






"""simple docstring"""






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
				import numpy as np
				import torch

				from . import (
				    AlbertForPreTraining,
				    BartForConditionalGeneration,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    CamembertForMaskedLM,
				    CTRLLMHeadModel,
				    DistilBertForMaskedLM,
				    DistilBertForQuestionAnswering,
				    DPRContextEncoder,
				    DPRQuestionEncoder,
				    DPRReader,
				    ElectraForPreTraining,
				    FlaubertWithLMHeadModel,
				    GPTaLMHeadModel,
				    LayoutLMForMaskedLM,
				    LxmertForPreTraining,
				    LxmertVisualFeatureEncoder,
				    OpenAIGPTLMHeadModel,
				    RobertaForMaskedLM,
				    RobertaForSequenceClassification,
				    TaForConditionalGeneration,
				    TransfoXLLMHeadModel,
				    XLMRobertaForMaskedLM,
				    XLMWithLMHeadModel,
				    XLNetLMHeadModel,
				)


logging.set_verbosity_info()

UpperCamelCase__						=			{
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[int]      ,       snake_case			:					Any      ,       snake_case			:					List[Any]=False      ,       snake_case			:					Optional[Any]=True    ):


					if model_type not in MODEL_CLASSES:
										raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}.'    )

					_lowerCAmelCase:str									=  MODEL_CLASSES[model_type]

					# Initialise TF model
					if config_file in aws_config_map:
										_lowerCAmelCase:Optional[Any]									=  cached_file(lowerCamelCase__      ,       lowerCamelCase__      ,       force_download=not use_cached_models    )
					_lowerCAmelCase:Tuple									=  config_class.from_json_file(lowerCamelCase__    )
					_lowerCAmelCase:List[Any]									=  True
					_lowerCAmelCase:Dict									=  True
					print(F'Building TensorFlow model from configuration: {config}'    )
					_lowerCAmelCase:Tuple									=  model_class(lowerCamelCase__    )

					# Load weights from tf checkpoint
					if pytorch_checkpoint_path in aws_config_map.keys():
										_lowerCAmelCase:Union[str, Any]									=  cached_file(
										    lowerCamelCase__      ,       lowerCamelCase__      ,       force_download=not use_cached_models    )
					# Load PyTorch checkpoint in tf2 model:
					_lowerCAmelCase:str									=  load_pytorch_checkpoint_in_tfa_model(lowerCamelCase__      ,       lowerCamelCase__    )

					if compare_with_pt_model:
										_lowerCAmelCase:int									=  tf_model(tf_model.dummy_inputs      ,       training=lowerCamelCase__    )  # build the network

										_lowerCAmelCase:Tuple									=  torch.load(lowerCamelCase__      ,       map_location='''cpu'''    )
										_lowerCAmelCase:Optional[Any]									=  pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=lowerCamelCase__      ,       config=lowerCamelCase__      ,       state_dict=lowerCamelCase__    )

										with torch.no_grad():
															_lowerCAmelCase:Any									=  pt_model(**pt_model.dummy_inputs    )

										_lowerCAmelCase:int									=  pto[0].numpy()
										_lowerCAmelCase:List[Any]									=  tfo[0].numpy()
										_lowerCAmelCase:List[Any]									=  np.amax(np.abs(np_pt - np_tf    )    )
										print(F'Max absolute difference between models outputs {diff}'    )
										assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

					# Save pytorch-model
					print(F'Save TensorFlow model to {tf_dump_path}'    )
					tf_model.save_weights(lowerCamelCase__      ,       save_format='''h5'''    )





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					List[str]      ,       snake_case			:					Any=None      ,       snake_case			:					Optional[Any]=None      ,       snake_case			:					List[Any]=False      ,       snake_case			:					Optional[int]=False      ,       snake_case			:					Tuple=False      ,       snake_case			:					str=False      ,       ):


					if args_model_type is None:
										_lowerCAmelCase:Tuple									=  list(MODEL_CLASSES.keys()    )
					else:
										_lowerCAmelCase:int									=  [args_model_type]

					for j, model_type in enumerate(lowerCamelCase__      ,       start=1    ):
										print('''=''' * 100    )
										print(F' Converting model type {j}/{len(lowerCamelCase__    )}: {model_type}'    )
										print('''=''' * 100    )
										if model_type not in MODEL_CLASSES:
															raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()    )}.'    )

										_lowerCAmelCase:Dict									=  MODEL_CLASSES[model_type]

										if model_shortcut_names_or_path is None:
															_lowerCAmelCase:int									=  list(aws_model_maps.keys()    )
										if config_shortcut_names_or_path is None:
															_lowerCAmelCase:Union[str, Any]									=  model_shortcut_names_or_path

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(lowerCamelCase__      ,       lowerCamelCase__    )      ,       start=1    ):
															print('''-''' * 100    )
															if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																				if not only_convert_finetuned_models:
																									print(F'    Skipping finetuned checkpoint {model_shortcut_name}'    )
																									continue
																				_lowerCAmelCase:Tuple									=  model_shortcut_name
															elif only_convert_finetuned_models:
																				print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'    )
																				continue
															print(
															    F'    Converting checkpoint {i}/{len(lowerCamelCase__    )}: {model_shortcut_name} - model_type {model_type}'    )
															print('''-''' * 100    )

															if config_shortcut_name in aws_config_map:
																				_lowerCAmelCase:Optional[Any]									=  cached_file(lowerCamelCase__      ,       lowerCamelCase__      ,       force_download=not use_cached_models    )
															else:
																				_lowerCAmelCase:Union[str, Any]									=  config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				_lowerCAmelCase:Union[str, Any]									=  cached_file(lowerCamelCase__      ,       lowerCamelCase__      ,       force_download=not use_cached_models    )
															else:
																				_lowerCAmelCase:Any									=  model_shortcut_name

															if os.path.isfile(lowerCamelCase__    ):
																				_lowerCAmelCase:Optional[Any]									=  "converted_model"

															convert_pt_checkpoint_to_tf(
															    model_type=lowerCamelCase__      ,       pytorch_checkpoint_path=lowerCamelCase__      ,       config_file=lowerCamelCase__      ,       tf_dump_path=os.path.join(lowerCamelCase__      ,       model_shortcut_name + '''-tf_model.h5'''    )      ,       compare_with_pt_model=lowerCamelCase__      ,       )
															if remove_cached_files:
																				os.remove(lowerCamelCase__    )
																				os.remove(lowerCamelCase__    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
				)
				parser.add_argument(
				    '''--model_type''',
				    default=None,
				    type=str,
				    help=(
				        F"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
				        '''convert all the models from AWS.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_checkpoint_path''',
				    default=None,
				    type=str,
				    help=(
				        '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
				        '''If not given, will download and convert all the checkpoints from AWS.'''
				    ),
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    help=(
				        '''The config json file corresponding to the pre-trained model. \n'''
				        '''This specifies the model architecture. If not given and '''
				        '''--pytorch_checkpoint_path is not given or is a shortcut name '''
				        '''use the configuration associated to the shortcut name on the AWS'''
				    ),
				)
				parser.add_argument(
				    '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
				)
				parser.add_argument(
				    '''--use_cached_models''',
				    action='''store_true''',
				    help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
				)
				parser.add_argument(
				    '''--remove_cached_files''',
				    action='''store_true''',
				    help='''Remove pytorch models after conversion (save memory when converting in batches).''',
				)
				parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
				UpperCamelCase__						=			parser.parse_args()

				# if args.pytorch_checkpoint_path is not None:
				#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
				#                                 args.pytorch_checkpoint_path,
				#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
				#                                 args.tf_dump_path,
				#                                 compare_with_pt_model=args.compare_with_pt_model,
				#                                 use_cached_models=args.use_cached_models)
				# else:
				convert_all_pt_checkpoints_to_tf(
				    args.model_type.lower() if args.model_type is not None else None,
				    args.tf_dump_path,
				    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
				    if args.pytorch_checkpoint_path is not None
				    else None,
				    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
				    compare_with_pt_model=args.compare_with_pt_model,
				    use_cached_models=args.use_cached_models,
				    remove_cached_files=args.remove_cached_files,
				    only_convert_finetuned_models=args.only_convert_finetuned_models,
				)












"""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   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = AudioLDMPipeline
						snake_case__             = TEXT_TO_AUDIO_PARAMS
						snake_case__             = TEXT_TO_AUDIO_BATCH_PARAMS
						snake_case__             = frozenset(
						    [
						        '''num_inference_steps''',
						        '''num_waveforms_per_prompt''',
						        '''generator''',
						        '''latents''',
						        '''output_type''',
						        '''return_dict''',
						        '''callback''',
						        '''callback_steps''',
						    ]  )





						def       __UpperCamelCase     (							self      :	int)						->       Any:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:int									=  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__     ,)
											_lowerCAmelCase:Optional[int]									=  DDIMScheduler(
											    beta_start=0.00085     ,beta_end=0.012     ,beta_schedule='''scaled_linear'''     ,clip_sample=a__     ,set_alpha_to_one=a__     ,)
											torch.manual_seed(0)
											_lowerCAmelCase:Optional[Any]									=  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)
											_lowerCAmelCase:Dict									=  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     ,)
											_lowerCAmelCase:str									=  ClapTextModelWithProjection(a__)
											_lowerCAmelCase:Dict									=  RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''     ,model_max_length=77)

											_lowerCAmelCase:Union[str, Any]									=  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__     ,)

											_lowerCAmelCase:List[Any]									=  SpeechTaHifiGan(a__)

											_lowerCAmelCase:Optional[Any]									=  {
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''vocoder''': vocoder,
											}
											return components





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	int     ,a__      :	Tuple=0)						->       Optional[int]:




											"""simple docstring"""







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





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




											"""simple docstring"""







											_lowerCAmelCase:str									=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

											_lowerCAmelCase:Dict									=  self.get_dummy_components()
											_lowerCAmelCase:List[str]									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:Optional[int]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:str									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:Optional[Any]									=  audioldm_pipe(**a__)
											_lowerCAmelCase:List[str]									=  output.audios[0]

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

											_lowerCAmelCase:List[str]									=  audio[:10]
											_lowerCAmelCase:Dict									=  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       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=  self.get_dummy_components()
											_lowerCAmelCase:Optional[Any]									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:Union[str, Any]									=  audioldm_pipe.to(a__)
											_lowerCAmelCase:Any									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Dict									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:List[Any]									=  3 * [inputs['''prompt''']]

											# forward
											_lowerCAmelCase:Dict									=  audioldm_pipe(**a__)
											_lowerCAmelCase:Union[str, Any]									=  output.audios[0]

											_lowerCAmelCase:Tuple									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:Optional[Any]									=  3 * [inputs.pop('''prompt''')]

											_lowerCAmelCase:Tuple									=  audioldm_pipe.tokenizer(
											    a__     ,padding='''max_length'''     ,max_length=audioldm_pipe.tokenizer.model_max_length     ,truncation=a__     ,return_tensors='''pt'''     ,)
											_lowerCAmelCase:Optional[int]									=  text_inputs['''input_ids'''].to(a__)

											_lowerCAmelCase:List[Any]									=  audioldm_pipe.text_encoder(
											    a__     ,)
											_lowerCAmelCase:int									=  prompt_embeds.text_embeds
											# additional L_2 normalization over each hidden-state
											_lowerCAmelCase:List[Any]									=  F.normalize(a__     ,dim=-1)

											_lowerCAmelCase:int									=  prompt_embeds

											# forward
											_lowerCAmelCase:Tuple									=  audioldm_pipe(**a__)
											_lowerCAmelCase:Dict									=  output.audios[0]

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





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




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.get_dummy_components()
											_lowerCAmelCase:str									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:int									=  audioldm_pipe.to(a__)
											_lowerCAmelCase:Any									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Any									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:Tuple									=  3 * ['''this is a negative prompt''']
											_lowerCAmelCase:str									=  negative_prompt
											_lowerCAmelCase:List[Any]									=  3 * [inputs['''prompt''']]

											# forward
											_lowerCAmelCase:Optional[Any]									=  audioldm_pipe(**a__)
											_lowerCAmelCase:Any									=  output.audios[0]

											_lowerCAmelCase:Tuple									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:Tuple									=  3 * [inputs.pop('''prompt''')]

											_lowerCAmelCase:Tuple									=  []
											for p in [prompt, negative_prompt]:
																_lowerCAmelCase:str									=  audioldm_pipe.tokenizer(
																    a__     ,padding='''max_length'''     ,max_length=audioldm_pipe.tokenizer.model_max_length     ,truncation=a__     ,return_tensors='''pt'''     ,)
																_lowerCAmelCase:Dict									=  text_inputs['''input_ids'''].to(a__)

																_lowerCAmelCase:int									=  audioldm_pipe.text_encoder(
																    a__     ,)
																_lowerCAmelCase:List[Any]									=  text_embeds.text_embeds
																# additional L_2 normalization over each hidden-state
																_lowerCAmelCase:List[str]									=  F.normalize(a__     ,dim=-1)

																embeds.append(a__)

											_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  embeds

											# forward
											_lowerCAmelCase:List[str]									=  audioldm_pipe(**a__)
											_lowerCAmelCase:int									=  output.audios[0]

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





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




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_lowerCAmelCase:Union[str, Any]									=  self.get_dummy_components()
											_lowerCAmelCase:Union[str, Any]									=  PNDMScheduler(skip_prk_steps=a__)
											_lowerCAmelCase:Union[str, Any]									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:List[str]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Tuple									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:int									=  '''egg cracking'''
											_lowerCAmelCase:Union[str, Any]									=  audioldm_pipe(**a__     ,negative_prompt=a__)
											_lowerCAmelCase:Dict									=  output.audios[0]

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

											_lowerCAmelCase:Optional[int]									=  audio[:10]
											_lowerCAmelCase:Optional[Any]									=  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       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_lowerCAmelCase:Optional[int]									=  self.get_dummy_components()
											_lowerCAmelCase:str									=  PNDMScheduler(skip_prk_steps=a__)
											_lowerCAmelCase:Any									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:List[str]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Tuple									=  '''A hammer hitting a wooden surface'''

											# test num_waveforms_per_prompt=1 (default)
											_lowerCAmelCase:str									=  audioldm_pipe(a__     ,num_inference_steps=2).audios

											assert audios.shape == (1, 256)

											# test num_waveforms_per_prompt=1 (default) for batch of prompts
											_lowerCAmelCase:Dict									=  2
											_lowerCAmelCase:List[str]									=  audioldm_pipe([prompt] * batch_size     ,num_inference_steps=2).audios

											assert audios.shape == (batch_size, 256)

											# test num_waveforms_per_prompt for single prompt
											_lowerCAmelCase:Any									=  2
											_lowerCAmelCase:Tuple									=  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
											_lowerCAmelCase:str									=  2
											_lowerCAmelCase:List[Any]									=  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       __UpperCamelCase     (							self      :	Optional[Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_lowerCAmelCase:Dict									=  self.get_dummy_components()
											_lowerCAmelCase:Optional[Any]									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:List[str]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)
											_lowerCAmelCase:Optional[Any]									=  audioldm_pipe.vocoder.config.sampling_rate

											_lowerCAmelCase:Tuple									=  self.get_dummy_inputs(a__)
											_lowerCAmelCase:Optional[Any]									=  audioldm_pipe(audio_length_in_s=0.016     ,**a__)
											_lowerCAmelCase:List[Any]									=  output.audios[0]

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

											_lowerCAmelCase:List[str]									=  audioldm_pipe(audio_length_in_s=0.032     ,**a__)
											_lowerCAmelCase:Optional[int]									=  output.audios[0]

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





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.get_dummy_components()
											_lowerCAmelCase:Tuple									=  AudioLDMPipeline(**a__)
											_lowerCAmelCase:Dict									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Optional[int]									=  ['''hey''']

											_lowerCAmelCase:List[str]									=  audioldm_pipe(a__     ,num_inference_steps=1)
											_lowerCAmelCase:Tuple									=  output.audios.shape
											assert audio_shape == (1, 256)

											_lowerCAmelCase:Any									=  audioldm_pipe.vocoder.config
											config.model_in_dim *= 2
											_lowerCAmelCase:Optional[Any]									=  SpeechTaHifiGan(a__).to(a__)
											_lowerCAmelCase:int									=  audioldm_pipe(a__     ,num_inference_steps=1)
											_lowerCAmelCase:List[str]									=  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       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a__)





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




											"""simple docstring"""







											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       __UpperCamelCase     (							self      :	Any)						->       Tuple:




											"""simple docstring"""







											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a__)




@slow
class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Tuple)						->       Optional[Any]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Optional[Any]     ,a__      :	int="cpu"     ,a__      :	str=torch.floataa     ,a__      :	Union[str, Any]=0)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  torch.Generator(device=a__).manual_seed(a__)
											_lowerCAmelCase:Tuple									=  np.random.RandomState(a__).standard_normal((1, 8, 128, 16))
											_lowerCAmelCase:List[str]									=  torch.from_numpy(a__).to(device=a__     ,dtype=a__)
											_lowerCAmelCase:Dict									=  {
											    '''prompt''': '''A hammer hitting a wooden surface''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 2.5,
											}
											return inputs





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm''')
											_lowerCAmelCase:Optional[Any]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Optional[int]									=  self.get_inputs(a__)
											_lowerCAmelCase:Optional[Any]									=  25
											_lowerCAmelCase:int									=  audioldm_pipe(**a__).audios[0]

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

											_lowerCAmelCase:int									=  audio[7_7230:7_7240]
											_lowerCAmelCase:Optional[Any]									=  np.array(
											    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315])
											_lowerCAmelCase:Dict									=  np.abs(expected_slice - audio_slice).max()
											assert max_diff < 1E-2







						def       __UpperCamelCase     (							self      :	List[str])						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm''')
											_lowerCAmelCase:List[Any]									=  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
											_lowerCAmelCase:Union[str, Any]									=  audioldm_pipe.to(a__)
											audioldm_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Optional[Any]									=  self.get_inputs(a__)
											_lowerCAmelCase:Union[str, Any]									=  audioldm_pipe(**a__).audios[0]

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

											_lowerCAmelCase:Tuple									=  audio[2_7780:2_7790]
											_lowerCAmelCase:Optional[Any]									=  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
											_lowerCAmelCase:Tuple									=  np.abs(expected_slice - audio_slice).max()
											assert max_diff < 3E-2






