
'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE   :		str					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		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_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",
    "mask_emb": "masked_spec_embed",
}



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):







				for attribute in key.split('''.'''     ):
								A_       	=  getattr(_A ,						_A     )

				if weight_type is not None:
								A_       	=  getattr(_A ,						_A     ).shape
				else:
								A_       	=  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":
								A_       	=  value
				elif weight_type == "weight_g":
								A_       	=  value
				elif weight_type == "weight_v":
								A_       	=  value
				elif weight_type == "bias":
								A_       	=  value
				else:
								A_       	=  value

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  []
				A_       	=  fairseq_model.state_dict()

				A_       	=  hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								A_       	=  False
								if "conv_layers" in name:
												load_conv_layer(
												    _A ,						_A ,						_A ,						_A ,						hf_model.config.feat_extract_norm == '''group''' ,						)
												A_       	=  True
								else:
												for key, mapped_key in MAPPING.items():
																A_       	=  mapped_key

																if key in name:
																				A_       	=  True
																				if "*" in mapped_key:
																								A_       	=  name.split(_A     )[0].split('''.'''     )[-2]
																								A_       	=  mapped_key.replace('''*''' ,						_A     )
																				if "weight_g" in name:
																								A_       	=  '''weight_g'''
																				elif "weight_v" in name:
																								A_       	=  '''weight_v'''
																				elif "weight" in name:
																								A_       	=  '''weight'''
																				elif "bias" in name:
																								A_       	=  '''bias'''
																				else:
																								A_       	=  None
																				set_recursively(_A ,						_A ,						_A ,						_A ,						_A     )
																continue
								if not is_used:
												unused_weights.append(_A     )

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  full_name.split('''conv_layers.'''     )[-1]
				A_       	=  name.split('''.'''     )
				A_       	=  int(items[0]     )
				A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  value
												logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
				else:
								unused_weights.append(_A     )



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  HubertConfig()
				A_       	=  model.config

				A_       	=  fs_config.activation_dropout
				A_       	=  False
				A_       	=  fs_config.attention_dropout
				A_       	=  False
				A_       	=  eval(fs_config.extractor_conv_feature_layers     )
				A_       	=  [x[0] for x in conv_layers]
				A_       	=  [x[1] for x in conv_layers]
				A_       	=  [x[2] for x in conv_layers]
				A_       	=  '''gelu'''
				A_       	=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				A_       	=  False
				A_       	=  0.0
				A_       	=  0.0
				A_       	=  fs_config.activation_fn
				A_       	=  fs_config.dropout
				A_       	=  fs_config.encoder_embed_dim
				A_       	=  0.02
				A_       	=  fs_config.encoder_ffn_embed_dim
				A_       	=  1e-5
				A_       	=  0.0
				A_       	=  fs_config.encoder_attention_heads
				A_       	=  fs_config.conv_pos_groups
				A_       	=  fs_config.conv_pos
				A_       	=  len(_A     )
				A_       	=  fs_config.encoder_layers

				return config



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase=None     ):
				A_       	=  distilhubert().model.model

				if config_path is not None:
								A_       	=  HubertConfig.from_pretrained(_A     )
				else:
								A_       	=  convert_config(_A     )
				A_       	=  model.eval()

				A_       	=  WavaVecaFeatureExtractor(
				    feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_A ,						return_attention_mask=_A ,						)
				A_       	=  HubertModel(_A     )

				recursively_load_weights(_A ,						_A     )

				feature_extractor.save_pretrained(_A     )
				hf_model.save_pretrained(_A     )


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

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 					__lowercase		(					A							,       A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	StableDiffusionModelEditingPipeline
					__magic_name__						:							List[str]     	=	TEXT_TO_IMAGE_PARAMS
					__magic_name__						:							Any     	=	TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__						:							Optional[Any]     	=	TEXT_TO_IMAGE_IMAGE_PARAMS
					__magic_name__						:							Tuple     	=	TEXT_TO_IMAGE_IMAGE_PARAMS


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





									'''simple docstring'''
									torch.manual_seed(0						)
									A_       	=  UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)       ,       layers_per_block=2       ,       sample_size=3_2       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,       cross_attention_dim=3_2       ,       )
									A_       	=  DDIMScheduler()
									torch.manual_seed(0						)
									A_       	=  AutoencoderKL(
									    block_out_channels=[3_2, 6_4]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,       latent_channels=4       ,       )
									torch.manual_seed(0						)
									A_       	=  CLIPTextConfig(
									    bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=3_2       ,       intermediate_size=3_7       ,       layer_norm_eps=1E-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_0_0_0       ,       )
									A_       	=  CLIPTextModel(a__						)
									A_       	=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

									A_       	=  {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=0						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    '''height''': None,
									    '''width''': None,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs


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





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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


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





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  '''french fries'''
									A_       	=  sd_pipe(**a__       ,       negative_prompt=a__						)
									A_       	=  output.images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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


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





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''						)
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									A_       	=  sd_pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									A_       	=  np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_       	=  self.get_dummy_components()
									A_       	=  PNDMScheduler()
									A_       	=  StableDiffusionModelEditingPipeline(**a__						)
									A_       	=  sd_pipe.to(a__						)
									sd_pipe.set_progress_bar_config(disable=a__						)

									A_       	=  self.get_dummy_inputs(a__						)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(a__						):
													A_       	=  sd_pipe(**a__						).images


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





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=5E-3						)


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





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)






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


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





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def        lowerCAmelCase_    (	self       ,       a__=0						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  torch.manual_seed(a__						)
									A_       	=  {
									    '''prompt''': '''A field of roses''',
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs


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





									'''simple docstring'''
									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(a__       ,       safety_checker=a__						)
									pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									A_       	=  np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

									# make sure image changes after editing
									pipe.edit_model('''A pack of roses'''       ,       '''A pack of blue roses'''						)

									A_       	=  pipe(**a__						).images
									A_       	=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice						).max() > 1E-1


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





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A_       	=  '''CompVis/stable-diffusion-v1-4'''
									A_       	=  DDIMScheduler.from_pretrained(a__       ,       subfolder='''scheduler'''						)
									A_       	=  StableDiffusionModelEditingPipeline.from_pretrained(
									    a__       ,       scheduler=a__       ,       safety_checker=a__						)
									A_       	=  pipe.to(a__						)
									pipe.set_progress_bar_config(disable=a__						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									A_       	=  self.get_inputs()
									A_       	=  pipe(**a__						)

									A_       	=  torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9