






"""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()
_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {
    '''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					      =  [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(__lowerCAmelCase				,	__lowerCAmelCase							)

      if weight_type is not None:
            UpperCAmelCase          =	getattr(__lowerCAmelCase				,	__lowerCAmelCase							).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 _a			(		_snake_case				,	_snake_case							):







      """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(
                      __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	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(__lowerCAmelCase							)[0].split("""."""							)[-2]
                                    UpperCAmelCase          =	mapped_key.replace("""*"""				,	__lowerCAmelCase							)
                              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(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase							)
                        continue
            if not is_used:
                  unused_weights.append(__lowerCAmelCase							)

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """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(__lowerCAmelCase							)

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case=None							):







      """simple docstring"""






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

      if config_path is not None:
            UpperCAmelCase          =	WavLMConfig.from_pretrained(__lowerCAmelCase							)
      else:
            UpperCAmelCase          =	WavLMConfig()

      UpperCAmelCase          =	WavLMModel(__lowerCAmelCase							)

      recursively_load_weights(__lowerCAmelCase				,	__lowerCAmelCase							)

      hf_wavlm.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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					      =  parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)











"""simple docstring"""







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



