




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()
UpperCAmelCase_		:				Dict									=     logging.get_logger(__name__)

UpperCAmelCase_		:				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",
}
UpperCAmelCase_		:				str									=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				for attribute in key.split("""."""   ):
								__magic_name__	:	List[str]								=getattr(lowerCamelCase	,     lowerCamelCase   )

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

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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	int								=[]
				__magic_name__	:	Tuple								=fairseq_model.state_dict()

				__magic_name__	:	Optional[int]								=hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				__magic_name__	:	Dict								=None

				for name, value in fairseq_dict.items():
								__magic_name__	:	Optional[int]								=False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     hf_model.config.feat_extract_norm == """group"""	,     )
												__magic_name__	:	List[str]								=True
								elif name.split("""."""   )[0] == "proj":
												__magic_name__	:	List[Any]								=fairseq_model.proj
												__magic_name__	:	int								=True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
																				__magic_name__	:	List[str]								=True
																				if "*" in mapped_key:
																								__magic_name__	:	str								=name.split(lowerCamelCase   )[0].split("""."""   )[-2]
																								__magic_name__	:	List[Any]								=mapped_key.replace("""*"""	,     lowerCamelCase   )
																				if "weight_g" in name:
																								__magic_name__	:	str								="""weight_g"""
																				elif "weight_v" in name:
																								__magic_name__	:	str								="""weight_v"""
																				elif "bias" in name:
																								__magic_name__	:	int								="""bias"""
																				elif "weight" in name:
																								__magic_name__	:	List[Any]								="""weight"""
																				else:
																								__magic_name__	:	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}"   )

				return proj_weight




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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__				, __magic_name__	:	Dict								=emb.weight.shape
				__magic_name__	:	List[str]								=nn.Linear(lowerCamelCase	,     lowerCamelCase	,     bias=lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=emb.weight.data
				return lin_layer




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								__magic_name__	:	Any								=f.readlines()
								__magic_name__	:	Any								=[line.split(""" """   )[0] for line in lines]

				__magic_name__	:	int								=len(lowerCamelCase   )

				__magic_name__	:	List[str]								={
				    """<s>""": 0,
				    """<pad>""": 1,
				    """</s>""": 2,
				    """<unk>""": 3,
				}

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




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     ):
				__magic_name__	:	int								=WavaVecaConfig.from_pretrained(lowerCamelCase   )
				__magic_name__	:	Any								=SpeechaTextaConfig.from_pretrained(
				    lowerCamelCase	,     vocab_size=lowerCamelCase	,     decoder_layers=lowerCamelCase	,     do_stable_layer_norm=lowerCamelCase   )

				__magic_name__	:	Optional[Any]								=WavaVecaFeatureExtractor(
				    feature_size=1	,     sampling_rate=16000	,     padding_value=0	,     do_normalize=lowerCamelCase	,     return_attention_mask=lowerCamelCase	,     )

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

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

				__magic_name__	:	Dict								=SpeechaTextaForCausalLM(lowerCamelCase   )
				__magic_name__				, __magic_name__	:	Tuple								=hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,     strict=lowerCamelCase   )

				# set output linear layer
				unexpected_keys.remove("""embed_out"""   )
				__magic_name__	:	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}"   )

				__magic_name__	:	Optional[int]								=SpeechEncoderDecoderModel(encoder=lowerCamelCase	,     decoder=lowerCamelCase   )
				__magic_name__	:	str								=False

				# add projection layer
				__magic_name__	:	Tuple								=nn.Parameter(projection_layer.weight   )
				__magic_name__	:	List[Any]								=nn.Parameter(projection_layer.bias   )

				__magic_name__	:	Optional[Any]								=create_vocab_dict(lowerCamelCase   )

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

				__magic_name__	:	str								=SpeechaTextaTokenizer(os.path.join(lowerCamelCase	,     """vocab.json"""   )   )
				tokenizer.save_pretrained(lowerCamelCase   )

				__magic_name__	:	Optional[int]								=hf_wavavec.config.to_dict()
				__magic_name__	:	Dict								=tokenizer.pad_token_id
				__magic_name__	:	str								=tokenizer.bos_token_id
				__magic_name__	:	List[str]								=tokenizer.eos_token_id
				__magic_name__	:	Union[str, Any]								="""speech_to_text_2"""
				__magic_name__	:	Tuple								="""wav2vec2"""

				__magic_name__	:	Optional[Any]								=SpeechEncoderDecoderConfig.from_dict(lowerCamelCase   )

				hf_wavavec.save_pretrained(lowerCamelCase   )
				feature_extractor.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Any									=     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=10224, type=int, help="Vocab size of decoder")
							parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

							UpperCAmelCase_		:				List[str]									=     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,
							)










# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase_		:				str									=     "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
							import torch

							print("Torch version:", torch.__version__)
							print("Cuda available:", torch.cuda.is_available())
							print("Cuda version:", torch.version.cuda)
							print("CuDNN version:", torch.backends.cudnn.version())
							print("Number of GPUs available:", torch.cuda.device_count())
							print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
							print("Torch version:", None)

try:
							import deepspeed

							print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
							print("DeepSpeed version:", None)

try:
							import tensorflow as tf

							print("TensorFlow version:", tf.__version__)
							print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
							print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
							print("TensorFlow version:", None)




