






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      :       Optional[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Tuple    =						{
    '''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      :       Optional[Any]    =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     for attribute in key.split("."							):
          __lowerCAmelCase             =       getattr(__snake_case       ,			__snake_case							)

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

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       []
     __lowerCAmelCase             =       fairseq_model.state_dict()

     __lowerCAmelCase             =       hf_model.feature_extractor

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

     for name, value in fairseq_dict.items():
          __lowerCAmelCase             =       False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			hf_model.config.feat_extract_norm == "group"       ,			)
               __lowerCAmelCase             =       True
          elif name.split("."							)[0] == "proj":
               __lowerCAmelCase             =       fairseq_model.proj
               __lowerCAmelCase             =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
                         __lowerCAmelCase             =       True
                         if "*" in mapped_key:
                              __lowerCAmelCase             =       name.split(__snake_case							)[0].split("."							)[-2]
                              __lowerCAmelCase             =       mapped_key.replace("*"       ,			__snake_case							)
                         if "weight_g" in name:
                              __lowerCAmelCase             =       "weight_g"
                         elif "weight_v" in name:
                              __lowerCAmelCase             =       "weight_v"
                         elif "bias" in name:
                              __lowerCAmelCase             =       "bias"
                         elif "weight" in name:
                              __lowerCAmelCase             =       "weight"
                         else:
                              __lowerCAmelCase             =       None
                         set_recursively(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
                    continue
          if not is_used:
               unused_weights.append(__snake_case							)

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

     return proj_weight





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       full_name.split("conv_layers."							)[-1]
     __lowerCAmelCase             =       name.split("."							)
     __lowerCAmelCase             =       int(items[0]							)
     __lowerCAmelCase             =       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."""
               )
               __lowerCAmelCase             =       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."""
               )
               __lowerCAmelCase             =       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."
               )
               __lowerCAmelCase             =       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."""
               )
               __lowerCAmelCase             =       value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
     else:
          unused_weights.append(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase      ,			__lowerCAmelCase             =       emb.weight.shape
     __lowerCAmelCase             =       nn.Linear(__snake_case       ,			__snake_case       ,			bias=__snake_case							)
     __lowerCAmelCase             =       emb.weight.data
     return lin_layer





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

     __lowerCAmelCase             =       len(__snake_case							)

     __lowerCAmelCase             =       {
         "<s>": 0,
         "<pad>": 1,
         "</s>": 2,
         "<unk>": 3,
     }

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





@torch.no_grad()
def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			):
     __lowerCAmelCase             =       WavaVecaConfig.from_pretrained(__snake_case							)
     __lowerCAmelCase             =       SpeechaTextaConfig.from_pretrained(
         __snake_case       ,			vocab_size=__snake_case       ,			decoder_layers=__snake_case       ,			do_stable_layer_norm=__snake_case							)

     __lowerCAmelCase             =       WavaVecaFeatureExtractor(
         feature_size=1       ,			sampling_rate=1_6000       ,			padding_value=0       ,			do_normalize=__snake_case       ,			return_attention_mask=__snake_case       ,			)

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

     # set weights for wav2vec2 encoder
     __lowerCAmelCase             =       WavaVecaModel(__snake_case							)
     __lowerCAmelCase             =       recursively_load_weights_wavaveca(model.encoder       ,			__snake_case							)

     __lowerCAmelCase             =       SpeechaTextaForCausalLM(__snake_case							)
     __lowerCAmelCase      ,			__lowerCAmelCase             =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,			strict=__snake_case							)

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

     __lowerCAmelCase             =       SpeechEncoderDecoderModel(encoder=__snake_case       ,			decoder=__snake_case							)
     __lowerCAmelCase             =       False

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

     __lowerCAmelCase             =       create_vocab_dict(__snake_case							)

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

     __lowerCAmelCase             =       SpeechaTextaTokenizer(os.path.join(__snake_case       ,			"vocab.json"							)							)
     tokenizer.save_pretrained(__snake_case							)

     __lowerCAmelCase             =       hf_wavavec.config.to_dict()
     __lowerCAmelCase             =       tokenizer.pad_token_id
     __lowerCAmelCase             =       tokenizer.bos_token_id
     __lowerCAmelCase             =       tokenizer.eos_token_id
     __lowerCAmelCase             =       "speech_to_text_2"
     __lowerCAmelCase             =       "wav2vec2"

     __lowerCAmelCase             =       SpeechEncoderDecoderConfig.from_dict(__snake_case							)

     hf_wavavec.save_pretrained(__snake_case							)
     feature_extractor.save_pretrained(__snake_case							)


if __name__ == "__main__":
 lowerCamelCase      :       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''')

 lowerCamelCase      :       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,
 )














import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






