

'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					lowerCamelCase_  				=      MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										lowerCamelCase_  				=      [1_44, 1_92, 2_40]
										lowerCamelCase_  				=      [16, 32, 64, 96, 1_28, 1_60, 6_40]
					elif "mobilevit_xs" in mobilevit_name:
										lowerCamelCase_  				=      [96, 1_20, 1_44]
										lowerCamelCase_  				=      [16, 32, 48, 64, 80, 96, 3_84]
					elif "mobilevit_xxs" in mobilevit_name:
										lowerCamelCase_  				=      [64, 80, 96]
										lowerCamelCase_  				=      [16, 16, 24, 48, 64, 80, 3_20]
										lowerCamelCase_  				=      0.05
										lowerCamelCase_  				=      2.0

					if mobilevit_name.startswith('deeplabv3_'	):
										lowerCamelCase_  				=      5_12
										lowerCamelCase_  				=      16
										lowerCamelCase_  				=      21
										lowerCamelCase_  				=      'pascal-voc-id2label.json'
					else:
										lowerCamelCase_  				=      10_00
										lowerCamelCase_  				=      'imagenet-1k-id2label.json'

					lowerCamelCase_  				=      'huggingface/label-files'
					lowerCamelCase_  				=      json.load(open(hf_hub_download(__UpperCamelCase    ,__UpperCamelCase    ,repo_type='dataset'	)    ,'r'	)	)
					lowerCamelCase_  				=      {int(__UpperCamelCase	): v for k, v in idalabel.items()}
					lowerCamelCase_  				=      idalabel
					lowerCamelCase_  				=      {v: k for k, v in idalabel.items()}

					return config







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Optional[Any]:
					for i in range(1    ,6	):
										if f'''layer_{i}.''' in name:
															lowerCamelCase_  				=      name.replace(f'''layer_{i}.'''    ,f'''encoder.layer.{i - 1}.'''	)

					if "conv_1." in name:
										lowerCamelCase_  				=      name.replace('conv_1.'    ,'conv_stem.'	)
					if ".block." in name:
										lowerCamelCase_  				=      name.replace('.block.'    ,'.'	)

					if "exp_1x1" in name:
										lowerCamelCase_  				=      name.replace('exp_1x1'    ,'expand_1x1'	)
					if "red_1x1" in name:
										lowerCamelCase_  				=      name.replace('red_1x1'    ,'reduce_1x1'	)
					if ".local_rep.conv_3x3." in name:
										lowerCamelCase_  				=      name.replace('.local_rep.conv_3x3.'    ,'.conv_kxk.'	)
					if ".local_rep.conv_1x1." in name:
										lowerCamelCase_  				=      name.replace('.local_rep.conv_1x1.'    ,'.conv_1x1.'	)
					if ".norm." in name:
										lowerCamelCase_  				=      name.replace('.norm.'    ,'.normalization.'	)
					if ".conv." in name:
										lowerCamelCase_  				=      name.replace('.conv.'    ,'.convolution.'	)
					if ".conv_proj." in name:
										lowerCamelCase_  				=      name.replace('.conv_proj.'    ,'.conv_projection.'	)

					for i in range(0    ,2	):
										for j in range(0    ,4	):
															if f'''.{i}.{j}.''' in name:
																				lowerCamelCase_  				=      name.replace(f'''.{i}.{j}.'''    ,f'''.{i}.layer.{j}.'''	)

					for i in range(2    ,6	):
										for j in range(0    ,4	):
															if f'''.{i}.{j}.''' in name:
																				lowerCamelCase_  				=      name.replace(f'''.{i}.{j}.'''    ,f'''.{i}.'''	)
																				if "expand_1x1" in name:
																									lowerCamelCase_  				=      name.replace('expand_1x1'    ,'downsampling_layer.expand_1x1'	)
																				if "conv_3x3" in name:
																									lowerCamelCase_  				=      name.replace('conv_3x3'    ,'downsampling_layer.conv_3x3'	)
																				if "reduce_1x1" in name:
																									lowerCamelCase_  				=      name.replace('reduce_1x1'    ,'downsampling_layer.reduce_1x1'	)

					for i in range(2    ,5	):
										if f'''.global_rep.{i}.weight''' in name:
															lowerCamelCase_  				=      name.replace(f'''.global_rep.{i}.weight'''    ,'.layernorm.weight'	)
										if f'''.global_rep.{i}.bias''' in name:
															lowerCamelCase_  				=      name.replace(f'''.global_rep.{i}.bias'''    ,'.layernorm.bias'	)

					if ".global_rep." in name:
										lowerCamelCase_  				=      name.replace('.global_rep.'    ,'.transformer.'	)
					if ".pre_norm_mha.0." in name:
										lowerCamelCase_  				=      name.replace('.pre_norm_mha.0.'    ,'.layernorm_before.'	)
					if ".pre_norm_mha.1.out_proj." in name:
										lowerCamelCase_  				=      name.replace('.pre_norm_mha.1.out_proj.'    ,'.attention.output.dense.'	)
					if ".pre_norm_ffn.0." in name:
										lowerCamelCase_  				=      name.replace('.pre_norm_ffn.0.'    ,'.layernorm_after.'	)
					if ".pre_norm_ffn.1." in name:
										lowerCamelCase_  				=      name.replace('.pre_norm_ffn.1.'    ,'.intermediate.dense.'	)
					if ".pre_norm_ffn.4." in name:
										lowerCamelCase_  				=      name.replace('.pre_norm_ffn.4.'    ,'.output.dense.'	)
					if ".transformer." in name:
										lowerCamelCase_  				=      name.replace('.transformer.'    ,'.transformer.layer.'	)

					if ".aspp_layer." in name:
										lowerCamelCase_  				=      name.replace('.aspp_layer.'    ,'.'	)
					if ".aspp_pool." in name:
										lowerCamelCase_  				=      name.replace('.aspp_pool.'    ,'.'	)
					if "seg_head." in name:
										lowerCamelCase_  				=      name.replace('seg_head.'    ,'segmentation_head.'	)
					if "segmentation_head.classifier.classifier." in name:
										lowerCamelCase_  				=      name.replace('segmentation_head.classifier.classifier.'    ,'segmentation_head.classifier.'	)

					if "classifier.fc." in name:
										lowerCamelCase_  				=      name.replace('classifier.fc.'    ,'classifier.'	)
					elif (not base_model) and ("segmentation_head." not in name):
										lowerCamelCase_  				=      'mobilevit.' + name

					return name







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=False	)       ->			List[str]:
					if base_model:
										lowerCamelCase_  				=      ''
					else:
										lowerCamelCase_  				=      'mobilevit.'

					for key in orig_state_dict.copy().keys():
										lowerCamelCase_  				=      orig_state_dict.pop(__UpperCamelCase	)

										if key[:8] == "encoder.":
															lowerCamelCase_  				=      key[8:]

										if "qkv" in key:
															lowerCamelCase_  				=      key.split('.'	)
															lowerCamelCase_  				=      int(key_split[0][6:]	) - 1
															lowerCamelCase_  				=      int(key_split[3]	)
															lowerCamelCase_  				=      model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''	)
															lowerCamelCase_  				=      layer.transformer.layer[transformer_num].attention.attention.all_head_size
															lowerCamelCase_  				=      (
															    f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
															)
															if "weight" in key:
																				lowerCamelCase_  				=      val[:dim, :]
																				lowerCamelCase_  				=      val[dim : dim * 2, :]
																				lowerCamelCase_  				=      val[-dim:, :]
															else:
																				lowerCamelCase_  				=      val[:dim]
																				lowerCamelCase_  				=      val[dim : dim * 2]
																				lowerCamelCase_  				=      val[-dim:]
										else:
															lowerCamelCase_  				=      val

					return orig_state_dict







def   _UpperCamelCase	(		)       ->			List[Any]:
					lowerCamelCase_  				=      'http://images.cocodataset.org/val2017/000000039769.jpg'
					lowerCamelCase_  				=      Image.open(requests.get(__UpperCamelCase    ,stream=__UpperCamelCase	).raw	)
					return im







@torch.no_grad()
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Any:
					lowerCamelCase_  				=      get_mobilevit_config(__UpperCamelCase	)

					# load original state_dict
					lowerCamelCase_  				=      torch.load(__UpperCamelCase    ,map_location='cpu'	)

					# load 🤗 model
					if mobilevit_name.startswith('deeplabv3_'	):
										lowerCamelCase_  				=      MobileViTForSemanticSegmentation(__UpperCamelCase	).eval()
					else:
										lowerCamelCase_  				=      MobileViTForImageClassification(__UpperCamelCase	).eval()

					lowerCamelCase_  				=      convert_state_dict(__UpperCamelCase    ,__UpperCamelCase	)
					model.load_state_dict(__UpperCamelCase	)

					# Check outputs on an image, prepared by MobileViTImageProcessor
					lowerCamelCase_  				=      MobileViTImageProcessor(crop_size=config.image_size    ,size=config.image_size + 32	)
					lowerCamelCase_  				=      image_processor(images=prepare_img()    ,return_tensors='pt'	)
					lowerCamelCase_  				=      model(**__UpperCamelCase	)
					lowerCamelCase_  				=      outputs.logits

					if mobilevit_name.startswith('deeplabv3_'	):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ]	)
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ]	)
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ]	)
										else:
															raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

										assert torch.allclose(logits[0, :3, :3, :3]    ,__UpperCamelCase    ,atol=1e-4	)
					else:
										assert logits.shape == (1, 10_00)

										if mobilevit_name == "mobilevit_s":
															lowerCamelCase_  				=      torch.tensor([-0.9866, 0.2392, -1.1241]	)
										elif mobilevit_name == "mobilevit_xs":
															lowerCamelCase_  				=      torch.tensor([-2.4761, -0.9399, -1.9587]	)
										elif mobilevit_name == "mobilevit_xxs":
															lowerCamelCase_  				=      torch.tensor([-1.9364, -1.2327, -0.4653]	)
										else:
															raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

										assert torch.allclose(logits[0, :3]    ,__UpperCamelCase    ,atol=1e-4	)

					Path(__UpperCamelCase	).mkdir(exist_ok=__UpperCamelCase	)
					print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''	)
					model.save_pretrained(__UpperCamelCase	)
					print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
					image_processor.save_pretrained(__UpperCamelCase	)

					if push_to_hub:
										lowerCamelCase_  				=      {
										    'mobilevit_s': 'mobilevit-small',
										    'mobilevit_xs': 'mobilevit-x-small',
										    'mobilevit_xxs': 'mobilevit-xx-small',
										    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
										    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
										    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
										}

										print('Pushing to the hub...'	)
										lowerCamelCase_  				=      model_mapping[mobilevit_name]
										image_processor.push_to_hub(__UpperCamelCase    ,organization='apple'	)
										model.push_to_hub(__UpperCamelCase    ,organization='apple'	)


if __name__ == "__main__":
				A_	= argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--mobilevit_name",
				    default="mobilevit_s",
				    type=str,
				    help=(
				        "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
				        " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
				    ),
				)
				parser.add_argument(
				    "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				A_	= parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






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 SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	AudioLDMPipeline
			__A    	=	TEXT_TO_AUDIO_PARAMS
			__A    	=	TEXT_TO_AUDIO_BATCH_PARAMS
			__A    	=	frozenset(
			    [
			        """num_inference_steps""",
			        """num_waveforms_per_prompt""",
			        """generator""",
			        """latents""",
			        """output_type""",
			        """return_dict""",
			        """callback""",
			        """callback_steps""",
			    ]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			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=__UpperCamelCase			,					)
				snake_case_   		=			DDIMScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_one=__UpperCamelCase			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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				)
				snake_case_   		=			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=10_00			,					projection_dim=32			,					)
				snake_case_   		=			ClapTextModelWithProjection(__UpperCamelCase				)
				snake_case_   		=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'			,					model_max_length=77				)

				snake_case_   		=			SpeechTaHifiGanConfig(
				    model_in_dim=8			,					sampling_rate=1_60_00			,					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=__UpperCamelCase			,					)

				snake_case_   		=			SpeechTaHifiGan(__UpperCamelCase				)

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A hammer hitting a wooden surface',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 2_56

				snake_case_   		=			audio[:10]
				snake_case_   		=			np.array(
				    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]				)

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs['prompt']]

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs.pop('prompt'				)]

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

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

				snake_case_   		=			prompt_embeds

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			3 * [inputs.pop('prompt'				)]

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

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

					embeds.append(__UpperCamelCase				)

				snake_case_		,							snake_case_   		=			embeds

				# forward
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 2_56

				snake_case_   		=			audio[:10]
				snake_case_   		=			np.array(
				    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]				)

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			PNDMScheduler(skip_prk_steps=__UpperCamelCase				)
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'A hammer hitting a wooden surface'

				# test num_waveforms_per_prompt=1 (default)
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=2				).audios

				assert audios.shape == (1, 2_56)

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

				assert audios.shape == (batch_size, 2_56)

				# test num_waveforms_per_prompt for single prompt
				snake_case_   		=			2
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=2			,					num_waveforms_per_prompt=__UpperCamelCase				).audios

				assert audios.shape == (num_waveforms_per_prompt, 2_56)

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

				assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.vocoder.config.sampling_rate

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(audio_length_in_s=0.016			,					**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) / vocoder_sampling_rate == 0.016

				snake_case_   		=			audioldm_pipe(audio_length_in_s=0.032			,					**__UpperCamelCase				)
				snake_case_   		=			output.audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) / vocoder_sampling_rate == 0.032


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			AudioLDMPipeline(**__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			['hey']

				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=1				)
				snake_case_   		=			output.audios.shape
				assert audio_shape == (1, 2_56)

				snake_case_   		=			audioldm_pipe.vocoder.config
				config.model_in_dim *= 2
				snake_case_   		=			SpeechTaHifiGan(__UpperCamelCase				).to(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(__UpperCamelCase			,					num_inference_steps=1				)
				snake_case_   		=			output.audios.shape
				# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
				assert audio_shape == (1, 2_56)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_inference_batch_single_identical(test_mean_pixel_difference=__UpperCamelCase				)







			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()			,					reason='XFormers attention is only available with CUDA and `xformers` installed'			,					)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCamelCase				)







@slow
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase="cpu"			,					__UpperCamelCase=torch.floataa			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			np.random.RandomState(__UpperCamelCase				).standard_normal((1, 8, 1_28, 16)				)
				snake_case_   		=			torch.from_numpy(__UpperCamelCase				).to(device=__UpperCamelCase			,					dtype=__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A hammer hitting a wooden surface',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 2.5,
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_inputs(__UpperCamelCase				)
				snake_case_   		=			25
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				).audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 8_19_20

				snake_case_   		=			audio[7_72_30:7_72_40]
				snake_case_   		=			np.array(
				    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]				)
				snake_case_   		=			np.abs(expected_slice - audio_slice				).max()
				assert max_diff < 1E-2







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'				)
				snake_case_   		=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config				)
				snake_case_   		=			audioldm_pipe.to(__UpperCamelCase				)
				audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_inputs(__UpperCamelCase				)
				snake_case_   		=			audioldm_pipe(**__UpperCamelCase				).audios[0]

				assert audio.ndim == 1
				assert len(__UpperCamelCase				) == 8_19_20

				snake_case_   		=			audio[2_77_80:2_77_90]
				snake_case_   		=			np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]				)
				snake_case_   		=			np.abs(expected_slice - audio_slice				).max()
				assert max_diff < 3E-2
