



'''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()
_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Union[str, Any]      	=	{
    "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",
}
_lowerCamelCase	:       Union[str, Any]      	=	[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   for attribute in key.split('.'    ):
      UpperCamelCase					=  getattr(__UpperCamelCase    ,						__UpperCamelCase    )

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

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  []
   UpperCamelCase					=  fairseq_model.state_dict()

   UpperCamelCase					=  hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCamelCase					=  False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase    ,						hf_model.config.feat_extract_norm == 'group'    ,						)
         UpperCamelCase					=  True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split('w2v_model.'    )[-1] == name.split('.'    )[0]:
               UpperCamelCase					=  True
               if "*" in mapped_key:
                  UpperCamelCase					=  name.split(__UpperCamelCase    )[0].split('.'    )[-2]
                  UpperCamelCase					=  mapped_key.replace('*'    ,						__UpperCamelCase    )
               if "weight_g" in name:
                  UpperCamelCase					=  'weight_g'
               elif "weight_v" in name:
                  UpperCamelCase					=  'weight_v'
               elif "bias" in name and "relative_attention_bias" not in name:
                  UpperCamelCase					=  'bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCamelCase					=  'weight'
               else:
                  UpperCamelCase					=  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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  full_name.split('conv_layers.'    )[-1]
   UpperCamelCase					=  name.split('.'    )
   UpperCamelCase					=  int(items[0]    )
   UpperCamelCase					=  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					=  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					=  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					=  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					=  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 				__lowerCamelCase				(   A__    ,						A__    ,						A__=None    )       ->	Optional[Any]:






   """simple docstring"""
   # load the pre-trained checkpoints
   UpperCamelCase					=  torch.load(__UpperCamelCase    )
   UpperCamelCase					=  WavLMConfigOrig(checkpoint['cfg']    )
   UpperCamelCase					=  WavLMOrig(__UpperCamelCase    )
   model.load_state_dict(checkpoint['model']    )
   model.eval()

   if config_path is not None:
      UpperCamelCase					=  WavLMConfig.from_pretrained(__UpperCamelCase    )
   else:
      UpperCamelCase					=  WavLMConfig()

   UpperCamelCase					=  WavLMModel(__UpperCamelCase    )

   recursively_load_weights(__UpperCamelCase    ,						__UpperCamelCase    )

   hf_wavlm.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
   _lowerCamelCase	:       Union[str, Any]      	=	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")
   _lowerCamelCase	:       List[str]      	=	parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        SCREAMING_SNAKE_CASE_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        assert (
            np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







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







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


