



'''simple docstring'''

import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				_a					:				List[str]				=							flax_key_tuple[:-1] + ("""weight""",)
				_a					:				Union[str, Any]				=							torch.permute(UpperCAmelCase__						,      (0, 2, 1)  )
		elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase__  ):
				# linear layer
				_a					:				Dict				=							flax_key_tuple[:-1] + ("""weight""",)
				_a					:				Any				=							flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				_a					:				Tuple				=							flax_key_tuple[:-1] + ("""weight""",)

		return flax_key_tuple, flax_tensor



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[Any]:
		if "metadata" in layer:
				_a					:				Tuple				=							layer.split('metadata'  )
				_a					:				Optional[Any]				=							"""""".join(split_layer[0]  )[:-1]
				_a					:				Union[str, Any]				=							[tuple(('metadata' + split_layer[1]).split('/'  )  )]
		elif "kvstore" in layer:
				_a					:				Union[str, Any]				=							layer.split('kvstore'  )
				_a					:				Union[str, Any]				=							"""""".join(split_layer[0]  )[:-1]
				_a					:				Optional[Any]				=							[tuple(('kvstore' + split_layer[1]).split('/'  )  )]

		else:
				_a					:				Any				=							layer.split('/'  )
				_a					:				Any				=							"""/""".join(split_layer[:-1]  )
				_a					:				Optional[int]				=							(split_layer[-1],)

		if "kvstore/path" in layer:
				_a					:				Any				=							f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
		elif "kvstore/driver" in layer:
				_a					:				List[str]				=							"""file"""
		else:
				_a					:				Tuple				=							checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		_a					:				Tuple				=							rename_keys(UpperCAmelCase__  )
		_a					:				Optional[Any]				=							{}
		for k, v in current_block.items():
				_a					:				str				=							v
		_a					:				List[Any]				=							new_current_block
		torch.save(UpperCAmelCase__						,      UpperCAmelCase__  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = WEIGHTS_NAME  )      ->				str:
		_a					:				Union[str, Any]				=							convert_file_size_to_int(UpperCAmelCase__  )
		_a					:				Any				=							[]
		_a					:				Union[str, Any]				=							{}
		_a					:				Any				=							0
		_a					:				Optional[Any]				=							0

		os.makedirs(UpperCAmelCase__						,      exist_ok=UpperCAmelCase__  )
		with gfile.GFile(switch_checkpoint_path + '/checkpoint'						,      'rb'  ) as fp:
				_a					:				int				=							serialization.msgpack_restore(fp.read()  )["""optimizer"""]["""target"""]
				_a					:				Union[str, Any]				=							flatten_dict(UpperCAmelCase__						,      sep='/'  )

		_a					:				Optional[Any]				=							{}
		for layer in checkpoint_info.keys():
				_a					:				List[str]				=							get_key_and_tensorstore_dict(
				    UpperCAmelCase__						,      UpperCAmelCase__						,      UpperCAmelCase__  )
				if curr_real_layer_name in all_layers:
						_a					:				List[Any]				=							content
				else:
						_a					:				Optional[Any]				=							{split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				_a					:				List[str]				=							ts.open(unflatten_dict(all_layers[key]  )  ).result().read().result()
				_a					:				int				=							torch.tensor(UpperCAmelCase__  )
				_a					:				Optional[Any]				=							raw_weights.numel() * dtype_byte_size(raw_weights.dtype  )

				# use the renaming pattern from the small conversion scripts
				_a					:				Union[str, Any]				=							rename_base_flax_keys(tuple(key.split('/'  )  )						,      UpperCAmelCase__  )
				_a					:				List[str]				=							"""/""".join(UpperCAmelCase__  )

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						_a					:				Dict				=							os.path.join(
						    UpperCAmelCase__						,      weights_name.replace('.bin'						,      f"""-{len(UpperCAmelCase__  )+1:05d}-of-???.bin"""  )  )
						rename_and_save_block(UpperCAmelCase__						,      UpperCAmelCase__  )
						sharded_state_dicts.append(current_block.keys()  )
						del current_block
						_a					:				Optional[int]				=							{}
						_a					:				Union[str, Any]				=							0

				_a					:				str				=							raw_weights.to(getattr(UpperCAmelCase__						,      UpperCAmelCase__  )  )
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		_a					:				int				=							os.path.join(UpperCAmelCase__						,      weights_name.replace('.bin'						,      f"""-{len(UpperCAmelCase__  )+1:05d}-of-???.bin"""  )  )
		rename_and_save_block(UpperCAmelCase__						,      UpperCAmelCase__  )
		sharded_state_dicts.append(current_block.keys()  )

		# If we only have one shard, we return it
		if len(UpperCAmelCase__  ) == 1:
				return {weights_name: sharded_state_dicts[0]}, None

		# Otherwise, let's build the index
		_a					:				int				=							{}
		_a					:				Tuple				=							{}
		for idx, shard in enumerate(UpperCAmelCase__  ):
				_a					:				Optional[int]				=							weights_name.replace(
				    '.bin'						,      f"""-{idx+1:05d}-of-{len(UpperCAmelCase__  ):05d}.bin"""  )  # len(sharded_state_dicts):05d}
				_a					:				Union[str, Any]				=							os.path.join(UpperCAmelCase__						,      weights_name.replace('.bin'						,      f"""-{idx+1:05d}-of-???.bin"""  )  )
				os.rename(UpperCAmelCase__						,      os.path.join(UpperCAmelCase__						,      UpperCAmelCase__  )  )
				_a					:				Tuple				=							shard
				for key in shard:
						_a					:				Union[str, Any]				=							shard_file

    # Add the metadata
		_a					:				Any				=							{"""total_size""": total_size}
		_a					:				Optional[Any]				=							{"""metadata""": metadata, """weight_map""": weight_map}

		with open(os.path.join(UpperCAmelCase__						,      UpperCAmelCase__  )						,      'w'						,      encoding='utf-8'  ) as f:
				_a					:				Optional[int]				=							json.dumps(UpperCAmelCase__						,      indent=2						,      sort_keys=UpperCAmelCase__  ) + """\n"""
				f.write(UpperCAmelCase__  )

		return metadata, index


if __name__ == "__main__":
							snake_case__						      =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--switch_t5x_checkpoint_path''',
							    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
							    type=str,
							    required=False,
							    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
							)
							parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
							parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
							    type=str,
							    required=False,
							    help='''Path to the output pytorch model.''',
							)
							snake_case__						      =       parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)



def 					__lowerCamelCase					(				)      ->				Optional[int]:
		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

		_a					:				str				=							SwitchTransformersConfig.from_pretrained('google/switch-base-8'  )
		config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'  )
		_a					:				Optional[int]				=							SwitchTransformersForConditionalGeneration.from_pretrained(
		    '/home/arthur_huggingface_co/transformers/switch_converted'						,      device_map='auto'  )

		_a					:				str				=							TaTokenizer.from_pretrained('t5-small'  )
		_a					:				List[Any]				=							"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

		_a					:				Optional[Any]				=							tokenizer(UpperCAmelCase__						,      return_tensors='pt'  ).input_ids
		_a					:				Union[str, Any]				=							model.generate(UpperCAmelCase__						,      decoder_start_token_id=0  )
		print(tokenizer.decode(out[0]  )  )









'''simple docstring'''

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()
__lowerCAmelCase						      =       logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase						      =       {
    '''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''',
}


__lowerCAmelCase						      =       {
    '''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''',
    },
}

__lowerCAmelCase						      =       os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase						      =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
__lowerCAmelCase						      =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
		_a					:				int				=							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



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

		# convert Bark model arguments to HF Bark model arguments
		_a					:				List[Any]				=							model_args.pop('n_head'  )
		_a					:				Any				=							model_args.pop('n_embd'  )
		_a					:				List[Any]				=							model_args.pop('n_layer'  )

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

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

						_a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

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

		return model



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[Any]:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		_a					:				Optional[int]				=							'cpu'  # do conversion on cpu

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		# load bark initial model
		_a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		if model_type == "text":
				_a					:				int				=							bark_model['model']

		if model.num_parameters(exclude_embeddings=lowerCAmelCase_  ) != 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
		_a					:				Any				=							5
		_a					:				List[str]				=							10

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

				_a					:				Tuple				=							model(lowerCAmelCase_  )

				# take last logits
				_a					:				Optional[int]				=							output_new_model_total.logits[:, [-1], :]

		else:
				_a					:				List[str]				=							3
				_a					:				List[Any]				=							8
				_a					:				Tuple				=							torch.randint(256						,      (batch_size, sequence_length, n_codes_total)						,      dtype=torch.int  )

				_a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

				_a					:				List[str]				=							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(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				int				=							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				Any				=							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[Any]				=							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[str]				=							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a					:				Any				=							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				List[str]				=							BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,      coarseAcoustic.generation_config						,      fineAcoustic.generation_config  )

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

		_a					:				List[str]				=							semantic
		_a					:				Union[str, Any]				=							coarseAcoustic
		_a					:				Optional[int]				=							fineAcoustic
		_a					:				Optional[Any]				=							codec

		_a					:				List[Any]				=							bark_generation_config

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


if __name__ == "__main__":
							__lowerCAmelCase						      =       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.''')

							__lowerCAmelCase						      =       parser.parse_args()

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




