
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowercase__          ={
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowercase__          =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
	__a			:  str		    =  {}
	with open(lowerCAmelCase__  ,       '''r'''  ) as file:
		for line_number, line in enumerate(lowerCAmelCase__  ):
			__a			:  Dict		    =  line.strip()
			if line:
				__a			:  List[str]		    =  line.split()
				__a			:  Optional[int]		    =  line_number
				__a			:  Optional[int]		    =  words[0]
				__a			:  List[Any]		    =  value
	return result




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[str]  ):
	for attribute in key.split('''.'''  ):
		__a			:  Union[str, Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )

	__a			:  str		    =  None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase__  ):
			__a			:  int		    =  PARAM_MAPPING[full_name.split('''.'''  )[-1]]
			__a			:  Dict		    =  '''param'''

	if weight_type is not None and weight_type != "param":
		__a			:  Optional[Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  ).shape
	elif weight_type is not None and weight_type == "param":
		__a			:  List[str]		    =  hf_pointer
		for attribute in hf_param_name.split('''.'''  ):
			__a			:  List[Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )
		__a			:  int		    =  shape_pointer.shape

		# let's reduce dimension
		__a			:  Union[str, Any]		    =  value[0]
	else:
		__a			:  str		    =  hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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			:  Union[str, Any]		    =  value
	elif weight_type == "weight_g":
		__a			:  Tuple		    =  value
	elif weight_type == "weight_v":
		__a			:  str		    =  value
	elif weight_type == "bias":
		__a			:  int		    =  value
	elif weight_type == "param":
		for attribute in hf_param_name.split('''.'''  ):
			__a			:  int		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )
		__a			:  Tuple		    =  value
	else:
		__a			:  List[str]		    =  value

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    List[Any]  ):
	__a			:  List[Any]		    =  None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase__  ):
			__a			:  str		    =  PARAM_MAPPING[full_name.split('''.'''  )[-1]]
			__a			:  Optional[Any]		    =  '''param'''

	if weight_type is not None and weight_type != "param":
		__a			:  Optional[int]		    =  '''.'''.join([key, weight_type]  )
	elif weight_type is not None and weight_type == "param":
		__a			:  int		    =  '''.'''.join([key, hf_param_name]  )
	else:
		__a			:  List[str]		    =  key

	__a			:  List[str]		    =  value if '''lm_head''' in full_key else value[0]


lowercase__          ={
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]=None  ,       lowerCAmelCase__							:    str=None  ):
	__a			:  int		    =  False
	for key, mapped_key in MAPPING.items():
		__a			:  Union[str, Any]		    =  '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
		if key in name or key.split('''w2v_model.'''  )[-1] == name.split('''.'''  )[0]:
			__a			:  Optional[Any]		    =  True
			if "*" in mapped_key:
				__a			:  Tuple		    =  name.split(lowerCAmelCase__  )[0].split('''.'''  )[-2]
				__a			:  Dict		    =  mapped_key.replace('''*'''  ,       lowerCAmelCase__  )
			if "weight_g" in name:
				__a			:  Optional[int]		    =  '''weight_g'''
			elif "weight_v" in name:
				__a			:  Optional[int]		    =  '''weight_v'''
			elif "bias" in name:
				__a			:  Dict		    =  '''bias'''
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				__a			:  List[str]		    =  '''weight'''
			else:
				__a			:  Union[str, Any]		    =  None
			if hf_dict is not None:
				rename_dict(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
			else:
				set_recursively(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
			return is_used
	return is_used




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]  ):
	__a			:  List[str]		    =  []
	__a			:  Any		    =  fairseq_model.state_dict()

	__a			:  int		    =  hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		__a			:  Optional[int]		    =  False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       hf_model.config.feat_extract_norm == '''group'''  ,       )
			__a			:  Optional[Any]		    =  True
		else:
			__a			:  Optional[int]		    =  load_wavaveca_layer(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
		if not is_used:
			unused_weights.append(lowerCAmelCase__  )

	logger.warning(f"Unused weights: {unused_weights}"  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    str  ):
	__a			:  Tuple		    =  full_name.split('''conv_layers.'''  )[-1]
	__a			:  Optional[int]		    =  name.split('''.'''  )
	__a			:  int		    =  int(items[0]  )
	__a			:  Dict		    =  int(items[1]  )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."  )
			__a			:  Union[str, Any]		    =  value
			logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."  )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."  )
			__a			:  Optional[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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."  )
			__a			:  int		    =  value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."  )
			__a			:  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__  )




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Tuple=None  ,       lowerCAmelCase__							:    Union[str, Any]=None  ,       lowerCAmelCase__							:    int=True  ,       lowerCAmelCase__							:    Tuple=False  ):
	if config_path is not None:
		__a			:  Dict		    =  WavaVecaConfig.from_pretrained(lowerCAmelCase__  )
	else:
		__a			:  Dict		    =  WavaVecaConfig()

	if is_seq_class:
		__a			:  str		    =  read_txt_into_dict(lowerCAmelCase__  )
		__a			:  str		    =  idalabel
		__a			:  str		    =  WavaVecaForSequenceClassification(lowerCAmelCase__  )
		__a			:  str		    =  WavaVecaFeatureExtractor(
		    feature_size=1  ,       sampling_rate=1_6_0_0_0  ,       padding_value=0  ,       do_normalize=lowerCAmelCase__  ,       return_attention_mask=lowerCAmelCase__  ,       )
		feature_extractor.save_pretrained(lowerCAmelCase__  )

	elif is_finetuned:
		if dict_path:
			__a			:  Tuple		    =  Dictionary.load(lowerCAmelCase__  )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__a			:  Optional[Any]		    =  target_dict.pad_index
			__a			:  Tuple		    =  target_dict.bos_index
			__a			:  int		    =  target_dict.eos_index
			__a			:  Any		    =  len(target_dict.symbols  )
			__a			:  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__  )
			__a			:  Dict		    =  target_dict.indices

			# fairseq has the <pad> and <s> switched
			__a			:  str		    =  0
			__a			:  Any		    =  1
			with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ) as vocab_handle:
				json.dump(lowerCAmelCase__  ,       lowerCAmelCase__  )
			__a			:  Tuple		    =  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__  ,       )
			__a			:  Any		    =  True if config.feat_extract_norm == '''layer''' else False
			__a			:  Union[str, Any]		    =  WavaVecaFeatureExtractor(
			    feature_size=1  ,       sampling_rate=1_6_0_0_0  ,       padding_value=0  ,       do_normalize=lowerCAmelCase__  ,       return_attention_mask=lowerCAmelCase__  ,       )
			__a			:  Dict		    =  WavaVecaProcessor(feature_extractor=lowerCAmelCase__  ,       tokenizer=lowerCAmelCase__  )
			processor.save_pretrained(lowerCAmelCase__  )

		__a			:  Tuple		    =  WavaVecaForCTC(lowerCAmelCase__  )
	else:
		__a			:  List[str]		    =  WavaVecaForPreTraining(lowerCAmelCase__  )

	if is_finetuned or is_seq_class:
		__a			:  int		    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]  ,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
	else:
		__a			:  List[str]		    =  argparse.Namespace(task='''audio_pretraining'''  )
		__a			:  int		    =  fairseq.tasks.setup_task(lowerCAmelCase__  )

		__a			:  List[Any]		    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,       task=lowerCAmelCase__  )

	__a			:  int		    =  model[0].eval()

	recursively_load_weights(lowerCAmelCase__  ,       lowerCAmelCase__  ,       not is_finetuned  )

	hf_wavavec.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
						lowercase__          =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(
						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						parser.add_argument(
						    '--is_seq_class',
						    action='store_true',
						    help='Whether the model to convert is a fine-tuned sequence classification model or not',
						)
						lowercase__          =parser.parse_args()

						lowercase__          =not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [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 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
