


import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   List[Any]							      =				{
    "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",
}
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    for attribute in key.split('.'				):
        _lowercase :							int   	=	getattr(lowerCamelCase_							,				lowerCamelCase_				)

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

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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							Optional[int]   	=	[]
    _lowercase :							Optional[int]   	=	fairseq_model.state_dict()

    _lowercase :							int   	=	hf_model.feature_extractor
    _lowercase :							Tuple   	=	hf_model.adapter

    for name, value in fairseq_dict.items():
        _lowercase :							List[str]   	=	False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				hf_model.config.feat_extract_norm == 'group'							,				)
            _lowercase :							str   	=	True
        elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']				):
            load_adapter(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Tuple   	=	True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
                    _lowercase :							str   	=	True
                    if "*" in mapped_key:
                        _lowercase :							Tuple   	=	name.split(lowerCamelCase_				)[0].split('.'				)[-2]
                        _lowercase :							Dict   	=	mapped_key.replace('*'							,				lowerCamelCase_				)
                    if "weight_g" in name:
                        _lowercase :							Optional[int]   	=	'weight_g'
                    elif "weight_v" in name:
                        _lowercase :							Optional[int]   	=	'weight_v'
                    elif "bias" in name:
                        _lowercase :							List[Any]   	=	'bias'
                    elif "weight" in name:
                        _lowercase :							List[Any]   	=	'weight'
                    else:
                        _lowercase :							Union[str, Any]   	=	None
                    set_recursively(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
                continue
        if not is_used:
            unused_weights.append(lowerCamelCase_				)

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



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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							Tuple   	=	full_name.split('adaptor.'				)[-1]
    _lowercase :							Any   	=	name.split('.'				)

    if items[1].isdigit():
        _lowercase :							Tuple   	=	int(items[1]				)
    else:
        _lowercase :							List[str]   	=	None

    if "adaptor" not in full_name:
        if "proj_ln" in full_name:
            # has to be layer norm
            if "bias" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.bias.data.shape
                ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                _lowercase :							Tuple   	=	value
                logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''				)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.weight.data.shape
                ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                _lowercase :							Optional[int]   	=	value
        else:
            # has to be projection layer
            if "bias" in name:
                assert (
                    value.shape == adapter.proj.bias.data.shape
                ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                _lowercase :							Optional[Any]   	=	value
                logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''				)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj.weight.data.shape
                ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                _lowercase :							List[str]   	=	value
                logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''				)
    elif isinstance(lowerCamelCase_							,				lowerCamelCase_				):
        if "bias" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.bias.data.shape
            ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
            _lowercase :							Dict   	=	value
            logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''				)
        elif "weight" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.weight.data.shape
            ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
            _lowercase :							Dict   	=	value
            logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''				)
    else:
        unused_weights.append(lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[Any]:
    _lowercase	,       _lowercase :							Dict   	=	emb.weight.shape
    _lowercase :							Dict   	=	nn.Linear(lowerCamelCase_							,				lowerCamelCase_							,				bias=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	emb.weight.data
    return lin_layer



@torch.no_grad()
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				)					->		List[str]:
    _lowercase :							List[str]   	=	WavaVecaConfig.from_pretrained(
        lowerCamelCase_							,				add_adapter=lowerCamelCase_							,				adapter_stride=lowerCamelCase_							,				adapter_kernel_size=lowerCamelCase_							,				use_auth_token=lowerCamelCase_							,				output_hidden_size=lowerCamelCase_							,				)
    _lowercase :							Any   	=	MBartConfig.from_pretrained(lowerCamelCase_				)

    # load model
    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]							,				arg_overrides={
            'config_yaml': config_yaml_path,
            'data': '/'.join(dict_path.split('/'				)[:-1]				),
            'w2v_path': checkpoint_path,
            'load_pretrained_decoder_from': None,
        }							,				)
    _lowercase :							int   	=	model[0].eval()

    # load feature extractor
    _lowercase :							Optional[Any]   	=	WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_							,				use_auth_token=lowerCamelCase_				)

    # set weights for wav2vec2 encoder
    _lowercase :							Optional[Any]   	=	WavaVecaModel(lowerCamelCase_				)

    recursively_load_weights_wavaveca(model.encoder							,				lowerCamelCase_				)

    # load decoder weights
    _lowercase :							List[Any]   	=	MBartForCausalLM(lowerCamelCase_				)
    _lowercase	,       _lowercase :							Tuple   	=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,				strict=lowerCamelCase_				)
    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}'''				)

    _lowercase :							Dict   	=	SpeechEncoderDecoderModel(encoder=lowerCamelCase_							,				decoder=lowerCamelCase_				)
    _lowercase :							Any   	=	False

    _lowercase :							List[Any]   	=	MBartaaTokenizer(lowerCamelCase_				)
    tokenizer.save_pretrained(lowerCamelCase_				)

    _lowercase :							Any   	=	hf_wavavec.config.to_dict()
    _lowercase :							Optional[Any]   	=	tokenizer.pad_token_id
    _lowercase :							Union[str, Any]   	=	tokenizer.bos_token_id
    _lowercase :							str   	=	tokenizer.eos_token_id
    _lowercase :							Optional[Any]   	=	'mbart50'
    _lowercase :							Union[str, Any]   	=	'wav2vec2'

    _lowercase :							Any   	=	tokenizer.eos_token_id
    _lowercase :							Any   	=	25_0004
    _lowercase :							str   	=	tokenizer.eos_token_id

    _lowercase :							Tuple   	=	SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_				)

    hf_wavavec.save_pretrained(lowerCamelCase_				)
    feature_extractor.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   str							      =				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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
 parser.add_argument(
     "--encoder_config_path",
     default="facebook/wav2vec2-xls-r-1b",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/mbart-large-50-one-to-many-mmt",
     type=str,
     help="Path to hf decoder checkpoint config",
 )
 parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
 parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
 parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
 parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
 parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config")

 SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )






from __future__ import annotations

import requests



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict:
    _lowercase :							Dict   	=	F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
    return requests.get(lowerCamelCase_				).json()



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		list[dict]:
    _lowercase :							Dict   	=	'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
    _lowercase :							Dict   	=	requests.get(lowerCamelCase_				).json()[:max_stories]
    return [get_hackernews_story(lowerCamelCase_				) for story_id in story_ids]



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		str:
    _lowercase :							Optional[int]   	=	hackernews_top_stories(lowerCamelCase_				)
    return "\n".join('* [{title}]({url})'.format(**lowerCamelCase_				) for story in stories				)


if __name__ == "__main__":
 print(hackernews_top_stories_as_markdown())


