile(snake_case							):
										os.makedirs("""pretrained_models"""		,  exist_ok=snake_case							)
										__UpperCamelCase				:  Optional[int]								=			F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
										download_url(snake_case		,  """pretrained_models"""							)
					__UpperCamelCase				:  Union[str, Any]								=			torch.load(snake_case		,  map_location=lambda snake_case		,  snake_case							: storage							)
					return model





def 							A__   (snake_case      :		Optional[Any]							)    ->	Union[str, Any]:
					__UpperCamelCase				:  Any								=			download_model(pretrained_models[args.image_size]							)

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

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

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

										__UpperCamelCase				:  Optional[Any]								=			q
										__UpperCamelCase				:  Tuple								=			q_bias
										__UpperCamelCase				:  List[str]								=			k
										__UpperCamelCase				:  Tuple								=			k_bias
										__UpperCamelCase				:  List[Any]								=			v
										__UpperCamelCase				:  Dict								=			v_bias

										__UpperCamelCase				:  Tuple								=			state_dict[
										    F'''blocks.{depth}.attn.proj.weight'''
										]
										__UpperCamelCase				:  Union[str, Any]								=			state_dict[F'''blocks.{depth}.attn.proj.bias''']

										__UpperCamelCase				:  List[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
										__UpperCamelCase				:  List[str]								=			state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
										__UpperCamelCase				:  Optional[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
										__UpperCamelCase				:  int								=			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				:  str								=			state_dict["""final_layer.adaLN_modulation.1.weight"""]
					__UpperCamelCase				:  List[str]								=			state_dict["""final_layer.adaLN_modulation.1.bias"""]
					__UpperCamelCase				:  Union[str, Any]								=			state_dict["""final_layer.linear.weight"""]
					__UpperCamelCase				:  Any								=			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				:  Optional[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=snake_case		,  num_attention_heads=16		,  activation_fn="""gelu-approximate"""		,  num_embeds_ada_norm=10_00		,  norm_type="""ada_norm_zero"""		,  norm_elementwise_affine=snake_case		,  )
					transformer.load_state_dict(snake_case		,  strict=snake_case							)

					__UpperCamelCase				:  Union[str, Any]								=			DDIMScheduler(
					    num_train_timesteps=10_00		,  beta_schedule="""linear"""		,  prediction_type="""epsilon"""		,  clip_sample=snake_case		,  )

					__UpperCamelCase				:  Optional[int]								=			AutoencoderKL.from_pretrained(args.vae_model							)

					__UpperCamelCase				:  str								=			DiTPipeline(transformer=snake_case		,  vae=snake_case		,  scheduler=snake_case							)

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


if __name__ == "__main__":
	a__		    =    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__		    =    parser.parse_args()
	main(args)






