
"""simple docstring"""




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a		:					Any    	=					logging.get_logger(__name__)

a		:					List[str]    	=					{
    '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 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		Optional[Any]       ,		UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		Tuple    )    ->	Tuple:
    for attribute in key.split('.'    ):
        a__    = getattr(UpperCamelCase       ,		UpperCamelCase    )

    if weight_type is not None:
        a__    = getattr(UpperCamelCase       ,		UpperCamelCase    ).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 						__magic_name__		(				UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		str    )    ->	List[str]:
    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(
                UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase       ,		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(UpperCamelCase    )[0].split('.'    )[-2]
                        a__    = mapped_key.replace('*'       ,		UpperCamelCase    )
                    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(UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase       ,		UpperCamelCase    )
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase    )

    logger.warning(f'Unused weights: {unused_weights}'    )



def 						__magic_name__		(				UpperCamelCase    :		Tuple       ,		UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		Optional[Any]       ,		UpperCamelCase    :		str       ,		UpperCamelCase    :		int    )    ->	List[Any]:
    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(UpperCamelCase    )



def 						__magic_name__		(				UpperCamelCase    :		Optional[int]    )    ->	Union[str, Any]:
    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(UpperCamelCase    )
    a__    = fs_config.encoder_layers

    return config



@torch.no_grad()
def 						__magic_name__		(				UpperCamelCase    :		Any       ,		UpperCamelCase    :		Union[str, Any]=None    )    ->	str:
    a__    = distilhubert().model.model

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

    a__    = WavaVecaFeatureExtractor(
        feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=UpperCamelCase       ,		return_attention_mask=UpperCamelCase       ,		)
    a__    = HubertModel(UpperCamelCase    )

    recursively_load_weights(UpperCamelCase       ,		UpperCamelCase    )

    feature_extractor.save_pretrained(UpperCamelCase    )
    hf_model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
    a		:					Tuple    	=					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')
    a		:					Union[str, Any]    	=					parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a		:					str    	=					False







@nightly
@require_torch_gpu
class 						lowercase(unittest.TestCase					):



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



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



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







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE      )
             a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      )

         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = generator.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"




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







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = 'cyberpunk 2077'
         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=5_0    ,							output_type='numpy'    ,							).images

         a__    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__    = np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]      )

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


