






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()
lowerCamelCase			:					Union[str, Any]          =	logging.get_logger(__name__)

lowerCamelCase			:					Optional[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''',
}
lowerCamelCase			:					Optional[int]          =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):






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

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

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





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __lowerCAmelCase								=					[]
     __lowerCAmelCase								=					fairseq_model.state_dict()

     __lowerCAmelCase								=					hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     __lowerCAmelCase								=					None

     for name, value in fairseq_dict.items():
          __lowerCAmelCase								=					False
          if "conv_layers" in name:
               load_conv_layer(
                   __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, hf_model.config.feat_extract_norm == "group"					, )
               __lowerCAmelCase								=					True
          elif name.split("."       )[0] == "proj":
               __lowerCAmelCase								=					fairseq_model.proj
               __lowerCAmelCase								=					True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                         __lowerCAmelCase								=					True
                         if "*" in mapped_key:
                              __lowerCAmelCase								=					name.split(__UpperCamelCase       )[0].split("."       )[-2]
                              __lowerCAmelCase								=					mapped_key.replace("*"					, __UpperCamelCase       )
                         if "weight_g" in name:
                              __lowerCAmelCase								=					"""weight_g"""
                         elif "weight_v" in name:
                              __lowerCAmelCase								=					"""weight_v"""
                         elif "bias" in name:
                              __lowerCAmelCase								=					"""bias"""
                         elif "weight" in name:
                              __lowerCAmelCase								=					"""weight"""
                         else:
                              __lowerCAmelCase								=					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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					full_name.split("conv_layers."       )[-1]
     __lowerCAmelCase								=					name.split("."       )
     __lowerCAmelCase								=					int(items[0]       )
     __lowerCAmelCase								=					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."""
               )
               __lowerCAmelCase								=					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."""
               )
               __lowerCAmelCase								=					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."
               )
               __lowerCAmelCase								=					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."""
               )
               __lowerCAmelCase								=					value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
     else:
          unused_weights.append(__UpperCamelCase       )





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					emb.weight.shape
     __lowerCAmelCase								=					nn.Linear(__UpperCamelCase					, __UpperCamelCase					, bias=__UpperCamelCase       )
     __lowerCAmelCase								=					emb.weight.data
     return lin_layer





def       __lowerCAmelCase      (						__snake_case       ):






     with open(__UpperCamelCase					, "r"					, encoding="utf-8"       ) as f:
          __lowerCAmelCase								=					f.readlines()
          __lowerCAmelCase								=					[line.split(" "       )[0] for line in lines]

     __lowerCAmelCase								=					len(__UpperCamelCase       )

     __lowerCAmelCase								=					{
         """<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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, ):
     __lowerCAmelCase								=					WavaVecaConfig.from_pretrained(__UpperCamelCase       )
     __lowerCAmelCase								=					SpeechaTextaConfig.from_pretrained(
         __UpperCamelCase					, vocab_size=__UpperCamelCase					, decoder_layers=__UpperCamelCase					, do_stable_layer_norm=__UpperCamelCase       )

     __lowerCAmelCase								=					WavaVecaFeatureExtractor(
         feature_size=1					, sampling_rate=1_6000					, padding_value=0					, do_normalize=__UpperCamelCase					, return_attention_mask=__UpperCamelCase					, )

     __lowerCAmelCase								=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]					, arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
     __lowerCAmelCase								=					model[0].eval()

     # set weights for wav2vec2 encoder
     __lowerCAmelCase								=					WavaVecaModel(__UpperCamelCase       )
     __lowerCAmelCase								=					recursively_load_weights_wavaveca(model.encoder					, __UpperCamelCase       )

     __lowerCAmelCase								=					SpeechaTextaForCausalLM(__UpperCamelCase       )
     __lowerCAmelCase								=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=__UpperCamelCase       )

     # set output linear layer
     unexpected_keys.remove("embed_out"       )
     __lowerCAmelCase								=					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}"""       )

     __lowerCAmelCase								=					SpeechEncoderDecoderModel(encoder=__UpperCamelCase					, decoder=__UpperCamelCase       )
     __lowerCAmelCase								=					False

     # add projection layer
     __lowerCAmelCase								=					nn.Parameter(projection_layer.weight       )
     __lowerCAmelCase								=					nn.Parameter(projection_layer.bias       )

     __lowerCAmelCase								=					create_vocab_dict(__UpperCamelCase       )

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

     __lowerCAmelCase								=					SpeechaTextaTokenizer(os.path.join(__UpperCamelCase					, "vocab.json"       )       )
     tokenizer.save_pretrained(__UpperCamelCase       )

     __lowerCAmelCase								=					hf_wavavec.config.to_dict()
     __lowerCAmelCase								=					tokenizer.pad_token_id
     __lowerCAmelCase								=					tokenizer.bos_token_id
     __lowerCAmelCase								=					tokenizer.eos_token_id
     __lowerCAmelCase								=					"""speech_to_text_2"""
     __lowerCAmelCase								=					"""wav2vec2"""

     __lowerCAmelCase								=					SpeechEncoderDecoderConfig.from_dict(__UpperCamelCase       )

     hf_wavavec.save_pretrained(__UpperCamelCase       )
     feature_extractor.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('''--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''')

 lowerCamelCase			:					Any          =	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,
 )














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					Any         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""markuplm"""


      def __init__(	self					,						__UpperCamelCase=3_0_5_2_2					,						__UpperCamelCase=7_6_8					,						__UpperCamelCase=1_2					,						__UpperCamelCase=1_2					,						__UpperCamelCase=3_0_7_2					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=0					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						__UpperCamelCase=2_5_6					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_1_6					,						__UpperCamelCase=1_0_0_1					,						__UpperCamelCase=3_2					,						__UpperCamelCase=5_0					,						__UpperCamelCase="absolute"					,						__UpperCamelCase=True					,						__UpperCamelCase=None					,						**__UpperCamelCase					,						)->	Dict:
           super().__init__(
               pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase					,						)
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					type_vocab_size
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					position_embedding_type
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					classifier_dropout
           # additional properties
           __lowerCAmelCase								=					max_depth
           __lowerCAmelCase								=					max_xpath_tag_unit_embeddings
           __lowerCAmelCase								=					max_xpath_subs_unit_embeddings
           __lowerCAmelCase								=					tag_pad_id
           __lowerCAmelCase								=					subs_pad_id
           __lowerCAmelCase								=					xpath_unit_hidden_size






