


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_   :				List[Any]						      = logging.get_logger(__name__)

UpperCAmelCase_   :				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',
}
UpperCAmelCase_   :				int						      = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    List[str]						,					__A				:    Any						,					__A				:    List[str]						,					__A				:    Any       )			->	Any:

       """simple docstring"""

       for attribute in key.split('.'       ):
              a_      :	List[str]							=						getattr(__A						,					__A       )

       if weight_type is not None:
              a_      :	Union[str, Any]							=						getattr(__A						,					__A       ).shape
       else:
              a_      :	int							=						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":
              a_      :	Any							=						value
       elif weight_type == "weight_g":
              a_      :	int							=						value
       elif weight_type == "weight_v":
              a_      :	str							=						value
       elif weight_type == "bias":
              a_      :	Tuple							=						value
       else:
              a_      :	str							=						value

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    int       )			->	Dict:

       """simple docstring"""

       a_      :	int							=						[]
       a_      :	int							=						fairseq_model.state_dict()

       a_      :	List[str]							=						hf_model.feature_extractor

       # if encoder has different dim to decoder -> use proj_weight
       a_      :	Tuple							=						None

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

       """simple docstring"""

       a_      :	str							=						full_name.split('conv_layers.'       )[-1]
       a_      :	Optional[int]							=						name.split('.'       )
       a_      :	str							=						int(items[0]       )
       a_      :	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."""
                     )
                     a_      :	str							=						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."""
                     )
                     a_      :	List[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."
                     )
                     a_      :	int							=						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."""
                     )
                     a_      :	Union[str, Any]							=						value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
       else:
              unused_weights.append(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_							,	a_      :	List[Any]							=						emb.weight.shape
       a_      :	Optional[int]							=						nn.Linear(__A						,					__A						,					bias=__A       )
       a_      :	Tuple							=						emb.weight.data
       return lin_layer







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Optional[Any]:

       """simple docstring"""

       with open(__A						,					'r'						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.readlines()
              a_      :	Tuple							=						[line.split(' '       )[0] for line in lines]

       a_      :	Optional[Any]							=						len(__A       )

       a_      :	Union[str, Any]							=						{
           '<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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    Optional[int]						,					__A				:    List[str]						,					__A				:    Optional[int]						,					__A				:    Any						,					__A				:    str						,					)			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						WavaVecaConfig.from_pretrained(__A       )
       a_      :	Tuple							=						SpeechaTextaConfig.from_pretrained(
           __A						,					vocab_size=__A						,					decoder_layers=__A						,					do_stable_layer_norm=__A       )

       a_      :	List[str]							=						WavaVecaFeatureExtractor(
           feature_size=1						,					sampling_rate=1_60_00						,					padding_value=0						,					do_normalize=__A						,					return_attention_mask=__A						,					)

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

       # set weights for wav2vec2 encoder
       a_      :	Optional[Any]							=						WavaVecaModel(__A       )
       a_      :	Union[str, Any]							=						recursively_load_weights_wavaveca(model.encoder						,					__A       )

       a_      :	Optional[int]							=						SpeechaTextaForCausalLM(__A       )
       a_							,	a_      :	Tuple							=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,					strict=__A       )

       # set output linear layer
       unexpected_keys.remove('embed_out'       )
       a_      :	Any							=						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}"""       )

       a_      :	Tuple							=						SpeechEncoderDecoderModel(encoder=__A						,					decoder=__A       )
       a_      :	Optional[Any]							=						False

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

       a_      :	int							=						create_vocab_dict(__A       )

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

       a_      :	Any							=						SpeechaTextaTokenizer(os.path.join(__A						,					'vocab.json'       )       )
       tokenizer.save_pretrained(__A       )

       a_      :	Optional[int]							=						hf_wavavec.config.to_dict()
       a_      :	Dict							=						tokenizer.pad_token_id
       a_      :	Optional[Any]							=						tokenizer.bos_token_id
       a_      :	Dict							=						tokenizer.eos_token_id
       a_      :	Any							=						'speech_to_text_2'
       a_      :	List[str]							=						'wav2vec2'

       a_      :	str							=						SpeechEncoderDecoderConfig.from_dict(__A       )

       hf_wavavec.save_pretrained(__A       )
       feature_extractor.save_pretrained(__A       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Optional[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=1_0224, type=int, help='Vocab size of decoder')
   parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

   UpperCAmelCase_   :				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,
   )






from argparse import ArgumentParser

from .env import EnvironmentCommand







def  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	str							=						ArgumentParser('Diffusers CLI tool'						,					usage='diffusers-cli <command> [<args>]'       )
       a_      :	Any							=						parser.add_subparsers(help='diffusers-cli command helpers'       )

       # Register commands
       EnvironmentCommand.register_subcommand(__A       )

       # Let's go
       a_      :	int							=						parser.parse_args()

       if not hasattr(__A						,					'func'       ):
              parser.print_help()
              exit(1       )

       # Run
       a_      :	Union[str, Any]							=						args.func(__A       )
       service.run()


if __name__ == "__main__":
   main()


