


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()
snake_case__     							= logging.get_logger(__name__)

snake_case__     							= {
    '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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Dict      ,						lowercase__  :int      ,						lowercase__  :Any				)       ->  List[str]:
						for attribute in key.split('.'				):
												__magic_name__				:							Optional[Any]				=      getattr(lowercase__      ,						lowercase__				)

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

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Tuple      ,						lowercase__  :Dict				)       ->  int:
						__magic_name__				:							List[str]				=      []
						__magic_name__				:							Any				=      fairseq_model.state_dict()

						__magic_name__				:							Union[str, Any]				=      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

						logger.warning(f"Unused weights: {unused_weights}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :int      ,						lowercase__  :str      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Optional[Any]				)       ->  Dict:
						__magic_name__				:							Optional[Any]				=      full_name.split('conv_layers.'				)[-1]
						__magic_name__				:							List[str]				=      name.split('.'				)
						__magic_name__				:							List[Any]				=      int(items[0]				)
						__magic_name__				:							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."
																		)
																		__magic_name__				:							List[str]				=      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."
																		)
																		__magic_name__				:							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."
																		)
																		__magic_name__				:							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."
																		)
																		__magic_name__				:							Dict				=      value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
						else:
												unused_weights.append(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :Dict				)       ->  Any:
						__magic_name__				:							Dict				=      SEWDConfig()
						if is_finetuned:
												__magic_name__				:							List[Any]				=      model.wav_encoder.wav_model.cfg
						else:
												__magic_name__				:							Union[str, Any]				=      model.cfg

						__magic_name__				:							str				=      fs_config.conv_bias
						__magic_name__				:							str				=      eval(fs_config.conv_feature_layers				)
						__magic_name__				:							Tuple				=      [x[0] for x in conv_layers]
						__magic_name__				:							Any				=      [x[1] for x in conv_layers]
						__magic_name__				:							List[Any]				=      [x[2] for x in conv_layers]
						__magic_name__				:							Optional[int]				=      'gelu'
						__magic_name__				:							Tuple				=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						__magic_name__				:							List[str]				=      0.0
						__magic_name__				:							Optional[Any]				=      fs_config.activation_fn.name
						__magic_name__				:							List[str]				=      fs_config.encoder_embed_dim
						__magic_name__				:							Optional[int]				=      0.0_2
						__magic_name__				:							Optional[Any]				=      fs_config.encoder_ffn_embed_dim
						__magic_name__				:							Dict				=      1e-5
						__magic_name__				:							List[str]				=      fs_config.encoder_layerdrop
						__magic_name__				:							int				=      fs_config.encoder_attention_heads
						__magic_name__				:							Optional[int]				=      fs_config.conv_pos_groups
						__magic_name__				:							int				=      fs_config.conv_pos
						__magic_name__				:							int				=      len(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      fs_config.encoder_layers
						__magic_name__				:							Optional[Any]				=      fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						__magic_name__				:							Tuple				=      fs_config.max_position_embeddings
						__magic_name__				:							Tuple				=      fs_config.position_buckets
						__magic_name__				:							Optional[int]				=      fs_config.share_att_key
						__magic_name__				:							List[str]				=      fs_config.relative_attention
						__magic_name__				:							Union[str, Any]				=      fs_config.position_biased_input
						__magic_name__				:							str				=      tuple(fs_config.pos_att_type.split('|'				)				)
						__magic_name__				:							Optional[Any]				=      fs_config.norm_rel_ebd

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

						__magic_name__				:							Union[str, Any]				=      'Wav2Vec2FeatureExtractor'
						__magic_name__				:							Optional[Any]				=      'Wav2Vec2CTCTokenizer'

						return config
@torch.no_grad()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Optional[int]      ,						lowercase__  :int=None      ,						lowercase__  :Dict=None      ,						lowercase__  :Dict=True				)       ->  int:

						if is_finetuned:
												__magic_name__		, __magic_name__		, __magic_name__				:							Union[str, Any]				=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]      ,						arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
						else:
												__magic_name__		, __magic_name__		, __magic_name__				:							str				=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

						if config_path is not None:
												__magic_name__				:							Optional[int]				=      SEWDConfig.from_pretrained(lowercase__				)
						else:
												__magic_name__				:							Tuple				=      convert_config(model[0]      ,						lowercase__				)
						__magic_name__				:							Any				=      model[0].eval()

						__magic_name__				:							Any				=      True if config.feat_extract_norm == 'layer' else False
						__magic_name__				:							Optional[int]				=      WavaVecaFeatureExtractor(
						    feature_size=1      ,						sampling_rate=1_6_0_0_0      ,						padding_value=0      ,						do_normalize=lowercase__      ,						return_attention_mask=lowercase__      ,						)

						if is_finetuned:
												if dict_path:
																		__magic_name__				:							Any				=      Dictionary.load(lowercase__				)

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

												__magic_name__				:							Union[str, Any]				=      SEWDForCTC(lowercase__				)
						else:
												__magic_name__				:							int				=      SEWDModel(lowercase__				)
												feature_extractor.save_pretrained(lowercase__				)

						recursively_load_weights(lowercase__      ,						lowercase__      ,						lowercase__				)

						hf_model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= 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'
			)
			snake_case__     							= parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)






import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


snake_case__     							= 'none'
snake_case__     							= 'none'
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						__magic_name__				:							Dict				=      value.split(lowercase__				)
						return len(lowercase__				) > 1 and any((len(lowercase__				) == 3 for x in split)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]				)       ->  List[str]:
						if isinstance(lowercase__      ,						lowercase__				):
												return value
						if isinstance(lowercase__      ,						lowercase__				):
												return float(lowercase__				)
						if not isinstance(lowercase__      ,						lowercase__				):
												raise ValueError('Argument value is not a string. Can\'t parse it as float'				)
						__magic_name__				:							List[str]				=      value

						try:
												# Example: 1,000.7
												if "." in sanitized and "," in sanitized:
																		return float(sanitized.replace(','      ,						''				)				)
												# 1,000
												if "," in sanitized and _split_thousands(','      ,						lowercase__				):
																		return float(sanitized.replace(','      ,						''				)				)
												# 5,5556
												if "," in sanitized and sanitized.count(','				) == 1 and not _split_thousands(','      ,						lowercase__				):
																		return float(sanitized.replace(','      ,						'.'				)				)
												# 0.0.0.1
												if sanitized.count('.'				) > 1:
																		return float(sanitized.replace('.'      ,						''				)				)
												# 0,0,0,1
												if sanitized.count(','				) > 1:
																		return float(sanitized.replace(','      ,						''				)				)
												return float(lowercase__				)
						except ValueError:
												# Avoid adding the sanitized value in the error message.
												raise ValueError('Unable to convert value to float'				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Tuple:
						if answer is None:
												return None
						try:
												__magic_name__				:							Any				=      convert_to_float(lowercase__				)
												if isinstance(lowercase__      ,						lowercase__				) and math.isnan(lowercase__				):
																		return None
												return value
						except ValueError:
												return answer.lower()


snake_case__     							= {
    'text': lambda x: x,
    'real': convert_to_float,
}



class     snake_case__  (      enum.Enum  ):
				__lowerCAmelCase		=      0
				__lowerCAmelCase		=      1
				__lowerCAmelCase		=      2
				__lowerCAmelCase		=      3
				__lowerCAmelCase		=      4
				__lowerCAmelCase		=      5






class     snake_case__  (      enum.Enum  ):
				__lowerCAmelCase		=      0
				__lowerCAmelCase		=      1
				__lowerCAmelCase		=      2






@dataclasses.dataclass
class     snake_case__  :
				__lowerCAmelCase		=      42
				__lowerCAmelCase		=      42
				__lowerCAmelCase		=      42






snake_case__     							= re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any				)       ->  str:
						return list(_TOKENIZER.findall(x.lower()				)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :str      ,						lowercase__  :Dict				)       ->  Dict:
						if operator == _Operator.EQUALS:
												return src == tgt
						elif operator == _Operator.GREATER:
												return src > tgt
						elif operator == _Operator.LESSER:
												return src < tgt
						raise ValueError(f"Unknown operator: {operator}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Tuple      ,						lowercase__  :Union[str, Any]				)       ->  Optional[int]:
						__magic_name__				:							str				=      table['types']
						return _TYPE_CONVERTER[types[column]](lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple				)       ->  Union[str, Any]:
						return isinstance(lowercase__      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :List[str]      ,						lowercase__  :int				)       ->  str:
						for cond in conditions:
												__magic_name__				:							Optional[Any]				=      row[cond.column]

												__magic_name__				:							List[str]				=      _parse_value(lowercase__      ,						cond.column      ,						cond.cmp_value				)

												if _is_string(lowercase__				) and _is_string(lowercase__				):
																		__magic_name__				:							str				=      _normalize_for_match(lowercase__				)
																		__magic_name__				:							List[str]				=      _normalize_for_match(lowercase__				)

												if not isinstance(lowercase__      ,						type(lowercase__				)				):
																		raise ValueError('Type difference {} != {}'.format(type(lowercase__				)      ,						type(lowercase__				)				)				)

												if not _compare(cond.operator      ,						lowercase__      ,						lowercase__				):
																		return False
						return True
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :List[Any]      ,						lowercase__  :List[Any]				)       ->  int:
						if not answer_coordinates:
												if aggregation_op == _Aggregation.COUNT:
																		return 0.0
												else:
																		return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
						if aggregation_op == _Aggregation.COUNT:
												return float(len(lowercase__				)				)

						# If we have just one answer, if float returns it or try a conversion.
						__magic_name__				:							Any				=      [table['rows'][i][j] for (i, j) in answer_coordinates]
						if len(lowercase__				) == 1:
												try:
																		return convert_to_float(values[0]				)
												except ValueError as e:
																		if aggregation_op != _Aggregation.NONE:
																								raise e

						if aggregation_op == _Aggregation.NONE:
												return None

						# Other aggregation only support numeric values. Bail out if we have strings.
						if not all((isinstance(lowercase__      ,						(int, float)				) for v in values)				):
												return None

						if aggregation_op == _Aggregation.SUM:
												return float(sum(lowercase__				)				)
						elif aggregation_op == _Aggregation.AVERAGE:
												return sum(lowercase__				) / len(lowercase__				)
						else:
												raise ValueError(f"Unknown aggregation: {aggregation_op}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Any				)       ->  Optional[int]:
						__magic_name__				:							Optional[Any]				=      sql_query['agg']
						if aggregation_op_index >= 3:
												__magic_name__				:							Any				=      _Aggregation(lowercase__				)
						else:
												__magic_name__				:							int				=      _Aggregation.NONE

						__magic_name__				:							str				=      sql_query['sel']
						__magic_name__				:							Any				=      [
						    _Condition(lowercase__      ,						_Operator(lowercase__				)      ,						lowercase__				)
						    for column, operator, cmp_value in zip(
						        sql_query['conds']['column_index']      ,						sql_query['conds']['operator_index']      ,						sql_query['conds']['condition']				)
						]

						__magic_name__				:							Any				=      []
						for row in range(len(table['rows']				)				):
												if _respect_conditions(lowercase__      ,						table['rows'][row]      ,						lowercase__				):
																		indices.append((row, target_column)				)

						if not indices:
												return [], aggregation_op

						if len(lowercase__				) == 1:
												return indices, aggregation_op

						# Parsing of MIN/MAX.
						if aggregation_op_index in (1, 2):
												__magic_name__				:							str				=      {2: min, 1: max}
												__magic_name__				:							Optional[int]				=      [(table['rows'][i][j], index) for index, (i, j) in enumerate(lowercase__				)]
												__magic_name__				:							str				=      functools.reduce(operators[sql_query['agg']]      ,						lowercase__				)

												__magic_name__				:							List[Any]				=      [indices[reduced[1]]]
												return ret, _Aggregation.NONE

						return indices, aggregation_op
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Any      ,						lowercase__  :Optional[int]				)       ->  List[Any]:
						if float_answer is not None:
												return [str(lowercase__				)]
						return [str(table['real_rows'][r][c]				) for r, c in answer_coordinates]

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]				)       ->  List:
						__magic_name__		, __magic_name__				:							List[Any]				=      _get_answer_coordinates(lowercase__      ,						lowercase__				)
						__magic_name__				:							List[Any]				=      _get_float_answer(lowercase__      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Any				=      _get_answer_text(lowercase__      ,						lowercase__      ,						lowercase__				)
						# keep the original data the same with TaPas
						if len(lowercase__				) == 0:
												__magic_name__				:							int				=      [EMPTY_ANSWER]
						return answer_text


