

'''simple docstring'''


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()
lowerCAmelCase__											= logging.get_logger(__name__)

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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[str]:
	for attribute in key.split('.'  ):
		UpperCAmelCase_		:      int       	=					getattr(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE  )

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

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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Any:
	UpperCAmelCase_		:      List[str]       	=					[]
	UpperCAmelCase_		:      List[Any]       	=					fairseq_model.state_dict()

	UpperCAmelCase_		:      Optional[Any]       	=					hf_model.feature_extractor
	UpperCAmelCase_		:      Any       	=					hf_model.adapter

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

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







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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      Union[str, Any]       	=					full_name.split('adaptor.'  )[-1]
	UpperCAmelCase_		:      Tuple       	=					name.split('.'  )

	if items[1].isdigit():
		UpperCAmelCase_		:      Any       	=					int(items[1]  )
	else:
		UpperCAmelCase_		:      Optional[int]       	=					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."""
				UpperCAmelCase_		:      Any       	=					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."""
				UpperCAmelCase_		:      Optional[Any]       	=					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."""
				UpperCAmelCase_		:      Optional[int]       	=					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."""
				UpperCAmelCase_		:      List[Any]       	=					value
				logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
	elif isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE  ):
		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."""
			UpperCAmelCase_		:      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."""
			UpperCAmelCase_		:      Dict       	=					value
			logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
	else:
		unused_weights.append(_SCREAMING_SNAKE_CASE  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					emb.weight.shape
	UpperCAmelCase_		:      str       	=					nn.Linear(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,bias=_SCREAMING_SNAKE_CASE  )
	UpperCAmelCase_		:      Optional[int]       	=					emb.weight.data
	return lin_layer







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,)    ->    List[str]:
	UpperCAmelCase_		:      Optional[int]       	=					WavaVecaConfig.from_pretrained(
	    _SCREAMING_SNAKE_CASE   ,add_adapter=_SCREAMING_SNAKE_CASE   ,adapter_stride=_SCREAMING_SNAKE_CASE   ,adapter_kernel_size=_SCREAMING_SNAKE_CASE   ,use_auth_token=_SCREAMING_SNAKE_CASE   ,output_hidden_size=_SCREAMING_SNAKE_CASE   ,)
	UpperCAmelCase_		:      int       	=					MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )

	# load model
	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					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,
	    }   ,)
	UpperCAmelCase_		:      int       	=					model[0].eval()

	# load feature extractor
	UpperCAmelCase_		:      str       	=					WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE   ,use_auth_token=_SCREAMING_SNAKE_CASE  )

	# set weights for wav2vec2 encoder
	UpperCAmelCase_		:      List[Any]       	=					WavaVecaModel(_SCREAMING_SNAKE_CASE  )

	recursively_load_weights_wavaveca(model.encoder   ,_SCREAMING_SNAKE_CASE  )

	# load decoder weights
	UpperCAmelCase_		:      List[str]       	=					MBartForCausalLM(_SCREAMING_SNAKE_CASE  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,strict=_SCREAMING_SNAKE_CASE  )
	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}"""  )

	UpperCAmelCase_		:      str       	=					SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE   ,decoder=_SCREAMING_SNAKE_CASE  )
	UpperCAmelCase_		:      Optional[int]       	=					False

	UpperCAmelCase_		:      Union[str, Any]       	=					MBartaaTokenizer(_SCREAMING_SNAKE_CASE  )
	tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE  )

	UpperCAmelCase_		:      Dict       	=					hf_wavavec.config.to_dict()
	UpperCAmelCase_		:      List[Any]       	=					tokenizer.pad_token_id
	UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.bos_token_id
	UpperCAmelCase_		:      List[Any]       	=					tokenizer.eos_token_id
	UpperCAmelCase_		:      int       	=					'mbart50'
	UpperCAmelCase_		:      str       	=					'wav2vec2'

	UpperCAmelCase_		:      Any       	=					tokenizer.eos_token_id
	UpperCAmelCase_		:      Tuple       	=					2_5_0_0_0_4
	UpperCAmelCase_		:      Dict       	=					tokenizer.eos_token_id

	UpperCAmelCase_		:      Any       	=					SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE  )

	hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE  )
	feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
						lowerCAmelCase__											= 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")

						lowerCAmelCase__											= 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,
						)









'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

lowerCAmelCase__										= [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase  )
	m.model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	m.half()
	m.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
						parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
						parser.add_argument(
						    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






