



'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Optional[Any]:
 # initialize config
 if "resnet-50" in model_name:
  _snake_case    :  List[Any]					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""				)
 elif "resnet-101" in model_name:
  _snake_case    :  Any					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""				)
 else:
  raise ValueError("""Model name should include either resnet50 or resnet101"""				)

 _snake_case    :  Union[str, Any]					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,       backbone_config=SCREAMING_SNAKE_CASE__				)

 # set label attributes
 _snake_case    :  List[str]					=					"""panoptic""" in model_name
 if is_panoptic:
  _snake_case    :  Optional[int]					=					250
 else:
  _snake_case    :  Optional[int]					=					91
  _snake_case    :  Optional[Any]					=					"""huggingface/label-files"""
  _snake_case    :  Optional[int]					=					"""coco-detection-id2label.json"""
  _snake_case    :  str					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       repo_type="""dataset"""				)				,       """r"""				)				)
  _snake_case    :  Dict					=					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
  _snake_case    :  List[str]					=					idalabel
  _snake_case    :  str					=					{v: k for k, v in idalabel.items()}

 return config, is_panoptic



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[int]:
 # here we list all keys to be renamed (original name on the left, our name on the right)
 _snake_case    :  Optional[Any]					=					[]

 # stem
 # fmt: off
 rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")				)
 rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")				)
 rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")				)
 rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")				)
 rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")				)
 # stages
 for stage_idx in range(len(config.backbone_config.depths				)				):
  for layer_idx in range(config.backbone_config.depths[stage_idx]				):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
        )				)
   # 3 convs
   for i in range(3				):
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
        )				)
    rename_keys.append(
        (
            F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
            F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
        )				)
    # fmt: on

 for i in range(config.encoder_layers				):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (
          F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
          F'''encoder.layers.{i}.self_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')				)
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
          F'''decoder.layers.{i}.self_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')				)
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')				)

 # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
 rename_keys.extend(
     [
         ("""input_proj.weight""", """input_projection.weight"""),
         ("""input_proj.bias""", """input_projection.bias"""),
         ("""query_embed.weight""", """query_position_embeddings.weight"""),
         ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
         ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
         ("""class_embed.weight""", """class_labels_classifier.weight"""),
         ("""class_embed.bias""", """class_labels_classifier.bias"""),
         ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
         ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
         ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
         ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
         ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
         ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
     ]				)

 return rename_keys



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Optional[Any]:
 _snake_case    :  str					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					val



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     Union[str, Any]:
 _snake_case    :  Optional[Any]					=					""""""
 if is_panoptic:
  _snake_case    :  Optional[Any]					=					"""detr."""

 # first: transformer encoder
 for i in range(6				):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  _snake_case    :  Any					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  List[str]					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[Any]					=					in_proj_weight[:256, :]
  _snake_case    :  Any					=					in_proj_bias[:256]
  _snake_case    :  List[Any]					=					in_proj_weight[256:512, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias[-256:]
 # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
 for i in range(6				):
  # read in weights + bias of input projection layer of self-attention
  _snake_case    :  Tuple					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  Dict					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[int]					=					in_proj_weight[:256, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[:256]
  _snake_case    :  Any					=					in_proj_weight[256:512, :]
  _snake_case    :  int					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Tuple					=					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  _snake_case    :  List[str]					=					state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
  _snake_case    :  Optional[Any]					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  _snake_case    :  int					=					in_proj_weight_cross_attn[:256, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[:256]
  _snake_case    :  int					=					in_proj_weight_cross_attn[256:512, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[256:512]
  _snake_case    :  Dict					=					in_proj_weight_cross_attn[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias_cross_attn[-256:]



def       lowercase       (   )  ->     Optional[Any]:
 _snake_case    :  Dict					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)

 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				)  ->     List[Any]:
 _snake_case    :  Union[str, Any]					=					get_detr_config(SCREAMING_SNAKE_CASE__				)

 # load original model from torch hub
 _snake_case    :  Dict					=					{
     """detr-resnet-50""": """detr_resnet50""",
     """detr-resnet-101""": """detr_resnet101""",
 }
 logger.info(F'''Converting model {model_name}...'''				)
 _snake_case    :  List[Any]					=					torch.hub.load("""facebookresearch/detr"""				,       model_name_to_original_name[model_name]				,       pretrained=SCREAMING_SNAKE_CASE__				).eval()
 _snake_case    :  Optional[int]					=					detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__				):
  if is_panoptic:
   _snake_case    :  int					=					"""detr.""" + src
  rename_key(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 # query, key and value matrices need special treatment
 read_in_q_k_v(SCREAMING_SNAKE_CASE__				,       is_panoptic=SCREAMING_SNAKE_CASE__				)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 _snake_case    :  List[Any]					=					"""detr.model.""" if is_panoptic else """model."""
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("""detr"""				)
       and not key.startswith("""class_labels_classifier"""				)
       and not key.startswith("""bbox_predictor"""				)
   ):
    _snake_case    :  List[str]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Optional[int]					=					val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    _snake_case    :  Tuple					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  List[str]					=					val
   elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
    continue
   else:
    _snake_case    :  Optional[int]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Dict					=					val
  else:
   if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
    _snake_case    :  Union[str, Any]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Tuple					=					val

    # finally, create HuggingFace model and load state dict
 _snake_case    :  int					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 # verify our conversion on an image
 _snake_case    :  List[Any]					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
 _snake_case    :  int					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  str					=					encoding["""pixel_values"""]

 _snake_case    :  Tuple					=					detr(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					model(SCREAMING_SNAKE_CASE__				)

 assert torch.allclose(outputs.logits				,       original_outputs["""pred_logits"""]				,       atol=1e-3				)
 assert torch.allclose(outputs.pred_boxes				,       original_outputs["""pred_boxes"""]				,       atol=1e-3				)
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks				,       original_outputs["""pred_masks"""]				,       atol=1e-4				)
 print("""Looks ok!"""				)

 if pytorch_dump_folder_path is not None:
  # Save model and image processor
  logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
  Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
  model.save_pretrained(SCREAMING_SNAKE_CASE__				)
  processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 if push_to_hub:
  # Upload model and image processor to the hub
  logger.info("""Uploading PyTorch model and image processor to the hub..."""				)
  model.push_to_hub(F'''nielsr/{model_name}'''				)
  processor.push_to_hub(F'''nielsr/{model_name}'''				)


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

  parser.add_argument(
      """--model_name""",
      default="""detr-resnet-50""",
      type=str,
      choices=["""detr-resnet-50""", """detr-resnet-101"""],
      help="""Name of the DETR model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
  a__    =	parser.parse_args()
  convert_detr_checkpoint(args.model_name, 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 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: str          =		AudioLDMPipeline
   snake_case_					: Any          =		TEXT_TO_AUDIO_PARAMS
   snake_case_					: Tuple          =		TEXT_TO_AUDIO_BATCH_PARAMS
   snake_case_					: Dict          =		frozenset(
       [
           """num_inference_steps""",
           """num_waveforms_per_prompt""",
           """generator""",
           """latents""",
           """output_type""",
           """return_dict""",
           """callback""",
           """callback_steps""",
       ]  )

   def      UpperCamelCase_  (					self							:			Any)				->				Any:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  List[str]					=					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=lowerCAmelCase				,		)
    _snake_case    :  List[Any]					=					DDIMScheduler(
        beta_start=0.00_085				,		beta_end=0.012				,		beta_schedule="""scaled_linear"""				,		clip_sample=lowerCAmelCase				,		set_alpha_to_one=lowerCAmelCase				,		)
    torch.manual_seed(0)
    _snake_case    :  str					=					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    :  Any					=					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				,		)
    _snake_case    :  Any					=					ClapTextModelWithProjection(lowerCAmelCase)
    _snake_case    :  List[str]					=					RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""				,		model_max_length=77)

    _snake_case    :  List[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=lowerCAmelCase				,		)

    _snake_case    :  Tuple					=					SpeechTaHifiGan(lowerCAmelCase)

    _snake_case    :  str					=					{
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """vocoder""": vocoder,
    }
    return components

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Dict=0)				->				int:

    """simple docstring"""







    if str(lowerCAmelCase).startswith("""mps"""):
     _snake_case    :  List[str]					=					torch.manual_seed(lowerCAmelCase)
    else:
     _snake_case    :  Optional[Any]					=					torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)
    _snake_case    :  Tuple					=					{
        """prompt""": """A hammer hitting a wooden surface""",
        """generator""": generator,
        """num_inference_steps""": 2,
        """guidance_scale""": 6.0,
    }
    return inputs

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

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator

    _snake_case    :  Dict					=					self.get_dummy_components()
    _snake_case    :  Optional[Any]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Tuple					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  int					=					output.audios[0]

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

    _snake_case    :  Union[str, Any]					=					audio[:10]
    _snake_case    :  Union[str, Any]					=					np.array(
        [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033])

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

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

    """simple docstring"""







    _snake_case    :  Dict					=					self.get_dummy_components()
    _snake_case    :  Any					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  int					=					audioldm_pipe.to(lowerCAmelCase)
    _snake_case    :  str					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[Any]					=					3 * [inputs["""prompt"""]]

    # forward
    _snake_case    :  str					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  Dict					=					output.audios[0]

    _snake_case    :  Dict					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[str]					=					3 * [inputs.pop("""prompt""")]

    _snake_case    :  List[Any]					=					audioldm_pipe.tokenizer(
        lowerCAmelCase				,		padding="""max_length"""				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=lowerCAmelCase				,		return_tensors="""pt"""				,		)
    _snake_case    :  List[Any]					=					text_inputs["""input_ids"""].to(lowerCAmelCase)

    _snake_case    :  str					=					audioldm_pipe.text_encoder(
        lowerCAmelCase				,		)
    _snake_case    :  Optional[int]					=					prompt_embeds.text_embeds
    # additional L_2 normalization over each hidden-state
    _snake_case    :  Tuple					=					F.normalize(lowerCAmelCase				,		dim=-1)

    _snake_case    :  List[Any]					=					prompt_embeds

    # forward
    _snake_case    :  Union[str, Any]					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  str					=					output.audios[0]

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

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.get_dummy_components()
    _snake_case    :  Optional[Any]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Tuple					=					audioldm_pipe.to(lowerCAmelCase)
    _snake_case    :  List[Any]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Optional[int]					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					3 * ["""this is a negative prompt"""]
    _snake_case    :  int					=					negative_prompt
    _snake_case    :  Dict					=					3 * [inputs["""prompt"""]]

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

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					3 * [inputs.pop("""prompt""")]

    _snake_case    :  Tuple					=					[]
    for p in [prompt, negative_prompt]:
     _snake_case    :  Optional[Any]					=					audioldm_pipe.tokenizer(
         lowerCAmelCase				,		padding="""max_length"""				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=lowerCAmelCase				,		return_tensors="""pt"""				,		)
     _snake_case    :  Any					=					text_inputs["""input_ids"""].to(lowerCAmelCase)

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

     embeds.append(lowerCAmelCase)

    _snake_case	,    _snake_case    :  int					=					embeds

    # forward
    _snake_case    :  List[str]					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					output.audios[0]

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

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				List[Any]:

    """simple docstring"""







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

    _snake_case    :  List[str]					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[Any]					=					"""egg cracking"""
    _snake_case    :  Optional[Any]					=					audioldm_pipe(**lowerCAmelCase				,		negative_prompt=lowerCAmelCase)
    _snake_case    :  List[str]					=					output.audios[0]

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

    _snake_case    :  int					=					audio[:10]
    _snake_case    :  Any					=					np.array(
        [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032])

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

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

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    _snake_case    :  Any					=					self.get_dummy_components()
    _snake_case    :  int					=					PNDMScheduler(skip_prk_steps=lowerCAmelCase)
    _snake_case    :  Tuple					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  List[str]					=					"""A hammer hitting a wooden surface"""

    # test num_waveforms_per_prompt=1 (default)
    _snake_case    :  List[Any]					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=2).audios

    assert audios.shape == (1, 256)

    # test num_waveforms_per_prompt=1 (default) for batch of prompts
    _snake_case    :  Optional[Any]					=					2
    _snake_case    :  Optional[int]					=					audioldm_pipe([prompt] * batch_size				,		num_inference_steps=2).audios

    assert audios.shape == (batch_size, 256)

    # test num_waveforms_per_prompt for single prompt
    _snake_case    :  List[Any]					=					2
    _snake_case    :  Tuple					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=2				,		num_waveforms_per_prompt=lowerCAmelCase).audios

    assert audios.shape == (num_waveforms_per_prompt, 256)

    # test num_waveforms_per_prompt for batch of prompts
    _snake_case    :  Dict					=					2
    _snake_case    :  int					=					audioldm_pipe(
        [prompt] * batch_size				,		num_inference_steps=2				,		num_waveforms_per_prompt=lowerCAmelCase).audios

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

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

    """simple docstring"""







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

    _snake_case    :  List[str]					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Optional[int]					=					audioldm_pipe(audio_length_in_s=0.016				,		**lowerCAmelCase)
    _snake_case    :  str					=					output.audios[0]

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

    _snake_case    :  int					=					audioldm_pipe(audio_length_in_s=0.032				,		**lowerCAmelCase)
    _snake_case    :  List[Any]					=					output.audios[0]

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

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

    """simple docstring"""







    _snake_case    :  str					=					self.get_dummy_components()
    _snake_case    :  List[str]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  str					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  List[str]					=					["""hey"""]

    _snake_case    :  int					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=1)
    _snake_case    :  Optional[int]					=					output.audios.shape
    assert audio_shape == (1, 256)

    _snake_case    :  Dict					=					audioldm_pipe.vocoder.config
    config.model_in_dim *= 2
    _snake_case    :  str					=					SpeechTaHifiGan(lowerCAmelCase).to(lowerCAmelCase)
    _snake_case    :  List[str]					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=1)
    _snake_case    :  Dict					=					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							:			Dict)				->				Dict:

    """simple docstring"""







    self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase)

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

    """simple docstring"""







    self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCAmelCase)


   @unittest.skipIf(
       torch_device != """cuda""" or not is_xformers_available()				,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,		)
   def      UpperCamelCase_  (					self							:			Dict)				->				Union[str, Any]:

    """simple docstring"""







    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase)







@slow
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Dict)				->				Union[str, Any]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[Any]="cpu"				,		lowerCAmelCase							:			Any=torch.floataa				,		lowerCAmelCase							:			Optional[Any]=0)				->				Tuple:

    """simple docstring"""







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

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

    """simple docstring"""







    _snake_case    :  Dict					=					AudioLDMPipeline.from_pretrained("""cvssp/audioldm""")
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Tuple					=					self.get_inputs(lowerCAmelCase)
    _snake_case    :  int					=					25
    _snake_case    :  Optional[Any]					=					audioldm_pipe(**lowerCAmelCase).audios[0]

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

    _snake_case    :  Any					=					audio[7_7230:7_7240]
    _snake_case    :  str					=					np.array(
        [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315])
    _snake_case    :  List[str]					=					np.abs(expected_slice - audio_slice).max()
    assert max_diff < 1E-2


   def      UpperCamelCase_  (					self							:			Dict)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					AudioLDMPipeline.from_pretrained("""cvssp/audioldm""")
    _snake_case    :  Any					=					LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Union[str, Any]					=					self.get_inputs(lowerCAmelCase)
    _snake_case    :  List[str]					=					audioldm_pipe(**lowerCAmelCase).audios[0]

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

    _snake_case    :  Optional[Any]					=					audio[2_7780:2_7790]
    _snake_case    :  int					=					np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212])
    _snake_case    :  List[Any]					=					np.abs(expected_slice - audio_slice).max()
    assert max_diff < 3E-2
