


"""simple docstring"""




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        lowercase							(		lowerCAmelCase__ :     Dict			) ->      Union[str, Any]:
		__a           =			{}
		with open(lowerCAmelCase__    ,					'''r'''			) as file:
				for line_number, line in enumerate(lowerCAmelCase__			):
						__a           =			line.strip()
						if line:
								__a           =			line.split()
								__a           =			line_number
								__a           =			words[0]
								__a           =			value
		return result

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]			) ->      Union[str, Any]:
		for attribute in key.split('''.'''			):
				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

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

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

def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Any			) ->      Any:
		__a           =			None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(lowerCAmelCase__			):
						__a           =			PARAM_MAPPING[full_name.split('''.'''			)[-1]]
						__a           =			'''param'''

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

		__a           =			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        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Union[str, Any]=None    ,					lowerCAmelCase__ :     Union[str, Any]=None			) ->      List[Any]:
		__a           =			False
		for key, mapped_key in MAPPING.items():
				__a           =			'''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           =			True
						if "*" in mapped_key:
								__a           =			name.split(lowerCAmelCase__			)[0].split('''.'''			)[-2]
								__a           =			mapped_key.replace('''*'''    ,					lowerCAmelCase__			)
						if "weight_g" in name:
								__a           =			'''weight_g'''
						elif "weight_v" in name:
								__a           =			'''weight_v'''
						elif "bias" in name:
								__a           =			'''bias'''
						elif "weight" in name:
								# TODO: don't match quantizer.weight_proj
								__a           =			'''weight'''
						else:
								__a           =			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        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Dict:
		__a           =			[]
		__a           =			fairseq_model.state_dict()

		__a           =			hf_model.wavaveca.feature_extractor

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

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

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     List[Any]			) ->      Union[str, Any]:
		__a           =			full_name.split('''conv_layers.'''			)[-1]
		__a           =			name.split('''.'''			)
		__a           =			int(items[0]			)
		__a           =			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           =			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           =			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           =			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           =			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        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     int=None    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     Dict=True    ,					lowerCAmelCase__ :     int=False			) ->      Optional[Any]:
		if config_path is not None:
				__a           =			WavaVecaConfig.from_pretrained(lowerCAmelCase__			)
		else:
				__a           =			WavaVecaConfig()

		if is_seq_class:
				__a           =			read_txt_into_dict(lowerCAmelCase__			)
				__a           =			idalabel
				__a           =			WavaVecaForSequenceClassification(lowerCAmelCase__			)
				__a           =			WavaVecaFeatureExtractor(
				    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
				feature_extractor.save_pretrained(lowerCAmelCase__			)

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

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						__a           =			target_dict.pad_index
						__a           =			target_dict.bos_index
						__a           =			target_dict.eos_index
						__a           =			len(target_dict.symbols			)
						__a           =			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           =			target_dict.indices

						# fairseq has the <pad> and <s> switched
						__a           =			0
						__a           =			1
						with open(lowerCAmelCase__    ,					'''w'''    ,					encoding='''utf-8'''			) as vocab_handle:
								json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)
						__a           =			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           =			True if config.feat_extract_norm == '''layer''' else False
						__a           =			WavaVecaFeatureExtractor(
						    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
						__a           =			WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,					tokenizer=lowerCAmelCase__			)
						processor.save_pretrained(lowerCAmelCase__			)

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

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

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

		__a           =			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,
				)










"""simple docstring"""




import warnings

from ..trainer import Trainer
from ..utils import logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=None							, **_a      ):
				warnings.warn(
				    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
				    '''instead.'''							, _a							, )
				super().__init__(args=_a							, **_a      )






