
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()
UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     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',
}
UpperCAmelCase__		:     str     			=      [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase__							(					a		,		a		,		a		,		a		,		a      )      ->   Any:
   for attribute in key.split('''.'''      ):
      _A:  Dict					=       getattr(a		,		a      )

   if weight_type is not None:
      _A:  List[Any]					=       getattr(a		,		a      ).shape
   else:
      _A:  int					=       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:  int					=       value
   elif weight_type == "weight_g":
      _A:  Tuple					=       value
   elif weight_type == "weight_v":
      _A:  List[str]					=       value
   elif weight_type == "bias":
      _A:  Optional[int]					=       value
   else:
      _A:  Dict					=       value

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






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   _A:  str					=       []
   _A:  int					=       fairseq_model.state_dict()

   _A:  str					=       hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _A:  List[Any]					=       False
      if "conv_layers" in name:
         load_conv_layer(
             a		,		a		,		a		,		a		,		hf_model.config.feat_extract_norm == '''group'''		,		)
         _A:  Optional[int]					=       True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
               _A:  str					=       True
               if "*" in mapped_key:
                  _A:  str					=       name.split(a      )[0].split('''.'''      )[-2]
                  _A:  List[Any]					=       mapped_key.replace('''*'''		,		a      )
               if "weight_g" in name:
                  _A:  str					=       '''weight_g'''
               elif "weight_v" in name:
                  _A:  Optional[int]					=       '''weight_v'''
               elif "bias" in name and "relative_attention_bias" not in name:
                  _A:  Tuple					=       '''bias'''
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  _A:  Union[str, Any]					=       '''weight'''
               else:
                  _A:  Union[str, Any]					=       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__							(					a		,		a		,		a		,		a		,		a      )      ->   Dict:
   _A:  int					=       full_name.split('''conv_layers.'''      )[-1]
   _A:  Tuple					=       name.split('''.'''      )
   _A:  Union[str, Any]					=       int(items[0]      )
   _A:  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."""
         )
         _A:  Union[str, 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."""
         )
         _A:  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."
         )
         _A:  List[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."""
         )
         _A:  str					=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
   else:
      unused_weights.append(a      )






@torch.no_grad()
def   lowerCamelCase__							(					a		,		a		,		a=None      )      ->   int:
   # load the pre-trained checkpoints
   _A:  List[str]					=       torch.load(a      )
   _A:  List[str]					=       WavLMConfigOrig(checkpoint['''cfg''']      )
   _A:  Union[str, Any]					=       WavLMOrig(a      )
   model.load_state_dict(checkpoint['''model''']      )
   model.eval()

   if config_path is not None:
      _A:  List[Any]					=       WavLMConfig.from_pretrained(a      )
   else:
      _A:  Union[str, Any]					=       WavLMConfig()

   _A:  List[Any]					=       WavLMModel(a      )

   recursively_load_weights(a		,		a      )

   hf_wavlm.save_pretrained(a      )


if __name__ == "__main__":
   UpperCAmelCase__		:     Optional[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')
   UpperCAmelCase__		:     Optional[Any]     			=      parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return F"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_     ) for s in shape]     )}.npy"""


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Tuple=(4, 4, 6_4, 6_4)			,			lowerCAmelCase_     :							List[str]=False     ):

      """simple docstring"""
      _A:  List[str]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Optional[int]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return image


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Optional[Any]="CompVis/stable-diffusion-v1-4"     ):

      """simple docstring"""
      _A:  Tuple					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  str					=       '''bf16''' if fpaa else None

      _A			,  _A:  Union[str, Any]					=       FlaxUNetaDConditionModel.from_pretrained(
          lowerCAmelCase_			,			subfolder='''unet'''			,			dtype=lowerCAmelCase_			,			revision=lowerCAmelCase_     )
      return model, params


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							str=(4, 7_7, 7_6_8)			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Optional[int]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Union[str, Any]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return hidden_states


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			,			fpaa=lowerCAmelCase_     )
      _A:  List[str]					=       self.get_latents(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )
      _A:  Optional[int]					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )

      _A:  List[str]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  Any					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  Tuple					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_latents(lowerCAmelCase_			,			shape=(4, 4, 9_6, 9_6)			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			shape=(4, 7_7, 1_0_2_4)			,			fpaa=lowerCAmelCase_     )

      _A:  Optional[int]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  List[str]					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  List[str]					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )



