






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
A_						:						Any						      =					logging.get_logger(__name__)

A_						:						List[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_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',
}
A_						:						Dict						      =					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Tuple:
  for attribute in key.split('.'    ):
    UpperCamelCase_:   Dict        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )

  if weight_type is not None:
    UpperCamelCase_:   int        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    ).shape
  else:
    UpperCamelCase_:   List[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":
    UpperCamelCase_:   Optional[Any]        =							value
  elif weight_type == "weight_g":
    UpperCamelCase_:   Any        =							value
  elif weight_type == "weight_v":
    UpperCamelCase_:   Optional[int]        =							value
  elif weight_type == "bias":
    UpperCamelCase_:   Any        =							value
  else:
    UpperCamelCase_:   List[Any]        =							value

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Any        =							[]
  UpperCamelCase_:   str        =							fairseq_model.state_dict()

  UpperCamelCase_:   int        =							hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  UpperCamelCase_:   Optional[int]        =							None

  for name, value in fairseq_dict.items():
    UpperCamelCase_:   Optional[int]        =							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       hf_model.config.feat_extract_norm == 'group'      ,       )
      UpperCamelCase_:   Optional[Any]        =							True
    elif name.split('.'    )[0] == "proj":
      UpperCamelCase_:   Union[str, Any]        =							fairseq_model.proj
      UpperCamelCase_:   int        =							True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('w2v_model.'    )[-1] == name.split('.'    )[0]:
          UpperCamelCase_:   int        =							True
          if "*" in mapped_key:
            UpperCamelCase_:   List[Any]        =							name.split(UpperCAmelCase__    )[0].split('.'    )[-2]
            UpperCamelCase_:   Tuple        =							mapped_key.replace('*'      ,       UpperCAmelCase__    )
          if "weight_g" in name:
            UpperCamelCase_:   Optional[int]        =							'weight_g'
          elif "weight_v" in name:
            UpperCamelCase_:   List[str]        =							'weight_v'
          elif "bias" in name:
            UpperCamelCase_:   int        =							'bias'
          elif "weight" in name:
            UpperCamelCase_:   Optional[Any]        =							'weight'
          else:
            UpperCamelCase_:   List[str]        =							None
          set_recursively(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
        continue
    if not is_used:
      unused_weights.append(UpperCAmelCase__    )

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

  return proj_weight

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   Union[str, Any]        =							full_name.split('conv_layers.'    )[-1]
  UpperCamelCase_:   Tuple        =							name.split('.'    )
  UpperCamelCase_:   Dict        =							int(items[0]    )
  UpperCamelCase_:   str        =							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_:   int        =							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_:   int        =							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_:   Dict        =							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_:   Optional[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     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							emb.weight.shape
  UpperCamelCase_:   Union[str, Any]        =							nn.Linear(UpperCAmelCase__      ,       UpperCAmelCase__      ,       bias=UpperCAmelCase__    )
  UpperCamelCase_:   Optional[int]        =							emb.weight.data
  return lin_layer

def     snake_case   (UpperCAmelCase__    )			->							int:
  with open(UpperCAmelCase__      ,       'r'      ,       encoding='utf-8'    ) as f:
    UpperCamelCase_:   str        =							f.readlines()
    UpperCamelCase_:   List[Any]        =							[line.split(' '    )[0] for line in lines]

  UpperCamelCase_:   Dict        =							len(UpperCAmelCase__    )

  UpperCamelCase_:   Tuple        =							{
      '<s>': 0,
      '<pad>': 1,
      '</s>': 2,
      '<unk>': 3,
  }

  vocab_dict.update(dict(zip(UpperCAmelCase__      ,       range(4      ,       num_words + 4    )    )    )    )
  return vocab_dict

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )			->							Tuple:
  UpperCamelCase_:   Dict        =							WavaVecaConfig.from_pretrained(UpperCAmelCase__    )
  UpperCamelCase_:   Tuple        =							SpeechaTextaConfig.from_pretrained(
      UpperCAmelCase__      ,       vocab_size=UpperCAmelCase__      ,       decoder_layers=UpperCAmelCase__      ,       do_stable_layer_norm=UpperCAmelCase__    )

  UpperCamelCase_:   Any        =							WavaVecaFeatureExtractor(
      feature_size=1      ,       sampling_rate=1_6_0_0_0      ,       padding_value=0      ,       do_normalize=UpperCAmelCase__      ,       return_attention_mask=UpperCAmelCase__      ,       )

  UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]      ,       arg_overrides={'data': '/'.join(dict_path.split('/'    )[:-1]    )}    )
  UpperCamelCase_:   Optional[int]        =							model[0].eval()

  # set weights for wav2vec2 encoder
  UpperCamelCase_:   Tuple        =							WavaVecaModel(UpperCAmelCase__    )
  UpperCamelCase_:   Any        =							recursively_load_weights_wavaveca(model.encoder      ,       UpperCAmelCase__    )

  UpperCamelCase_:   Any        =							SpeechaTextaForCausalLM(UpperCAmelCase__    )
  UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,       strict=UpperCAmelCase__    )

  # set output linear layer
  unexpected_keys.remove('embed_out'    )
  UpperCamelCase_:   Dict        =							nn.Parameter(model.decoder.embed_out.detach()    )

  # layer norm is init to identity matrix so leaving it is fine
  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}'''    )

  UpperCamelCase_:   Any        =							SpeechEncoderDecoderModel(encoder=UpperCAmelCase__      ,       decoder=UpperCAmelCase__    )
  UpperCamelCase_:   Any        =							False

  # add projection layer
  UpperCamelCase_:   int        =							nn.Parameter(projection_layer.weight    )
  UpperCamelCase_:   str        =							nn.Parameter(projection_layer.bias    )

  UpperCamelCase_:   Dict        =							create_vocab_dict(UpperCAmelCase__    )

  with open(os.path.join(UpperCAmelCase__      ,       'vocab.json'    )      ,       'w'    ) as fp:
    json.dump(UpperCAmelCase__      ,       UpperCAmelCase__    )

  UpperCamelCase_:   Dict        =							SpeechaTextaTokenizer(os.path.join(UpperCAmelCase__      ,       'vocab.json'    )    )
  tokenizer.save_pretrained(UpperCAmelCase__    )

  UpperCamelCase_:   int        =							hf_wavavec.config.to_dict()
  UpperCamelCase_:   Union[str, Any]        =							tokenizer.pad_token_id
  UpperCamelCase_:   List[Any]        =							tokenizer.bos_token_id
  UpperCamelCase_:   Any        =							tokenizer.eos_token_id
  UpperCamelCase_:   Any        =							'speech_to_text_2'
  UpperCamelCase_:   List[Any]        =							'wav2vec2'

  UpperCamelCase_:   int        =							SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__    )

  hf_wavavec.save_pretrained(UpperCAmelCase__    )
  feature_extractor.save_pretrained(UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						Dict						      =					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(
       '--encoder_config_path',
       default='facebook/wav2vec2-large-lv60',
       type=str,
       help='Path to hf encoder wav2vec2 checkpoint config',
   )
   parser.add_argument(
       '--decoder_config_path',
       default='facebook/s2t-small-mustc-en-fr-st',
       type=str,
       help='Path to hf decoder s2t checkpoint config',
   )
   parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
   parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

   A_						:						Dict						      =					parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       vocab_size=args.vocab_size,
       num_decoder_layers=args.num_decoder_layers,
   )


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
