


'''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()
lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "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",
}
lowercase__				     =		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






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







     '''simple docstring'''






     for attribute in key.split("."				):
          _a						=		getattr(__lowerCamelCase      ,  __lowerCamelCase				)

     if weight_type is not None:
          _a						=		getattr(__lowerCamelCase      ,  __lowerCamelCase				).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 __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[Any]				)      ->     Optional[int]:







     '''simple docstring'''






     _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(
                   __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  hf_model.config.feat_extract_norm == "group"      ,  )
               _a						=		True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
                         _a						=		True
                         if "*" in mapped_key:
                              _a						=		name.split(__lowerCamelCase				)[0].split("."				)[-2]
                              _a						=		mapped_key.replace("*"      ,  __lowerCamelCase				)
                         if "weight_g" in name:
                              _a						=		"weight_g"
                         elif "weight_v" in name:
                              _a						=		"weight_v"
                         elif "bias" in name and "relative_attention_bias" not in name:
                              _a						=		"bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a						=		"weight"
                         else:
                              _a						=		None

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

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






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







     '''simple docstring'''






     _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(__lowerCamelCase				)






@torch.no_grad()
def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: List[str]=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		torch.load(__lowerCamelCase				)
     _a						=		WavLMConfigOrig(checkpoint["cfg"]				)
     _a						=		WavLMOrig(__lowerCamelCase				)
     model.load_state_dict(checkpoint["model"]				)
     model.eval()

     if config_path is not None:
          _a						=		WavLMConfig.from_pretrained(__lowerCamelCase				)
     else:
          _a						=		WavLMConfig()

     _a						=		WavLMModel(__lowerCamelCase				)

     recursively_load_weights(__lowerCamelCase      ,  __lowerCamelCase				)

     hf_wavlm.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
   lowercase__				     =		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")
   lowercase__				     =		parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, 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 VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







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






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							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  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

