






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()
__snake_case      :      str             =       logging.get_logger(__name__)

__snake_case      :      str             =       {
    """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""",
}
__snake_case      :      Dict             =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  {}
						with open(UpperCamelCase_      ,    'r'						) as file:
												for line_number, line in enumerate(UpperCamelCase_						):
																		lowerCAmelCase__        =  line.strip()
																		if line:
																								lowerCAmelCase__        =  line.split()
																								lowerCAmelCase__        =  line_number
																								lowerCAmelCase__        =  words[0]
																								lowerCAmelCase__        =  value
						return result







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[int]						)					->				str:

						"""simple docstring"""



						for attribute in key.split('.'						):
												lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    UpperCamelCase_						)

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

						if weight_type is not None and weight_type != "param":
												lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    UpperCamelCase_						).shape
						elif weight_type is not None and weight_type == "param":
												lowerCAmelCase__        =  hf_pointer
												for attribute in hf_param_name.split('.'						):
																		lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    UpperCamelCase_						)
												lowerCAmelCase__        =  shape_pointer.shape

												# let's reduce dimension
												lowerCAmelCase__        =  value[0]
						else:
												lowerCAmelCase__        =  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":
												lowerCAmelCase__        =  value
						elif weight_type == "weight_g":
												lowerCAmelCase__        =  value
						elif weight_type == "weight_v":
												lowerCAmelCase__        =  value
						elif weight_type == "bias":
												lowerCAmelCase__        =  value
						elif weight_type == "param":
												for attribute in hf_param_name.split('.'						):
																		lowerCAmelCase__        =  getattr(UpperCamelCase_      ,    UpperCamelCase_						)
												lowerCAmelCase__        =  value
						else:
												lowerCAmelCase__        =  value

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   int						)					->				Any:

						"""simple docstring"""



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

						if weight_type is not None and weight_type != "param":
												lowerCAmelCase__        =  '.'.join([key, weight_type]						)
						elif weight_type is not None and weight_type == "param":
												lowerCAmelCase__        =  '.'.join([key, hf_param_name]						)
						else:
												lowerCAmelCase__        =  key

						lowerCAmelCase__        =  value if 'lm_head' in full_key else value[0]


__snake_case      :      Any             =       {
    """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				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Any=None      ,    UpperCamelCase_    :   Dict=None						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  False
						for key, mapped_key in MAPPING.items():
												lowerCAmelCase__        =  '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]:
																		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:
																								# TODO: don't match quantizer.weight_proj
																								lowerCAmelCase__        =  'weight'
																		else:
																								lowerCAmelCase__        =  None
																		if hf_dict is not None:
																								rename_dict(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
																		else:
																								set_recursively(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
																		return is_used
						return is_used







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Union[str, Any]						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						lowerCAmelCase__        =  fairseq_model.state_dict()

						lowerCAmelCase__        =  hf_model.wavaveca.feature_extractor

						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
												else:
																		lowerCAmelCase__        =  load_wavaveca_layer(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
												if not is_used:
																		unused_weights.append(UpperCamelCase_						)

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]						)					->				List[Any]:

						"""simple docstring"""



						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:
																		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."						)
																		lowerCAmelCase__        =  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."						)
																		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:
																		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."						)
																		lowerCAmelCase__        =  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."						)
																		lowerCAmelCase__        =  value
																		logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
						else:
												unused_weights.append(UpperCamelCase_						)







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   str=None      ,    UpperCamelCase_    :   Dict=None      ,    UpperCamelCase_    :   str=True      ,    UpperCamelCase_    :   Optional[Any]=False						)					->				Tuple:

						"""simple docstring"""



						if config_path is not None:
												lowerCAmelCase__        =  WavaVecaConfig.from_pretrained(UpperCamelCase_						)
						else:
												lowerCAmelCase__        =  WavaVecaConfig()

						if is_seq_class:
												lowerCAmelCase__        =  read_txt_into_dict(UpperCamelCase_						)
												lowerCAmelCase__        =  idalabel
												lowerCAmelCase__        =  WavaVecaForSequenceClassification(UpperCamelCase_						)
												lowerCAmelCase__        =  WavaVecaFeatureExtractor(
												    feature_size=1      ,    sampling_rate=1_6000      ,    padding_value=0      ,    do_normalize=UpperCamelCase_      ,    return_attention_mask=UpperCamelCase_      ,    )
												feature_extractor.save_pretrained(UpperCamelCase_						)

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

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowerCAmelCase__        =  target_dict.pad_index
																		lowerCAmelCase__        =  target_dict.bos_index
																		lowerCAmelCase__        =  target_dict.eos_index
																		lowerCAmelCase__        =  len(target_dict.symbols						)
																		lowerCAmelCase__        =  os.path.join(UpperCamelCase_      ,    'vocab.json'						)
																		if not os.path.isdir(UpperCamelCase_						):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCamelCase_						)						)
																								return
																		os.makedirs(UpperCamelCase_      ,    exist_ok=UpperCamelCase_						)
																		lowerCAmelCase__        =  target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		lowerCAmelCase__        =  0
																		lowerCAmelCase__        =  1
																		with open(UpperCamelCase_      ,    'w'      ,    encoding='utf-8'						) as vocab_handle:
																								json.dump(UpperCamelCase_      ,    UpperCamelCase_						)
																		lowerCAmelCase__        =  WavaVecaCTCTokenizer(
																		    UpperCamelCase_      ,    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=UpperCamelCase_      ,    )
																		lowerCAmelCase__        =  True if config.feat_extract_norm == 'layer' else False
																		lowerCAmelCase__        =  WavaVecaFeatureExtractor(
																		    feature_size=1      ,    sampling_rate=1_6000      ,    padding_value=0      ,    do_normalize=UpperCamelCase_      ,    return_attention_mask=UpperCamelCase_      ,    )
																		lowerCAmelCase__        =  WavaVecaProcessor(feature_extractor=UpperCamelCase_      ,    tokenizer=UpperCamelCase_						)
																		processor.save_pretrained(UpperCamelCase_						)

												lowerCAmelCase__        =  WavaVecaForCTC(UpperCamelCase_						)
						else:
												lowerCAmelCase__        =  WavaVecaForPreTraining(UpperCamelCase_						)

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

												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,    task=UpperCamelCase_						)

						lowerCAmelCase__        =  model[0].eval()

						recursively_load_weights(UpperCamelCase_      ,    UpperCamelCase_      ,    not is_finetuned						)

						hf_wavavec.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
			__snake_case      :      List[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("""--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""",
			)
			__snake_case      :      List[Any]             =       parser.parse_args()

			__snake_case      :      List[Any]             =       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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





