
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase__     :		List[Any]        = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCamelCase__     :		Dict        = {
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
lowerCamelCase__     :		str        = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     lowerCamelCase__     :		List[str]        = """facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
     lowerCamelCase__     :		Optional[Any]        = """allenai"""

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Any     		=   dict((re.sub(R"""@@$"""			,			""""""			,			lowercase_ ), v) if k.endswith("""@@""" ) else (re.sub(R"""$"""			,			"""</w>"""			,			lowercase_ ), v) for k, v in d.items() )
  lowercase__	: List[str]     		=   """<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'{k}</w>']
    lowercase__	: int     		=   d[k]  # restore
  return da

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  assert os.path.exists(lowercase_ )
  os.makedirs(lowercase_			,			exist_ok=lowercase_ )
  print(F'Writing results to {pytorch_dump_folder_path}' )

  # handle various types of models

  lowercase__	: int     		=   basename(lowercase_ )
  lowercase__	: List[Any]     		=   dirname(lowercase_ )

  lowercase__	: List[str]     		=   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  lowercase__	: int     		=   cls.hub_models()
  lowercase__	: Tuple     		=   {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
  lowercase__	: Optional[Any]     		=   """."""
  # note: since the model dump is old, fairseq has upgraded its model some
  # time later, and it does a whole lot of rewrites and splits on the saved
  # weights, therefore we can't use torch.load() directly on the model file.
  # see: upgrade_state_dict(state_dict) in fairseq_model.py
  print(F'using checkpoint {checkpoint_file}' )
  lowercase__	: Optional[int]     		=   hub_utils.from_pretrained(
      lowercase_			,			lowercase_			,			lowercase_			,			archive_map=lowercase_			,			**lowercase_ )

  lowercase__	: Tuple     		=   vars(chkpt["""args"""]["""model"""] )

  lowercase__	: List[Any]     		=   args["""source_lang"""]
  lowercase__	: Dict     		=   args["""target_lang"""]

  lowercase__	: Optional[Any]     		=   dirname(lowercase_ )
  lowercase__	: int     		=   basename(lowercase_ )

  # dicts
  lowercase__	: Union[str, Any]     		=   os.path.join(lowercase_			,			F'dict.{src_lang}.txt' )
  lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			F'dict.{tgt_lang}.txt' )

  lowercase__	: Optional[int]     		=   Dictionary.load(lowercase_ )
  lowercase__	: Union[str, Any]     		=   rewrite_dict_keys(src_dict.indices )
  lowercase__	: str     		=   len(lowercase_ )
  lowercase__	: Any     		=   os.path.join(lowercase_			,			"""vocab-src.json""" )
  print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # detect whether this is a do_lower_case situation, which can be derived by checking whether we
  # have at least one uppercase letter in the source vocab
  lowercase__	: Any     		=   True
  for k in src_vocab.keys():
    if not k.islower():
      lowercase__	: List[str]     		=   False
      break

  lowercase__	: Tuple     		=   Dictionary.load(lowercase_ )
  lowercase__	: Any     		=   rewrite_dict_keys(tgt_dict.indices )
  lowercase__	: Any     		=   len(lowercase_ )
  lowercase__	: Any     		=   os.path.join(lowercase_			,			"""vocab-tgt.json""" )
  print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # merges_file (bpecodes)
  lowercase__	: Union[str, Any]     		=   os.path.join(lowercase_			,			VOCAB_FILES_NAMES["""merges_file"""] )
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			lowercase_ )
    if os.path.exists(lowercase_ ):
      break
  with open(lowercase_			,			encoding="""utf-8""" ) as fin:
    lowercase__	: List[Any]     		=   fin.read()
  lowercase__	: Optional[Any]     		=   re.sub(R""" \d+$"""			,			""""""			,			lowercase_			,			0			,			re.M )  # remove frequency number
  print(F'Generating {merges_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as fout:
    fout.write(lowercase_ )

  # model config
  lowercase__	: Tuple     		=   os.path.join(lowercase_			,			"""config.json""" )

  # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
  # may have to modify the tokenizer if a different type is used by a future model
  assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
  assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

  lowercase__	: List[str]     		=   {
      """architectures""": ["""FSMTForConditionalGeneration"""],
      """model_type""": """fsmt""",
      """activation_dropout""": args["""activation_dropout"""],
      """activation_function""": """relu""",
      """attention_dropout""": args["""attention_dropout"""],
      """d_model""": args["""decoder_embed_dim"""],
      """dropout""": args["""dropout"""],
      """init_std""": 0.02,
      """max_position_embeddings""": args["""max_source_positions"""],
      """num_hidden_layers""": args["""encoder_layers"""],
      """src_vocab_size""": src_vocab_size,
      """tgt_vocab_size""": tgt_vocab_size,
      """langs""": [src_lang, tgt_lang],
      """encoder_attention_heads""": args["""encoder_attention_heads"""],
      """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
      """encoder_layerdrop""": args["""encoder_layerdrop"""],
      """encoder_layers""": args["""encoder_layers"""],
      """decoder_attention_heads""": args["""decoder_attention_heads"""],
      """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
      """decoder_layerdrop""": args["""decoder_layerdrop"""],
      """decoder_layers""": args["""decoder_layers"""],
      """bos_token_id""": 0,
      """pad_token_id""": 1,
      """eos_token_id""": 2,
      """is_encoder_decoder""": True,
      """scale_embedding""": not args["""no_scale_embedding"""],
      """tie_word_embeddings""": args["""share_all_embeddings"""],
  }

  # good hparam defaults to start with
  lowercase__	: Optional[int]     		=   5
  lowercase__	: List[str]     		=   False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    lowercase__	: Optional[int]     		=   best_score_hparams[model_dir]["""length_penalty"""]
  else:
    lowercase__	: Union[str, Any]     		=   1.0

  print(F'Generating {fsmt_model_config_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # tokenizer config
  lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			lowercase_ )

  lowercase__	: int     		=   {
      """langs""": [src_lang, tgt_lang],
      """model_max_length""": 10_24,
      """do_lower_case""": do_lower_case,
  }

  print(F'Generating {fsmt_tokenizer_config_file}' )
  with open(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # model
  lowercase__	: Dict     		=   chkpt["""models"""][0]
  lowercase__	: Optional[Any]     		=   model.state_dict()

  # rename keys to start with 'model.'
  lowercase__	: Any     		=   OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )

  # remove unneeded keys
  lowercase__	: List[Any]     		=   [
      """model.model""",
      """model.encoder.version""",
      """model.decoder.version""",
      """model.encoder_embed_tokens.weight""",
      """model.decoder_embed_tokens.weight""",
      """model.encoder.embed_positions._float_tensor""",
      """model.decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(lowercase_			,			lowercase_ )

  lowercase__	: str     		=   FSMTConfig.from_pretrained(lowercase_ )
  lowercase__	: List[str]     		=   FSMTForConditionalGeneration(lowercase_ )

  # check that it loads ok
  model_new.load_state_dict(lowercase_			,			strict=lowercase_ )

  # save
  lowercase__	: str     		=   os.path.join(lowercase_			,			lowercase_ )
  print(F'Generating {pytorch_weights_dump_path}' )
  torch.save(lowercase_			,			lowercase_ )

  print("""Conversion is done!""" )
  print("""\nLast step is to upload the files to s3""" )
  print(F'cd {data_root}' )
  print(F'transformers-cli upload {model_dir}' )


if __name__ == "__main__":
     lowerCamelCase__     :		List[str]        = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--fsmt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help=(
             """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
             """ bpecodes, etc."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     lowerCamelCase__     :		List[str]        = parser.parse_args()
     convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)













import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE_   :      List[Any]			=   {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

SCREAMING_SNAKE_CASE_   :      List[str]			=   os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE_   :      List[Any]			=   os.path.join(os.path.expanduser('''~'''), '''.cache''')
SCREAMING_SNAKE_CASE_   :      Any			=   os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     model_type
		if use_small:
				key += "_small"
		return os.path.join(snake_case						,   REMOTE_MODEL_PATHS[key]['file_name']							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[str]:
		os.makedirs(snake_case						,   exist_ok=snake_case							)
		hf_hub_download(repo_id=snake_case						,   filename=snake_case						,   local_dir=snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False						,   snake_case="text"							)		->    List[Any]:
		if model_type == "text":
				__lowercase											=     BarkSemanticModel
				__lowercase											=     BarkSemanticConfig
				__lowercase											=     BarkSemanticGenerationConfig
		elif model_type == "coarse":
				__lowercase											=     BarkCoarseModel
				__lowercase											=     BarkCoarseConfig
				__lowercase											=     BarkCoarseGenerationConfig
		elif model_type == "fine":
				__lowercase											=     BarkFineModel
				__lowercase											=     BarkFineConfig
				__lowercase											=     BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		__lowercase											=     F"{model_type}_small" if use_small else model_type
		__lowercase											=     REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(snake_case							):
				logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."							)
				_download(model_info['repo_id']						,   model_info['file_name']							)
		__lowercase											=     torch.load(snake_case						,   map_location=snake_case							)
		# this is a hack
		__lowercase											=     checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				__lowercase											=     model_args['vocab_size']
				__lowercase											=     model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		__lowercase											=     model_args.pop('n_head'							)
		__lowercase											=     model_args.pop('n_embd'							)
		__lowercase											=     model_args.pop('n_layer'							)

		__lowercase											=     ConfigClass(**checkpoint['model_args']							)
		__lowercase											=     ModelClass(config=snake_case							)
		__lowercase											=     GenerationConfigClass()

		__lowercase											=     model_generation_config
		__lowercase											=     checkpoint['model']
		# fixup checkpoint
		__lowercase											=     '_orig_mod.'
		for k, v in list(state_dict.items()							):
				if k.startswith(snake_case							):
						# replace part of the key with corresponding layer name in HF implementation
						__lowercase											=     k[len(snake_case							) :]
						for old_layer_name in new_layer_name_dict:
								__lowercase											=     new_k.replace(snake_case						,   new_layer_name_dict[old_layer_name]							)

						__lowercase											=     state_dict.pop(snake_case							)

		__lowercase											=     set(state_dict.keys()							) - set(model.state_dict().keys()							)
		__lowercase											=     {k for k in extra_keys if not k.endswith('.attn.bias'							)}
		__lowercase											=     set(model.state_dict().keys()							) - set(state_dict.keys()							)
		__lowercase											=     {k for k in missing_keys if not k.endswith('.attn.bias'							)}
		if len(snake_case							) != 0:
				raise ValueError(F"extra keys found: {extra_keys}"							)
		if len(snake_case							) != 0:
				raise ValueError(F"missing keys: {missing_keys}"							)
		model.load_state_dict(snake_case						,   strict=snake_case							)
		__lowercase											=     model.num_parameters(exclude_embeddings=snake_case							)
		__lowercase											=     checkpoint['best_val_loss'].item()
		logger.info(F"model loaded: {round(n_params/1E6						,   1							)}M params, {round(snake_case						,   3							)} loss"							)
		model.eval()
		model.to(snake_case							)
		del checkpoint, state_dict

		return model



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False						,   snake_case="text"							)		->    Tuple:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		__lowercase											=     'cpu'  # do conversion on cpu

		__lowercase											=     _get_ckpt_path(snake_case						,   use_small=snake_case							)
		__lowercase											=     _load_model(snake_case						,   snake_case						,   model_type=snake_case						,   use_small=snake_case							)

		# load bark initial model
		__lowercase											=     _bark_load_model(snake_case						,   'cpu'						,   model_type=snake_case						,   use_small=snake_case							)

		if model_type == "text":
				__lowercase											=     bark_model['model']

		if model.num_parameters(exclude_embeddings=snake_case							) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'							)

		# check if same output as the bark model
		__lowercase											=     5
		__lowercase											=     10

		if model_type in ["text", "coarse"]:
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length)						,   dtype=torch.int							)
				__lowercase											=     bark_model(snake_case							)[0]

				__lowercase											=     model(snake_case							)

				# take last logits
				__lowercase											=     output_new_model_total.logits[:, [-1], :]

		else:
				__lowercase											=     3
				__lowercase											=     8
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length, n_codes_total)						,   dtype=torch.int							)

				__lowercase											=     model(snake_case						,   snake_case							)
				__lowercase											=     bark_model(snake_case						,   snake_case							)

				__lowercase											=     output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'							)
		if (output_new_model - output_old_model).abs().max().item() > 1E-3:
				raise ValueError('initial and new outputs are not equal'							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		model.save_pretrained(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   )		->    List[str]:
		__lowercase											=     os.path.join(snake_case						,   snake_case							)

		__lowercase											=     BarkSemanticConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkCoarseConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkFineConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     EncodecConfig.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkSemanticModel.from_pretrained(snake_case							)
		__lowercase											=     BarkCoarseModel.from_pretrained(snake_case							)
		__lowercase											=     BarkFineModel.from_pretrained(snake_case							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkConfig.from_sub_model_configs(
		    snake_case						,   snake_case						,   snake_case						,   snake_case							)

		__lowercase											=     BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,   coarseAcoustic.generation_config						,   fineAcoustic.generation_config							)

		__lowercase											=     BarkModel(snake_case							)

		__lowercase											=     semantic
		__lowercase											=     coarseAcoustic
		__lowercase											=     fineAcoustic
		__lowercase											=     codec

		__lowercase											=     bark_generation_config

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		bark.save_pretrained(snake_case						,   repo_id=snake_case						,   push_to_hub=snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   argparse.ArgumentParser()
		# Required parameters

		parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




