


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()
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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		for attribute in key.split('''.'''	):
				lowercase__				= getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		if weight_type is not None:
				lowercase__				= getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	).shape
		else:
				lowercase__				= 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__				= value
		elif weight_type == "weight_g":
				lowercase__				= value
		elif weight_type == "weight_v":
				lowercase__				= value
		elif weight_type == "bias":
				lowercase__				= value
		else:
				lowercase__				= value

		logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'	)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= []
		lowercase__				= fairseq_model.state_dict()

		lowercase__				= hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		lowercase__				= None

		for name, value in fairseq_dict.items():
				lowercase__				= 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'''		,							)
						lowercase__				= True
				elif name.split('''.'''	)[0] == "proj":
						lowercase__				= fairseq_model.proj
						lowercase__				= True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
										lowercase__				= True
										if "*" in mapped_key:
												lowercase__				= name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
												lowercase__				= mapped_key.replace('''*'''		,							SCREAMING_SNAKE_CASE	)
										if "weight_g" in name:
												lowercase__				= '''weight_g'''
										elif "weight_v" in name:
												lowercase__				= '''weight_v'''
										elif "bias" in name:
												lowercase__				= '''bias'''
										elif "weight" in name:
												lowercase__				= '''weight'''
										else:
												lowercase__				= 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}'	)

		return proj_weight
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= full_name.split('''conv_layers.'''	)[-1]
		lowercase__				= name.split('''.'''	)
		lowercase__				= int(items[0]	)
		lowercase__				= 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__				= 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__				= 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__				= 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__				= 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      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__	,						lowercase__				= emb.weight.shape
		lowercase__				= nn.Linear(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							bias=SCREAMING_SNAKE_CASE	)
		lowercase__				= emb.weight.data
		return lin_layer
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE		,							'''r'''		,							encoding='''utf-8'''	) as f:
				lowercase__				= f.readlines()
				lowercase__				= [line.split(''' '''	)[0] for line in lines]

		lowercase__				= len(SCREAMING_SNAKE_CASE	)

		lowercase__				= {
		    '''<s>''': 0,
		    '''<pad>''': 1,
		    '''</s>''': 2,
		    '''<unk>''': 3,
		}

		vocab_dict.update(dict(zip(SCREAMING_SNAKE_CASE		,							range(4		,							num_words + 4	)	)	)	)
		return vocab_dict
@torch.no_grad()
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							):


		"""simple docstring"""




		lowercase__				= WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
		lowercase__				= SpeechaTextaConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE		,							vocab_size=SCREAMING_SNAKE_CASE		,							decoder_layers=SCREAMING_SNAKE_CASE		,							do_stable_layer_norm=SCREAMING_SNAKE_CASE	)

		lowercase__				= WavaVecaFeatureExtractor(
		    feature_size=1		,							sampling_rate=1_60_00		,							padding_value=0		,							do_normalize=SCREAMING_SNAKE_CASE		,							return_attention_mask=SCREAMING_SNAKE_CASE		,							)

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

		# set weights for wav2vec2 encoder
		lowercase__				= WavaVecaModel(SCREAMING_SNAKE_CASE	)
		lowercase__				= recursively_load_weights_wavaveca(model.encoder		,							SCREAMING_SNAKE_CASE	)

		lowercase__				= SpeechaTextaForCausalLM(SCREAMING_SNAKE_CASE	)
		lowercase__	,						lowercase__				= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,							strict=SCREAMING_SNAKE_CASE	)

		# set output linear layer
		unexpected_keys.remove('''embed_out'''	)
		lowercase__				= 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}'	)

		lowercase__				= SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE		,							decoder=SCREAMING_SNAKE_CASE	)
		lowercase__				= False

		# add projection layer
		lowercase__				= nn.Parameter(projection_layer.weight	)
		lowercase__				= nn.Parameter(projection_layer.bias	)

		lowercase__				= create_vocab_dict(SCREAMING_SNAKE_CASE	)

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

		lowercase__				= SpeechaTextaTokenizer(os.path.join(SCREAMING_SNAKE_CASE		,							'''vocab.json'''	)	)
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE	)

		lowercase__				= hf_wavavec.config.to_dict()
		lowercase__				= tokenizer.pad_token_id
		lowercase__				= tokenizer.bos_token_id
		lowercase__				= tokenizer.eos_token_id
		lowercase__				= '''speech_to_text_2'''
		lowercase__				= '''wav2vec2'''

		lowercase__				= 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(
			    '--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=1_0224, type=int, help='Vocab size of decoder')
			parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

			lowerCAmelCase			=				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,
			)









'''simple docstring'''






import argparse

import torch

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


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

__UpperCamelCase					    =	[
    ["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 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

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


if __name__ == "__main__":
	__UpperCamelCase					    =	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"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


