import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

A__           =							{
    """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""",
    """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""",
}
A__           =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def  _lowerCamelCase					(   a_				: List[str]						,			a_				: List[str]						,			a_				: str						,			a_				: Union[str, Any]						,			a_				: Optional[Any]):
			for attribute in key.split('''.'''):
						lowerCamelCase						:Optional[Any]						  =     getattr(a_						,			a_)

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

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





def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: int):
			lowerCamelCase						:List[str]						  =     []
			lowerCamelCase						:Tuple						  =     fairseq_model.state_dict()

			lowerCamelCase						:Optional[int]						  =     hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase						:Union[str, Any]						  =     None

			for name, value in fairseq_dict.items():
						lowerCamelCase						:List[str]						  =     False
						if "conv_layers" in name:
									load_conv_layer(
									    a_						,			a_						,			a_						,			a_						,			hf_model.config.feat_extract_norm == '''group'''						,			)
									lowerCamelCase						:Optional[int]						  =     True
						elif name.split('''.''')[0] == "proj":
									lowerCamelCase						:Any						  =     fairseq_model.proj
									lowerCamelCase						:int						  =     True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
															lowerCamelCase						:Tuple						  =     True
															if "*" in mapped_key:
																		lowerCamelCase						:str						  =     name.split(a_)[0].split('''.''')[-2]
																		lowerCamelCase						:int						  =     mapped_key.replace('''*'''						,			a_)
															if "weight_g" in name:
																		lowerCamelCase						:Optional[int]						  =     '''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase						:Union[str, Any]						  =     '''weight_v'''
															elif "bias" in name:
																		lowerCamelCase						:Optional[Any]						  =     '''bias'''
															elif "weight" in name:
																		lowerCamelCase						:str						  =     '''weight'''
															else:
																		lowerCamelCase						:List[str]						  =     None
															set_recursively(a_						,			a_						,			a_						,			a_						,			a_)
												continue
						if not is_used:
									unused_weights.append(a_)

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

			return proj_weight





def  _lowerCamelCase					(   a_				: List[Any]						,			a_				: int						,			a_				: Optional[int]						,			a_				: int						,			a_				: Tuple):
			lowerCamelCase						:Any						  =     full_name.split('''conv_layers.''')[-1]
			lowerCamelCase						:Union[str, Any]						  =     name.split('''.''')
			lowerCamelCase						:Dict						  =     int(items[0])
			lowerCamelCase						:Optional[int]						  =     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."
									)
									lowerCamelCase						: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."
									)
									lowerCamelCase						: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."
									)
									lowerCamelCase						:Tuple						  =     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."
									)
									lowerCamelCase						:Dict						  =     value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
			else:
						unused_weights.append(a_)





def  _lowerCamelCase					(   a_				: Tuple):
			lowerCamelCase  ,    lowerCamelCase						:List[str]						  =     emb.weight.shape
			lowerCamelCase						:int						  =     nn.Linear(a_						,			a_						,			bias=a_)
			lowerCamelCase						:Any						  =     emb.weight.data
			return lin_layer





def  _lowerCamelCase					(   a_				: Union[str, Any]):
			with open(a_						,			'''r'''						,			encoding='''utf-8''') as f:
						lowerCamelCase						:Union[str, Any]						  =     f.readlines()
						lowerCamelCase						:Union[str, Any]						  =     [line.split(''' ''')[0] for line in lines]

			lowerCamelCase						:Tuple						  =     len(a_)

			lowerCamelCase						:Optional[Any]						  =     {
			    '''<s>''': 0,
			    '''<pad>''': 1,
			    '''</s>''': 2,
			    '''<unk>''': 3,
			}

			vocab_dict.update(dict(zip(a_						,			range(4						,			num_words + 4))))
			return vocab_dict





@torch.no_grad()
def  _lowerCamelCase					(   a_				: List[Any]						,			a_				: Dict						,			a_				: Optional[int]						,			a_				: str						,			a_				: Tuple						,			a_				: Any						,			a_				: List[str]						,			):
			lowerCamelCase						:Any						  =     WavaVecaConfig.from_pretrained(a_)
			lowerCamelCase						:List[str]						  =     SpeechaTextaConfig.from_pretrained(
			    a_						,			vocab_size=a_						,			decoder_layers=a_						,			do_stable_layer_norm=a_)

			lowerCamelCase						:Optional[int]						  =     WavaVecaFeatureExtractor(
			    feature_size=1						,			sampling_rate=1_60_00						,			padding_value=0						,			do_normalize=a_						,			return_attention_mask=a_						,			)

			lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:Any						  =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]						,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1])})
			lowerCamelCase						:Tuple						  =     model[0].eval()

			# set weights for wav2vec2 encoder
			lowerCamelCase						:Dict						  =     WavaVecaModel(a_)
			lowerCamelCase						:List[Any]						  =     recursively_load_weights_wavaveca(model.encoder						,			a_)

			lowerCamelCase						:int						  =     SpeechaTextaForCausalLM(a_)
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,			strict=a_)

			# set output linear layer
			unexpected_keys.remove('''embed_out''')
			lowerCamelCase						:List[str]						  =     nn.Parameter(model.decoder.embed_out.detach())

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}")
			logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}")

			lowerCamelCase						:Optional[Any]						  =     SpeechEncoderDecoderModel(encoder=a_						,			decoder=a_)
			lowerCamelCase						:Optional[int]						  =     False

			# add projection layer
			lowerCamelCase						:Any						  =     nn.Parameter(projection_layer.weight)
			lowerCamelCase						:str						  =     nn.Parameter(projection_layer.bias)

			lowerCamelCase						:Any						  =     create_vocab_dict(a_)

			with open(os.path.join(a_						,			'''vocab.json''')						,			'''w''') as fp:
						json.dump(a_						,			a_)

			lowerCamelCase						:List[Any]						  =     SpeechaTextaTokenizer(os.path.join(a_						,			'''vocab.json'''))
			tokenizer.save_pretrained(a_)

			lowerCamelCase						:int						  =     hf_wavavec.config.to_dict()
			lowerCamelCase						:Tuple						  =     tokenizer.pad_token_id
			lowerCamelCase						:Tuple						  =     tokenizer.bos_token_id
			lowerCamelCase						:str						  =     tokenizer.eos_token_id
			lowerCamelCase						:str						  =     '''speech_to_text_2'''
			lowerCamelCase						:Any						  =     '''wav2vec2'''

			lowerCamelCase						:Optional[int]						  =     SpeechEncoderDecoderConfig.from_dict(a_)

			hf_wavavec.save_pretrained(a_)
			feature_extractor.save_pretrained(a_)


if __name__ == "__main__":
			A__           =							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(
			    """--encoder_config_path""",
			    default="""facebook/wav2vec2-large-lv60""",
			    type=str,
			    help="""Path to hf encoder wav2vec2 checkpoint config""",
			)
			parser.add_argument(
			    """--decoder_config_path""",
			    default="""facebook/s2t-small-mustc-en-fr-st""",
			    type=str,
			    help="""Path to hf decoder s2t checkpoint config""",
			)
			parser.add_argument("""--vocab_size""", default=10_224, type=int, help="""Vocab size of decoder""")
			parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

			A__           =							parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)




def  _lowerCamelCase					(   a_				: int = 4_00_00_00):
			lowerCamelCase						:Dict						  =     [0, 1]
			lowerCamelCase						:Optional[Any]						  =     0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1])
						if fib[i + 2] > n:
									break
						i += 1
			lowerCamelCase						:Dict						  =     0
			for j in range(len(a_) - 1):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


if __name__ == "__main__":
			print(F'{solution() = }')



