


'''simple docstring'''
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_ :     str								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Tuple								=      {
    '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',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_ :     Optional[int]								=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any			):
 for attribute in key.split('.'			):
  __A  :		Optional[int]       =			getattr(UpperCamelCase__,				UpperCamelCase__			)

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

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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[Any]			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			fairseq_model.state_dict()

 __A  :		Tuple       =			hf_model.feature_extractor
 __A  :		Dict       =			hf_model.adapter

 for name, value in fairseq_dict.items():
  __A  :		List[str]       =			False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				hf_model.config.feat_extract_norm == 'group',				)
   __A  :		str       =			True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']			):
   load_adapter(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
   __A  :		Optional[Any]       =			True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
     __A  :		Union[str, Any]       =			True
     if "*" in mapped_key:
      __A  :		Optional[int]       =			name.split(UpperCamelCase__			)[0].split('.'			)[-2]
      __A  :		List[Any]       =			mapped_key.replace('*',				UpperCamelCase__			)
     if "weight_g" in name:
      __A  :		Optional[Any]       =			'weight_g'
     elif "weight_v" in name:
      __A  :		List[str]       =			'weight_v'
     elif "bias" in name:
      __A  :		Optional[Any]       =			'bias'
     elif "weight" in name:
      __A  :		Tuple       =			'weight'
     else:
      __A  :		List[Any]       =			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 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Dict			):
 __A  :		Union[str, Any]       =			full_name.split('conv_layers.'			)[-1]
 __A  :		Tuple       =			name.split('.'			)
 __A  :		List[str]       =			int(items[0]			)
 __A  :		Optional[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  :		Tuple       =			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  :		Union[str, Any]       =			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  :		Tuple       =			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  :		List[Any]       =			value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
 else:
  unused_weights.append(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str			):
 __A  :		List[Any]       =			full_name.split('adaptor.'			)[-1]
 __A  :		Union[str, Any]       =			name.split('.'			)

 if items[1].isdigit():
  __A  :		Tuple       =			int(items[1]			)
 else:
  __A  :		Any       =			None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
    __A  :		List[str]       =			value
    logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""			)
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
    __A  :		str       =			value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
    __A  :		str       =			value
    logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""			)
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
    __A  :		Optional[Any]       =			value
    logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""			)
 elif isinstance(UpperCamelCase__,				UpperCamelCase__			):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
   __A  :		List[Any]       =			value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""			)
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
   __A  :		List[Any]       =			value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""			)
 else:
  unused_weights.append(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A     ,__A  :		Optional[Any]       =			emb.weight.shape
 __A  :		List[str]       =			nn.Linear(UpperCamelCase__,				UpperCamelCase__,				bias=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			emb.weight.data
 return lin_layer


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					str,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					List[str],				):
 __A  :		Dict       =			WavaVecaConfig.from_pretrained(
     UpperCamelCase__,				add_adapter=UpperCamelCase__,				adapter_stride=UpperCamelCase__,				adapter_kernel_size=UpperCamelCase__,				use_auth_token=UpperCamelCase__,				output_hidden_size=UpperCamelCase__,				)
 __A  :		Tuple       =			MBartConfig.from_pretrained(UpperCamelCase__			)

 # load model
 __A     ,__A     ,__A  :		Tuple       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path],				arg_overrides={
         'config_yaml': config_yaml_path,
         'data': '/'.join(dict_path.split('/'			)[:-1]			),
         'w2v_path': checkpoint_path,
         'load_pretrained_decoder_from': None,
     },				)
 __A  :		Optional[int]       =			model[0].eval()

 # load feature extractor
 __A  :		List[str]       =			WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__,				use_auth_token=UpperCamelCase__			)

 # set weights for wav2vec2 encoder
 __A  :		Dict       =			WavaVecaModel(UpperCamelCase__			)

 recursively_load_weights_wavaveca(model.encoder,				UpperCamelCase__			)

 # load decoder weights
 __A  :		Any       =			MBartForCausalLM(UpperCamelCase__			)
 __A     ,__A  :		Optional[Any]       =			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),				strict=UpperCamelCase__			)
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""			)
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""			)

 __A  :		int       =			SpeechEncoderDecoderModel(encoder=UpperCamelCase__,				decoder=UpperCamelCase__			)
 __A  :		Any       =			False

 __A  :		Dict       =			MBartaaTokenizer(UpperCamelCase__			)
 tokenizer.save_pretrained(UpperCamelCase__			)

 __A  :		List[str]       =			hf_wavavec.config.to_dict()
 __A  :		Optional[Any]       =			tokenizer.pad_token_id
 __A  :		Any       =			tokenizer.bos_token_id
 __A  :		int       =			tokenizer.eos_token_id
 __A  :		List[str]       =			'mbart50'
 __A  :		int       =			'wav2vec2'

 __A  :		Optional[int]       =			tokenizer.eos_token_id
 __A  :		List[Any]       =			250004
 __A  :		str       =			tokenizer.eos_token_id

 __A  :		str       =			SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__			)

 hf_wavavec.save_pretrained(UpperCamelCase__			)
 feature_extractor.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
 UpperCAmelCase_ :     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('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
 parser.add_argument(
     '--encoder_config_path',
     default='facebook/wav2vec2-xls-r-1b',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/mbart-large-50-one-to-many-mmt',
     type=str,
     help='Path to hf decoder checkpoint config',
 )
 parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
 parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
 parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
 parser.add_argument('--encoder_output_dim', default=1_0_2_4, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config')

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )










'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






