



"""simple docstring"""
import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A:   Optional[int]								=		logging.get_logger(__name__)

A:   Tuple								=		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
A:   List[str]								=		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Optional[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						)

 if weight_type is not None:
  UpperCAmelCase	:					List[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						).shape
 else:
  UpperCAmelCase	:					str	=	hf_pointer.shape

 assert hf_shape == value.shape, (
     F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
     F" {value.shape} for {full_name}"
 )

 if weight_type == "weight":
  UpperCAmelCase	:					Optional[Any]	=	value
 elif weight_type == "weight_g":
  UpperCAmelCase	:					str	=	value
 elif weight_type == "weight_v":
  UpperCAmelCase	:					Union[str, Any]	=	value
 elif weight_type == "bias":
  UpperCAmelCase	:					str	=	value
 else:
  UpperCAmelCase	:					Union[str, Any]	=	value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	:					Any	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Tuple	=	hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					str	=	False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		hf_model.config.feat_extract_norm == """group"""			,		)
   UpperCAmelCase	:					Union[str, Any]	=	True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
     UpperCAmelCase	:					Dict	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					str	=	name.split(UpperCamelCase						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					Tuple	=	mapped_key.replace("""*"""			,		UpperCamelCase						)
     if "weight_g" in name:
      UpperCAmelCase	:					Any	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					Optional[Any]	=	"""weight_v"""
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCAmelCase	:					Union[str, Any]	=	"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCAmelCase	:					str	=	"""weight"""
     else:
      UpperCAmelCase	:					Optional[Any]	=	None

     set_recursively(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
    continue
  if not is_used:
   unused_weights.append(UpperCamelCase						)

 logger.warning(F"Unused weights: {unused_weights}"						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					str	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					Dict	=	name.split("""."""						)
 UpperCAmelCase	:					List[str]	=	int(items[0]						)
 UpperCAmelCase	:					Any	=	int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F"{full_name} has size {value.shape}, but"
       F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
   )
   UpperCAmelCase	:					Optional[Any]	=	value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F"{full_name} has size {value.shape}, but"
       F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
   )
   UpperCAmelCase	:					Tuple	=	value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
       " found."
   )
   UpperCAmelCase	:					str	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F"{full_name} has size {value.shape}, but"
       F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
   )
   UpperCAmelCase	:					Optional[Any]	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(UpperCamelCase						)

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]=None						):
 # load the pre-trained checkpoints
 UpperCAmelCase	:					List[Any]	=	torch.load(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	WavLMConfigOrig(checkpoint["""cfg"""]						)
 UpperCAmelCase	:					Optional[int]	=	WavLMOrig(UpperCamelCase						)
 model.load_state_dict(checkpoint["""model"""]						)
 model.eval()

 if config_path is not None:
  UpperCAmelCase	:					List[str]	=	WavLMConfig.from_pretrained(UpperCamelCase						)
 else:
  UpperCAmelCase	:					List[Any]	=	WavLMConfig()

 UpperCAmelCase	:					Any	=	WavLMModel(UpperCamelCase						)

 recursively_load_weights(UpperCamelCase			,		UpperCamelCase						)

 hf_wavlm.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   int								=		argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      A:   Tuple								=		parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 					_SCREAMING_SNAKE_CASE(   A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  ConsistencyModelPipeline
       SCREAMING_SNAKE_CASE_   :      Any							=  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE_   :      Dict							=  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

       # Override required_optional_params to remove num_images_per_prompt
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  frozenset(
           [
               '''num_inference_steps''',
               '''generator''',
               '''latents''',
               '''output_type''',
               '''return_dict''',
               '''callback''',
               '''callback_steps''',
           ]  )




       @property
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  UNetaDModel.from_pretrained(
                 '''diffusers/consistency-models-test'''		,subfolder='''test_unet'''		,)
             return unet




       @property
       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  UNetaDModel.from_pretrained(
                 '''diffusers/consistency-models-test'''		,subfolder='''test_unet_class_cond'''		,)
             return unet




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=False							)			->      Union[str, Any]:





             """simple docstring"""






             if class_cond:
                   __SCREAMING_SNAKE_CASE   :str						=  self.dummy_cond_unet
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.dummy_uncond_unet

             # Default to CM multistep sampler
             __SCREAMING_SNAKE_CASE   :List[str]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)

             __SCREAMING_SNAKE_CASE   :List[str]						=  {
                 '''unet''': unet,
                 '''scheduler''': scheduler,
             }

             return components




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)			->      Dict:





             """simple docstring"""






             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Dict						=  {
                 '''batch_size''': 1,
                 '''num_inference_steps''': None,
                 '''timesteps''': [22, 0],
                 '''generator''': generator,
                 '''output_type''': '''np''',
             }

             return inputs




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Any						=  np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  0
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :Dict						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :Tuple						=  self.get_dummy_components()
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  1
             __SCREAMING_SNAKE_CASE   :List[str]						=  None
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :int						=  np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
             __SCREAMING_SNAKE_CASE   :Any						=  self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  pipe.to(SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  None
             __SCREAMING_SNAKE_CASE   :List[Any]						=  0
             __SCREAMING_SNAKE_CASE   :Any						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 32, 32, 3)

             __SCREAMING_SNAKE_CASE   :int						=  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3






@slow
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




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





             """simple docstring"""






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




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__="cpu"		,SCREAMING_SNAKE_CASE__=torch.floataa		,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  torch.manual_seed(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  {
                 '''num_inference_steps''': None,
                 '''timesteps''': [22, 0],
                 '''class_labels''': 0,
                 '''generator''': generator,
                 '''output_type''': '''np''',
             }

             if get_fixed_latents:
                   __SCREAMING_SNAKE_CASE   :int						=  self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__		,shape=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :int						=  latents

             return inputs




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__="cpu"		,SCREAMING_SNAKE_CASE__=torch.floataa		,SCREAMING_SNAKE_CASE__=(1, 3, 64, 64)							)			->      int:





             """simple docstring"""






             if type(SCREAMING_SNAKE_CASE__							) == str:
                   __SCREAMING_SNAKE_CASE   :int						=  torch.device(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  randn_tensor(SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__							)
             return latents




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Dict						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :int						=  self.get_inputs()
             __SCREAMING_SNAKE_CASE   :List[str]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :Dict						=  np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.get_inputs()
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :List[str]						=  np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2




       @require_torch_a
       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[Any]						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :Any						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :Any						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__		,torch_dtype=torch.floataa							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__							)
             # Ensure usage of flash attention in torch 2.0
             with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__		,enable_math=SCREAMING_SNAKE_CASE__		,enable_mem_efficient=SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :List[str]						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :List[Any]						=  np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




       @require_torch_a
       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  UNetaDModel.from_pretrained('''diffusers/consistency_models'''		,subfolder='''diffusers_cd_imagenet64_l2'''							)
             __SCREAMING_SNAKE_CASE   :Dict						=  CMStochasticIterativeScheduler(
                 num_train_timesteps=40		,sigma_min=0.0_0_2		,sigma_max=8_0.0		,)
             __SCREAMING_SNAKE_CASE   :int						=  ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             pipe.to(torch_device=SCREAMING_SNAKE_CASE__		,torch_dtype=torch.floataa							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Dict						=  self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__		,device=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  1
             __SCREAMING_SNAKE_CASE   :int						=  None
             # Ensure usage of flash attention in torch 2.0
             with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__		,enable_math=SCREAMING_SNAKE_CASE__		,enable_mem_efficient=SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :str						=  pipe(**SCREAMING_SNAKE_CASE__							).images
             assert image.shape == (1, 64, 64, 3)

             __SCREAMING_SNAKE_CASE   :str						=  image[0, -3:, -3:, -1]

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3