





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCamelCase:  List[str]   =   {5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[Any] ):




		'''simple docstring'''
		lowercase__				:			List[str]            =		f"""pretrained_models/{model_name}"""
		if not os.path.isfile(_lowercase ):
				os.makedirs('pretrained_models'      ,						exist_ok=_lowercase )
				lowercase__				:			Union[str, Any]            =		f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
				download_url(_lowercase      ,						'pretrained_models' )
		lowercase__				:			Optional[Any]            =		torch.load(_lowercase      ,						map_location=lambda _lowercase      ,						_lowercase : storage )
		return model


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Union[str, Any] ):




		'''simple docstring'''
		lowercase__				:			str            =		download_model(pretrained_models[args.image_size] )

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

				lowercase__				:			Dict            =		state_dict[
				    f"""blocks.{depth}.adaLN_modulation.1.weight"""
				]
				lowercase__				:			Optional[int]            =		state_dict[
				    f"""blocks.{depth}.adaLN_modulation.1.bias"""
				]

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

				lowercase__				:			Dict            =		q
				lowercase__				:			Tuple            =		q_bias
				lowercase__				:			int            =		k
				lowercase__				:			Optional[int]            =		k_bias
				lowercase__				:			Dict            =		v
				lowercase__				:			Optional[Any]            =		v_bias

				lowercase__				:			Optional[int]            =		state_dict[
				    f"""blocks.{depth}.attn.proj.weight"""
				]
				lowercase__				:			Tuple            =		state_dict[f"""blocks.{depth}.attn.proj.bias"""]

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

		lowercase__				:			int            =		state_dict['final_layer.adaLN_modulation.1.weight']
		lowercase__				:			List[Any]            =		state_dict['final_layer.adaLN_modulation.1.bias']
		lowercase__				:			Any            =		state_dict['final_layer.linear.weight']
		lowercase__				:			List[str]            =		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
		lowercase__				:			List[str]            =		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=1_000      ,						norm_type='ada_norm_zero'      ,						norm_elementwise_affine=_lowercase      ,						)
		transformer.load_state_dict(_lowercase      ,						strict=_lowercase )

		lowercase__				:			Optional[Any]            =		DDIMScheduler(
		    num_train_timesteps=1_000      ,						beta_schedule='linear'      ,						prediction_type='epsilon'      ,						clip_sample=_lowercase      ,						)

		lowercase__				:			List[str]            =		AutoencoderKL.from_pretrained(args.vae_model )

		lowercase__				:			Any            =		DiTPipeline(transformer=_lowercase      ,						vae=_lowercase      ,						scheduler=_lowercase )

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


if __name__ == "__main__":
			__UpperCamelCase:  Optional[Any]   =   argparse.ArgumentParser()

			parser.add_argument(
			    """--image_size""",
			    default=2_5_6,
			    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[Any]   =   parser.parse_args()
			main(args)









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


__UpperCamelCase:  int   =   random.Random()


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int      ,						_lowercase    :   List[Any]=1.0      ,						_lowercase    :   Dict=None      ,						_lowercase    :   List[str]=None )      ->       List[str]:




		'''simple docstring'''
		if rng is None:
				lowercase__				:			int            =		global_rng

		lowercase__				:			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 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	int=7,				lowerCamelCase_:	Tuple=400,				lowerCamelCase_:	Union[str, Any]=2000,				lowerCamelCase_:	Optional[int]=1,				lowerCamelCase_:	int=0.0,				lowerCamelCase_:	Any=24000,				lowerCamelCase_:	Dict=True,				):
			lowercase__				:			List[str]            =		parent
			lowercase__				:			Any            =		batch_size
			lowercase__				:			List[str]            =		min_seq_length
			lowercase__				:			Tuple            =		max_seq_length
			lowercase__				:			Optional[Any]            =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowercase__				:			int            =		feature_size
			lowercase__				:			Optional[Any]            =		padding_value
			lowercase__				:			Optional[int]            =		sampling_rate
			lowercase__				:			str            =		return_attention_mask

	def       snake_case__(	self:	str ):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			}





	def       snake_case__(	self:	Dict,				lowerCamelCase_:	int=False,				lowerCamelCase_:	Optional[Any]=False ):
			def _flatten(lowerCamelCase_:	int ):
					return list(itertools.chain(*lowerCamelCase_ ) )

			if equal_length:
					lowercase__				:			Tuple            =		floats_list((self.batch_size, self.max_seq_length) )
			else:
					# make sure that inputs increase in size
					lowercase__				:			Dict            =		[
					    _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:
					lowercase__				:			Any            =		[np.asarray(lowerCamelCase_ ) for x in audio_inputs]

			return audio_inputs


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


	'''simple docstring'''





	_A       	=							EncodecFeatureExtractor

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			List[Any]            =		EnCodecFeatureExtractionTester(self )

	def       snake_case__(	self:	Optional[int] ):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowercase__				:			List[str]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			# create three inputs of length 800, 1000, and 1200
			lowercase__				:			Dict            =		[floats_list((1, x) )[0] for x in range(800,				1400,				200 )]
			lowercase__				:			List[Any]            =		[np.asarray(lowerCamelCase_ ) for audio_input in audio_inputs]

			# Test not batched input
			lowercase__				:			List[str]            =		feat_extract(audio_inputs[0],				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		feat_extract(np_audio_inputs[0],				return_tensors='np' ).input_values
			self.assertTrue(np.allclose(lowerCamelCase_,				lowerCamelCase_,				atol=1E-3 ) )

			# Test batched
			lowercase__				:			Any            =		feat_extract(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		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       snake_case__(	self:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			lowercase__				:			Optional[int]            =		np.random.rand(100 ).astype(np.floataa )
			lowercase__				:			Optional[int]            =		np_audio_inputs.tolist()

			for inputs in [py_audio_inputs, np_audio_inputs]:
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='np' )
					self.assertTrue(np_processed.input_values.dtype == np.floataa )
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='pt' )
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa )

	def       snake_case__(	self:	Any,				lowerCamelCase_:	Any ):
			from datasets import load_dataset

			lowercase__				:			Dict            =		load_dataset('hf-internal-testing/librispeech_asr_dummy',				'clean',				split='validation' )
			# automatic decoding with librispeech
			lowercase__				:			Union[str, Any]            =		ds.sort('id' ).select(range(lowerCamelCase_ ) )[:num_samples]['audio']

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

	def       snake_case__(	self:	List[str] ):
			# fmt: off
			lowercase__				:			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
			lowercase__				:			Tuple            =		self._load_datasamples(1 )
			lowercase__				:			List[Any]            =		EncodecFeatureExtractor()
			lowercase__				:			Optional[int]            =		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       snake_case__(	self:	Dict ):
			# fmt: off
			lowercase__				:			Union[str, 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
			lowercase__				:			Dict            =		self._load_datasamples(1 )
			lowercase__				:			Tuple            =		[np.tile(input_audio[0][None],				reps=(2, 1) )]
			input_audio[0][1] *= 0.5
			lowercase__				:			Tuple            =		EncodecFeatureExtractor(feature_size=2 )
			lowercase__				:			Dict            =		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       snake_case__(	self:	List[str] ):
			lowercase__				:			List[Any]            =		self._load_datasamples(2 )
			# would be easier if the stride was like
			lowercase__				:			List[str]            =		EncodecFeatureExtractor(feature_size=1,				chunk_length_s=1,				overlap=0.0_1 )

			# pad and trunc raise an error ?
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Both padding and truncation were set. Make sure you only set one.$',				):
					lowercase__				:			Tuple            =		feature_extractor(
					    lowerCamelCase_,				padding='max_length',				truncation=lowerCamelCase_,				return_tensors='pt' ).input_values

			# truncate to chunk
			lowercase__				:			List[str]            =		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
			lowercase__				:			int            =		feature_extractor(
			    lowerCamelCase_,				truncation=lowerCamelCase_,				max_length=48000,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 48000) )

			# pad to chunk
			lowercase__				:			str            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(padded_outputs.shape,				(2, 1, 95280) )

			# pad to chunk
			lowercase__				:			Tuple            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 95280) )

			# force pad to max length
			lowercase__				:			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.$',				):
					lowercase__				:			List[str]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		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
			lowercase__				:			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.$',				):
					lowercase__				:			List[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			Any            =		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
			lowercase__				:			str            =		2
			lowercase__				:			Tuple            =		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.$',				):
					lowercase__				:			Optional[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )


