





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__          =logging.get_logger(__name__)

UpperCamelCase__          ={
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     for attribute in key.split("."		):
          _SCREAMING_SNAKE_CASE				:		str  			=							getattr(__lowerCamelCase,				__lowerCamelCase		)

     if weight_type is not None:
          _SCREAMING_SNAKE_CASE				:		Tuple  			=							getattr(__lowerCamelCase,				__lowerCamelCase		).shape
     else:
          _SCREAMING_SNAKE_CASE				:		Tuple  			=							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":
          _SCREAMING_SNAKE_CASE				:		Dict  			=							value
     elif weight_type == "weight_g":
          _SCREAMING_SNAKE_CASE				:		Dict  			=							value
     elif weight_type == "weight_v":
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							value
     elif weight_type == "bias":
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							value
     else:
          _SCREAMING_SNAKE_CASE				:		Dict  			=							value

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






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[]
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE				:		Any  			=							hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				hf_model.config.feat_extract_norm == "group",				)
               _SCREAMING_SNAKE_CASE				:		Dict  			=							True
          else:
               for key, mapped_key in MAPPING.items():
                    _SCREAMING_SNAKE_CASE				:		Tuple  			=							"sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                    if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
                         _SCREAMING_SNAKE_CASE				:		Tuple  			=							True
                         if "*" in mapped_key:
                              _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							name.split(__lowerCamelCase		)[0].split("."		)[-2]
                              if not layer_index.isnumeric():
                                   continue
                              _SCREAMING_SNAKE_CASE				:		Dict  			=							mapped_key.replace("*",				__lowerCamelCase		)
                         if "weight_g" in name:
                              _SCREAMING_SNAKE_CASE				:		Tuple  			=							"weight_g"
                         elif "weight_v" in name:
                              _SCREAMING_SNAKE_CASE				:		List[str]  			=							"weight_v"
                         elif "weight" in name:
                              _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"weight"
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE				:		Any  			=							"bias"
                         else:
                              _SCREAMING_SNAKE_CASE				:		List[str]  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		str  			=							full_name.split("conv_layers."		)[-1]
     _SCREAMING_SNAKE_CASE				:		Any  			=							name.split("."		)
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							int(items[0]		)
     _SCREAMING_SNAKE_CASE				:		List[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."""
               )
               _SCREAMING_SNAKE_CASE				:		Optional[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."""
               )
               _SCREAMING_SNAKE_CASE				:		str  			=							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."
               )
               _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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."""
               )
               _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
     else:
          unused_weights.append(__lowerCamelCase		)






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		str  			=							SEWDConfig()
     if is_finetuned:
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							model.wav_encoder.wav_model.cfg
     else:
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							model.cfg

     _SCREAMING_SNAKE_CASE				:		Any  			=							fs_config.conv_bias
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							eval(fs_config.conv_feature_layers		)
     _SCREAMING_SNAKE_CASE				:		Dict  			=							[x[0] for x in conv_layers]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							[x[1] for x in conv_layers]
     _SCREAMING_SNAKE_CASE				:		int  			=							[x[2] for x in conv_layers]
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"gelu"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"layer" if fs_config.extractor_mode == "layer_norm" else "group"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							0.0
     _SCREAMING_SNAKE_CASE				:		str  			=							fs_config.activation_fn.name
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							fs_config.encoder_embed_dim
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							0.02
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							fs_config.encoder_ffn_embed_dim
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1e-5
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							fs_config.encoder_layerdrop
     _SCREAMING_SNAKE_CASE				:		int  			=							fs_config.encoder_attention_heads
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							fs_config.conv_pos_groups
     _SCREAMING_SNAKE_CASE				:		Any  			=							fs_config.conv_pos
     _SCREAMING_SNAKE_CASE				:		Any  			=							len(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		Any  			=							fs_config.encoder_layers
     _SCREAMING_SNAKE_CASE				:		str  			=							fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							fs_config.max_position_embeddings
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							fs_config.position_buckets
     _SCREAMING_SNAKE_CASE				:		Dict  			=							fs_config.share_att_key
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							fs_config.relative_attention
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							fs_config.position_biased_input
     _SCREAMING_SNAKE_CASE				:		int  			=							tuple(fs_config.pos_att_type.split("|"		)		)
     _SCREAMING_SNAKE_CASE				:		int  			=							fs_config.norm_rel_ebd

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							model.cfg
          _SCREAMING_SNAKE_CASE				:		Dict  			=							fs_config.final_dropout
          _SCREAMING_SNAKE_CASE				:		Any  			=							fs_config.layerdrop
     _SCREAMING_SNAKE_CASE				:		int  			=							fs_config.activation_dropout
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							fs_config.attention_dropout
     _SCREAMING_SNAKE_CASE				:		Any  			=							fs_config.dropout_input
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							fs_config.dropout
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							fs_config.mask_channel_length
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							fs_config.mask_channel_prob
     _SCREAMING_SNAKE_CASE				:		int  			=							fs_config.mask_length
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							fs_config.mask_prob

     _SCREAMING_SNAKE_CASE				:		Any  			=							"Wav2Vec2FeatureExtractor"
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							"Wav2Vec2CTCTokenizer"

     return config






@torch.no_grad()
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase=None,				__lowerCamelCase=None,				__lowerCamelCase=True		):

     if is_finetuned:
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		List[Any]  			=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path],				arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
     else:
          _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

     if config_path is not None:
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							SEWDConfig.from_pretrained(__lowerCamelCase		)
     else:
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							convert_config(model[0],				__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		str  			=							model[0].eval()

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							True if config.feat_extract_norm == "layer" else False
     _SCREAMING_SNAKE_CASE				:		str  			=							WavaVecaFeatureExtractor(
         feature_size=1,				sampling_rate=16000,				padding_value=0,				do_normalize=__lowerCamelCase,				return_attention_mask=__lowerCamelCase,				)

     if is_finetuned:
          if dict_path:
               _SCREAMING_SNAKE_CASE				:		int  			=							Dictionary.load(__lowerCamelCase		)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _SCREAMING_SNAKE_CASE				:		Any  			=							target_dict.pad_index
               _SCREAMING_SNAKE_CASE				:		Any  			=							target_dict.bos_index
               _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							target_dict.pad_index
               _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							target_dict.bos_index
               _SCREAMING_SNAKE_CASE				:		Tuple  			=							target_dict.eos_index
               _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							len(target_dict.symbols		)
               _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							os.path.join(__lowerCamelCase,				"vocab.json"		)
               if not os.path.isdir(__lowerCamelCase		):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCamelCase		)		)
                    return
               os.makedirs(__lowerCamelCase,				exist_ok=__lowerCamelCase		)
               with open(__lowerCamelCase,				"w",				encoding="utf-8"		) as vocab_handle:
                    json.dump(target_dict.indices,				__lowerCamelCase		)
               _SCREAMING_SNAKE_CASE				:		List[str]  			=							WavaVecaCTCTokenizer(
                   __lowerCamelCase,				unk_token=target_dict.unk_word,				pad_token=target_dict.pad_word,				bos_token=target_dict.bos_word,				eos_token=target_dict.eos_word,				word_delimiter_token="|",				do_lower_case=__lowerCamelCase,				)
               _SCREAMING_SNAKE_CASE				:		List[str]  			=							WavaVecaProcessor(feature_extractor=__lowerCamelCase,				tokenizer=__lowerCamelCase		)
               processor.save_pretrained(__lowerCamelCase		)

          _SCREAMING_SNAKE_CASE				:		Dict  			=							SEWDForCTC(__lowerCamelCase		)
     else:
          _SCREAMING_SNAKE_CASE				:		Any  			=							SEWDModel(__lowerCamelCase		)
          feature_extractor.save_pretrained(__lowerCamelCase		)

     recursively_load_weights(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)

     hf_model.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('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       UpperCamelCase__          =parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCamelCase__          =logging.get_logger(__name__)

UpperCamelCase__          ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          ={
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__          ={
    'microsoft/layoutlm-base-uncased': 512,
    'microsoft/layoutlm-large-uncased': 512,
}

UpperCamelCase__          ={
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			VOCAB_FILES_NAMES
       __snake_case       	=			PRETRAINED_VOCAB_FILES_MAP
       __snake_case       	=			PRETRAINED_INIT_CONFIGURATION
       __snake_case       	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case       	=			LayoutLMTokenizer




       def __init__(					self			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase="[UNK]"			,  __lowerCamelCase="[SEP]"			,  __lowerCamelCase="[PAD]"			,  __lowerCamelCase="[CLS]"			,  __lowerCamelCase="[MASK]"			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  **__lowerCamelCase			,  )	->   Dict:
            super().__init__(
                __lowerCamelCase			,  tokenizer_file=__lowerCamelCase			,  do_lower_case=__lowerCamelCase			,  unk_token=__lowerCamelCase			,  sep_token=__lowerCamelCase			,  pad_token=__lowerCamelCase			,  cls_token=__lowerCamelCase			,  mask_token=__lowerCamelCase			,  tokenize_chinese_chars=__lowerCamelCase			,  strip_accents=__lowerCamelCase			,  **__lowerCamelCase			,  )

            _SCREAMING_SNAKE_CASE				:		int  			=							json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
            if (
                normalizer_state.get("lowercase"			,  __lowerCamelCase							) != do_lower_case
                or normalizer_state.get("strip_accents"			,  __lowerCamelCase							) != strip_accents
                or normalizer_state.get("handle_chinese_chars"			,  __lowerCamelCase							) != tokenize_chinese_chars
            ):
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							getattr(__lowerCamelCase			,  normalizer_state.pop("type"							)							)
                 _SCREAMING_SNAKE_CASE				:		str  			=							do_lower_case
                 _SCREAMING_SNAKE_CASE				:		int  			=							strip_accents
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tokenize_chinese_chars
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							normalizer_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							do_lower_case




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=None							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

            return output




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   List[int]:
            _SCREAMING_SNAKE_CASE				:		Any  			=							[self.sep_token_id]
            _SCREAMING_SNAKE_CASE				:		str  			=							[self.cls_token_id]
            if token_ids_a is None:
                 return len(cls + token_ids_a + sep							) * [0]
            return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   Tuple[str]:
            _SCREAMING_SNAKE_CASE				:		int  			=							self._tokenizer.model.save(__lowerCamelCase			,  name=__lowerCamelCase							)
            return tuple(__lowerCamelCase							)