




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()
lowercase__				:     str 		=						logging.get_logger(__name__)

lowercase__				:     Union[str, 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''',
}
lowercase__				:     List[Any] 		=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	for attribute in key.split("."							):
		snake_case_		    =     getattr(A_     ,	A_							)

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

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


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     fairseq_model.state_dict()

	snake_case_		    =     hf_model.feature_extractor

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

	for name, value in fairseq_dict.items():
		snake_case_		    =     False
		if "conv_layers" in name:
			load_conv_layer(
			    A_     ,	A_     ,	A_     ,	A_     ,	hf_model.config.feat_extract_norm == "group"     ,	)
			snake_case_		    =     True
		elif name.split("."							)[0] == "proj":
			snake_case_		    =     fairseq_model.proj
			snake_case_		    =     True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
					snake_case_		    =     True
					if "*" in mapped_key:
						snake_case_		    =     name.split(A_							)[0].split("."							)[-2]
						snake_case_		    =     mapped_key.replace("*"     ,	A_							)
					if "weight_g" in name:
						snake_case_		    =     '''weight_g'''
					elif "weight_v" in name:
						snake_case_		    =     '''weight_v'''
					elif "bias" in name:
						snake_case_		    =     '''bias'''
					elif "weight" in name:
						snake_case_		    =     '''weight'''
					else:
						snake_case_		    =     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     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     full_name.split("conv_layers."							)[-1]
	snake_case_		    =     name.split("."							)
	snake_case_		    =     int(items[0]							)
	snake_case_		    =     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."
			)
			snake_case_		    =     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."
			)
			snake_case_		    =     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."
			)
			snake_case_		    =     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."
			)
			snake_case_		    =     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							):







	'''simple docstring'''





	snake_case_		    =     emb.weight.shape
	snake_case_		    =     nn.Linear(A_     ,	A_     ,	bias=A_							)
	snake_case_		    =     emb.weight.data
	return lin_layer


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	snake_case_		    =     len(A_							)

	snake_case_		    =     {
	    '''<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     ,	_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	):







	'''simple docstring'''





	snake_case_		    =     WavaVecaConfig.from_pretrained(A_							)
	snake_case_		    =     SpeechaTextaConfig.from_pretrained(
	    A_     ,	vocab_size=A_     ,	decoder_layers=A_     ,	do_stable_layer_norm=A_							)

	snake_case_		    =     WavaVecaFeatureExtractor(
	    feature_size=1     ,	sampling_rate=16000     ,	padding_value=0     ,	do_normalize=A_     ,	return_attention_mask=A_     ,	)

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

	# set weights for wav2vec2 encoder
	snake_case_		    =     WavaVecaModel(A_							)
	snake_case_		    =     recursively_load_weights_wavaveca(model.encoder     ,	A_							)

	snake_case_		    =     SpeechaTextaForCausalLM(A_							)
	snake_case_		    =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,	strict=A_							)

	# set output linear layer
	unexpected_keys.remove("embed_out"							)
	snake_case_		    =     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}"							)

	snake_case_		    =     SpeechEncoderDecoderModel(encoder=A_     ,	decoder=A_							)
	snake_case_		    =     False

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

	snake_case_		    =     create_vocab_dict(A_							)

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

	snake_case_		    =     SpeechaTextaTokenizer(os.path.join(A_     ,	"vocab.json"							)							)
	tokenizer.save_pretrained(A_							)

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

	snake_case_		    =     SpeechEncoderDecoderConfig.from_dict(A_							)

	hf_wavavec.save_pretrained(A_							)
	feature_extractor.save_pretrained(A_							)


if __name__ == "__main__":
		lowercase__				:     Dict 		=						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")

		lowercase__				:     List[Any] 		=						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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




