
"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase				 =					logging.get_logger(__name__)






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  List[Any]=False  ):
	A     = OrderedDict()
	for key, value in state_dict.items():
		if encoder_only and not key.startswith('head'  ):
			A     = 'segformer.encoder.' + key
		if key.startswith('backbone'  ):
			A     = key.replace('backbone'						,   'segformer.encoder'  )
		if "patch_embed" in key:
			# replace for example patch_embed1 by patch_embeddings.0
			A     = key[key.find('patch_embed'  ) + len('patch_embed'  )]
			A     = key.replace(F'patch_embed{idx}'						,   F'patch_embeddings.{int(snake_case__  )-1}'  )
		if "norm" in key:
			A     = key.replace('norm'						,   'layer_norm'  )
		if "segformer.encoder.layer_norm" in key:
			# replace for example layer_norm1 by layer_norm.0
			A     = key[key.find('segformer.encoder.layer_norm'  ) + len('segformer.encoder.layer_norm'  )]
			A     = key.replace(F'layer_norm{idx}'						,   F'layer_norm.{int(snake_case__  )-1}'  )
		if "layer_norm1" in key:
			A     = key.replace('layer_norm1'						,   'layer_norm_1'  )
		if "layer_norm2" in key:
			A     = key.replace('layer_norm2'						,   'layer_norm_2'  )
		if "block" in key:
			# replace for example block1 by block.0
			A     = key[key.find('block'  ) + len('block'  )]
			A     = key.replace(F'block{idx}'						,   F'block.{int(snake_case__  )-1}'  )
		if "attn.q" in key:
			A     = key.replace('attn.q'						,   'attention.self.query'  )
		if "attn.proj" in key:
			A     = key.replace('attn.proj'						,   'attention.output.dense'  )
		if "attn" in key:
			A     = key.replace('attn'						,   'attention.self'  )
		if "fc1" in key:
			A     = key.replace('fc1'						,   'dense1'  )
		if "fc2" in key:
			A     = key.replace('fc2'						,   'dense2'  )
		if "linear_pred" in key:
			A     = key.replace('linear_pred'						,   'classifier'  )
		if "linear_fuse" in key:
			A     = key.replace('linear_fuse.conv'						,   'linear_fuse'  )
			A     = key.replace('linear_fuse.bn'						,   'batch_norm'  )
		if "linear_c" in key:
			# replace for example linear_c4 by linear_c.3
			A     = key[key.find('linear_c'  ) + len('linear_c'  )]
			A     = key.replace(F'linear_c{idx}'						,   F'linear_c.{int(snake_case__  )-1}'  )
		if key.startswith('head'  ):
			A     = key.replace('head'						,   'classifier'  )
		A     = value

	return new_state_dict






def    _snake_case					(		snake_case__ :  List[Any]						,   snake_case__ :  Union[str, Any]  ):
	# for each of the encoder blocks:
	for i in range(config.num_encoder_blocks  ):
		for j in range(config.depths[i]  ):
			# read in weights + bias of keys and values (which is a single matrix in the original implementation)
			A     = state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'  )
			A     = state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'  )
			# next, add keys and values (in that order) to the state dict
			A     = kv_weight[
			    : config.hidden_sizes[i], :
			]
			A     = kv_bias[: config.hidden_sizes[i]]
			A     = kv_weight[
			    config.hidden_sizes[i] :, :
			]
			A     = kv_bias[
			    config.hidden_sizes[i] :
			]






def    _snake_case					(		):
	A     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
	A     = Image.open(requests.get(snake_case__						,   stream=snake_case__  ).raw  )

	return image






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Optional[Any]						,   snake_case__ :  Tuple  ):
	A     = SegformerConfig()
	A     = False

	# set attributes based on model_name
	A     = 'huggingface/label-files'
	if "segformer" in model_name:
		A     = model_name[len('segformer.'  ) : len('segformer.'  ) + 2]
		if "ade" in model_name:
			A     = 150
			A     = 'ade20k-id2label.json'
			A     = (1, 150, 128, 128)
		elif "city" in model_name:
			A     = 19
			A     = 'cityscapes-id2label.json'
			A     = (1, 19, 128, 128)
		else:
			raise ValueError(F'Model {model_name} not supported'  )
	elif "mit" in model_name:
		A     = True
		A     = model_name[4:6]
		A     = 1000
		A     = 'imagenet-1k-id2label.json'
		A     = (1, 1000)
	else:
		raise ValueError(F'Model {model_name} not supported'  )

	# set config attributes
	A     = json.load(open(hf_hub_download(snake_case__						,   snake_case__						,   repo_type='dataset'  )						,   'r'  )  )
	A     = {int(snake_case__  ): v for k, v in idalabel.items()}
	A     = idalabel
	A     = {v: k for k, v in idalabel.items()}
	if size == "b0":
		pass
	elif size == "b1":
		A     = [64, 128, 320, 512]
		A     = 256
	elif size == "b2":
		A     = [64, 128, 320, 512]
		A     = 768
		A     = [3, 4, 6, 3]
	elif size == "b3":
		A     = [64, 128, 320, 512]
		A     = 768
		A     = [3, 4, 18, 3]
	elif size == "b4":
		A     = [64, 128, 320, 512]
		A     = 768
		A     = [3, 8, 27, 3]
	elif size == "b5":
		A     = [64, 128, 320, 512]
		A     = 768
		A     = [3, 6, 40, 3]
	else:
		raise ValueError(F'Size {size} not supported'  )

	# load image processor (only resize + normalize)
	A     = SegformerImageProcessor(
	    image_scale=(512, 512)						,   keep_ratio=snake_case__						,   align=snake_case__						,   do_random_crop=snake_case__  )

	# prepare image
	A     = prepare_img()
	A     = image_processor(images=snake_case__						,   return_tensors='pt'  ).pixel_values

	logger.info(F'Converting model {model_name}...'  )

	# load original state dict
	if encoder_only:
		A     = torch.load(snake_case__						,   map_location=torch.device('cpu'  )  )
	else:
		A     = torch.load(snake_case__						,   map_location=torch.device('cpu'  )  )['state_dict']

	# rename keys
	A     = rename_keys(snake_case__						,   encoder_only=snake_case__  )
	if not encoder_only:
		del state_dict["decode_head.conv_seg.weight"]
		del state_dict["decode_head.conv_seg.bias"]

	# key and value matrices need special treatment
	read_in_k_v(snake_case__						,   snake_case__  )

	# create HuggingFace model and load state dict
	if encoder_only:
		A     = False
		A     = SegformerForImageClassification(snake_case__  )
	else:
		A     = SegformerForSemanticSegmentation(snake_case__  )
	model.load_state_dict(snake_case__  )
	model.eval()

	# forward pass
	A     = model(snake_case__  )
	A     = outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		A     = torch.tensor(
		    [
		        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
		        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
		        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
		    ]  )
	elif model_name == "segformer.b1.512x512.ade.160k":
		A     = torch.tensor(
		    [
		        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
		        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
		        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
		    ]  )
	elif model_name == "segformer.b2.512x512.ade.160k":
		A     = torch.tensor(
		    [
		        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
		        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
		        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
		    ]  )
	elif model_name == "segformer.b3.512x512.ade.160k":
		A     = torch.tensor(
		    [
		        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
		        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
		        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
		    ]  )
	elif model_name == "segformer.b4.512x512.ade.160k":
		A     = torch.tensor(
		    [
		        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
		        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
		        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
		    ]  )
	elif model_name == "segformer.b5.640x640.ade.160k":
		A     = torch.tensor(
		    [
		        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
		        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
		        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
		    ]  )
	# Cityscapes checkpoints
	elif model_name == "segformer.b0.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
		        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
		        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
		    ]  )
	elif model_name == "segformer.b0.512x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
		        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
		        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
		    ]  )
	elif model_name == "segformer.b0.640x1280.city.160k":
		A     = torch.tensor(
		    [
		        [
		            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
		            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
		            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
		        ],
		        [
		            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
		            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
		            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
		        ],
		        [
		            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
		            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
		            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
		        ],
		    ]  )
	elif model_name == "segformer.b0.768x768.city.160k":
		A     = torch.tensor(
		    [
		        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
		        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
		        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
		    ]  )
	elif model_name == "segformer.b1.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
		        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
		        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
		    ]  )
	elif model_name == "segformer.b2.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
		        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
		        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
		    ]  )
	elif model_name == "segformer.b3.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
		        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
		        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
		    ]  )
	elif model_name == "segformer.b4.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
		        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
		        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
		    ]  )
	elif model_name == "segformer.b5.1024x1024.city.160k":
		A     = torch.tensor(
		    [
		        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
		        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
		        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
		    ]  )
	else:
		A     = logits.argmax(-1  ).item()
		print('Predicted class:'						,   model.config.idalabel[predicted_class_idx]  )

	# verify logits
	if not encoder_only:
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3, :3, :3]						,   snake_case__						,   atol=1e-2  )

	# finally, save model and image processor
	logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'  )
	Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
	model.save_pretrained(snake_case__  )
	image_processor.save_pretrained(snake_case__  )


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

		parser.add_argument(
		    '''--model_name''',
		    default='''segformer.b0.512x512.ade.160k''',
		    type=str,
		    help='''Name of the model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		_lowercase				 =					parser.parse_args()
		convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     AudioLDMPipeline
						_lowerCamelCase:     Optional[int]   =     TEXT_TO_AUDIO_PARAMS
						_lowerCamelCase:     Optional[int]   =     TEXT_TO_AUDIO_BATCH_PARAMS
						_lowerCamelCase:     Optional[int]   =     frozenset(
						    [
						        '''num_inference_steps''',
						        '''num_waveforms_per_prompt''',
						        '''generator''',
						        '''latents''',
						        '''output_type''',
						        '''return_dict''',
						        '''callback''',
						        '''callback_steps''',
						    ]     )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Any:
							torch.manual_seed(0   )
							A     = UNetaDConditionModel(
							    block_out_channels=(32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=4	,out_channels=4	,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')	,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')	,cross_attention_dim=(32, 64)	,class_embed_type='simple_projection'	,projection_class_embeddings_input_dim=32	,class_embeddings_concat=A_	,)
							A     = DDIMScheduler(
							    beta_start=0.0_00_85	,beta_end=0.0_12	,beta_schedule='scaled_linear'	,clip_sample=A_	,set_alpha_to_one=A_	,)
							torch.manual_seed(0   )
							A     = AutoencoderKL(
							    block_out_channels=[32, 64]	,in_channels=1	,out_channels=1	,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']	,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']	,latent_channels=4	,)
							torch.manual_seed(0   )
							A     = ClapTextConfig(
							    bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1000	,projection_dim=32	,)
							A     = ClapTextModelWithProjection(A_   )
							A     = RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'	,model_max_length=77   )

							A     = SpeechTaHifiGanConfig(
							    model_in_dim=8	,sampling_rate=1_6000	,upsample_initial_channel=16	,upsample_rates=[2, 2]	,upsample_kernel_sizes=[4, 4]	,resblock_kernel_sizes=[3, 7]	,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]	,normalize_before=A_	,)

							A     = SpeechTaHifiGan(A_   )

							A     = {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'vocoder': vocoder,
							}
							return components


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   Any	,A_  :   Dict=0   )  -> str:
							if str(A_   ).startswith('mps'   ):
								A     = torch.manual_seed(A_   )
							else:
								A     = torch.Generator(device=A_   ).manual_seed(A_   )
							A     = {
							    'prompt': 'A hammer hitting a wooden surface',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							}
							return inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							A     = 'cpu'  # ensure determinism for the device-dependent torch.Generator

							A     = self.get_dummy_components()
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs(A_   )
							A     = audioldm_pipe(**A_   )
							A     = output.audios[0]

							assert audio.ndim == 1
							assert len(A_   ) == 256

							A     = audio[:10]
							A     = np.array(
							    [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]   )

							assert np.abs(audio_slice - expected_slice   ).max() < 1e-2


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Tuple:
							A     = self.get_dummy_components()
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs(A_   )
							A     = 3 * [inputs['prompt']]

							# forward
							A     = audioldm_pipe(**A_   )
							A     = output.audios[0]

							A     = self.get_dummy_inputs(A_   )
							A     = 3 * [inputs.pop('prompt'   )]

							A     = audioldm_pipe.tokenizer(
							    A_	,padding='max_length'	,max_length=audioldm_pipe.tokenizer.model_max_length	,truncation=A_	,return_tensors='pt'	,)
							A     = text_inputs['input_ids'].to(A_   )

							A     = audioldm_pipe.text_encoder(
							    A_	,)
							A     = prompt_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							A     = F.normalize(A_	,dim=-1   )

							A     = prompt_embeds

							# forward
							A     = audioldm_pipe(**A_   )
							A     = output.audios[0]

							assert np.abs(audio_a - audio_a   ).max() < 1e-2


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Any:
							A     = self.get_dummy_components()
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs(A_   )
							A     = 3 * ['this is a negative prompt']
							A     = negative_prompt
							A     = 3 * [inputs['prompt']]

							# forward
							A     = audioldm_pipe(**A_   )
							A     = output.audios[0]

							A     = self.get_dummy_inputs(A_   )
							A     = 3 * [inputs.pop('prompt'   )]

							A     = []
							for p in [prompt, negative_prompt]:
								A     = audioldm_pipe.tokenizer(
								    A_	,padding='max_length'	,max_length=audioldm_pipe.tokenizer.model_max_length	,truncation=A_	,return_tensors='pt'	,)
								A     = text_inputs['input_ids'].to(A_   )

								A     = audioldm_pipe.text_encoder(
								    A_	,)
								A     = text_embeds.text_embeds
								# additional L_2 normalization over each hidden-state
								A     = F.normalize(A_	,dim=-1   )

								embeds.append(A_   )

							A ,   A     = embeds

							# forward
							A     = audioldm_pipe(**A_   )
							A     = output.audios[0]

							assert np.abs(audio_a - audio_a   ).max() < 1e-2


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> int:
							A     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
							A     = self.get_dummy_components()
							A     = PNDMScheduler(skip_prk_steps=A_   )
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs(A_   )
							A     = 'egg cracking'
							A     = audioldm_pipe(**A_	,negative_prompt=A_   )
							A     = output.audios[0]

							assert audio.ndim == 1
							assert len(A_   ) == 256

							A     = audio[:10]
							A     = np.array(
							    [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]   )

							assert np.abs(audio_slice - expected_slice   ).max() < 1e-2


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							A     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
							A     = self.get_dummy_components()
							A     = PNDMScheduler(skip_prk_steps=A_   )
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = 'A hammer hitting a wooden surface'

							# test num_waveforms_per_prompt=1 (default)
							A     = audioldm_pipe(A_	,num_inference_steps=2   ).audios

							assert audios.shape == (1, 256)

							# test num_waveforms_per_prompt=1 (default) for batch of prompts
							A     = 2
							A     = audioldm_pipe([prompt] * batch_size	,num_inference_steps=2   ).audios

							assert audios.shape == (batch_size, 256)

							# test num_waveforms_per_prompt for single prompt
							A     = 2
							A     = audioldm_pipe(A_	,num_inference_steps=2	,num_waveforms_per_prompt=A_   ).audios

							assert audios.shape == (num_waveforms_per_prompt, 256)

							# test num_waveforms_per_prompt for batch of prompts
							A     = 2
							A     = audioldm_pipe(
							    [prompt] * batch_size	,num_inference_steps=2	,num_waveforms_per_prompt=A_   ).audios

							assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Optional[Any]:
							A     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
							A     = self.get_dummy_components()
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )
							A     = audioldm_pipe.vocoder.config.sampling_rate

							A     = self.get_dummy_inputs(A_   )
							A     = audioldm_pipe(audio_length_in_s=0.0_16	,**A_   )
							A     = output.audios[0]

							assert audio.ndim == 1
							assert len(A_   ) / vocoder_sampling_rate == 0.0_16

							A     = audioldm_pipe(audio_length_in_s=0.0_32	,**A_   )
							A     = output.audios[0]

							assert audio.ndim == 1
							assert len(A_   ) / vocoder_sampling_rate == 0.0_32


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> List[Any]:
							A     = self.get_dummy_components()
							A     = AudioLDMPipeline(**A_   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = ['hey']

							A     = audioldm_pipe(A_	,num_inference_steps=1   )
							A     = output.audios.shape
							assert audio_shape == (1, 256)

							A     = audioldm_pipe.vocoder.config
							config.model_in_dim *= 2
							A     = SpeechTaHifiGan(A_   ).to(A_   )
							A     = audioldm_pipe(A_	,num_inference_steps=1   )
							A     = output.audios.shape
							# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
							assert audio_shape == (1, 256)


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> int:
							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							self._test_inference_batch_single_identical(test_mean_pixel_difference=A_   )




						@unittest.skipIf(
						    torch_device != 'cuda' or not is_xformers_available()	,reason='XFormers attention is only available with CUDA and `xformers` installed'	,)
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> List[str]:
							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_   )







@slow
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[str]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   List[Any]	,A_  :   str="cpu"	,A_  :   List[str]=torch.floataa	,A_  :   str=0   )  -> List[Any]:
							A     = torch.Generator(device=A_   ).manual_seed(A_   )
							A     = np.random.RandomState(A_   ).standard_normal((1, 8, 128, 16)   )
							A     = torch.from_numpy(A_   ).to(device=A_	,dtype=A_   )
							A     = {
							    'prompt': 'A hammer hitting a wooden surface',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 2.5,
							}
							return inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> int:
							A     = AudioLDMPipeline.from_pretrained('cvssp/audioldm'   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_inputs(A_   )
							A     = 25
							A     = audioldm_pipe(**A_   ).audios[0]

							assert audio.ndim == 1
							assert len(A_   ) == 8_1920

							A     = audio[7_7230:7_7240]
							A     = np.array(
							    [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]   )
							A     = np.abs(expected_slice - audio_slice   ).max()
							assert max_diff < 1e-2




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[Any]:
							A     = AudioLDMPipeline.from_pretrained('cvssp/audioldm'   )
							A     = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config   )
							A     = audioldm_pipe.to(A_   )
							audioldm_pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_inputs(A_   )
							A     = audioldm_pipe(**A_   ).audios[0]

							assert audio.ndim == 1
							assert len(A_   ) == 8_1920

							A     = audio[2_7780:2_7790]
							A     = np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]   )
							A     = np.abs(expected_slice - audio_slice   ).max()
							assert max_diff < 3e-2