
"""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()
a_			   =					logging.get_logger(__name__)

set_seed(770)


a_			   =					{
    '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',
}


a_			   =					{
    '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',
    },
}

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=False			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					model_type
					if use_small:
										key += "_small"
					return os.path.join(__UpperCamelCase				,    REMOTE_MODEL_PATHS[key]["""file_name"""]			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					os.makedirs(__UpperCamelCase				,    exist_ok=__UpperCamelCase			)
					hf_hub_download(repo_id=__UpperCamelCase				,    filename=__UpperCamelCase				,    local_dir=__UpperCamelCase			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  int=False				,    SCREAMING_SNAKE_CASE__ :  Optional[int]="text"			):



					"""simple docstring"""





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

					# convert Bark model arguments to HF Bark model arguments
					snake_case_					:		Optional[Any] 					=					model_args.pop("""n_head"""			)
					snake_case_					:		Tuple 					=					model_args.pop("""n_embd"""			)
					snake_case_					:		Tuple 					=					model_args.pop("""n_layer"""			)

					snake_case_					:		Any 					=					ConfigClass(**checkpoint["""model_args"""]			)
					snake_case_					:		Tuple 					=					ModelClass(config=__UpperCamelCase			)
					snake_case_					:		Any 					=					GenerationConfigClass()

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

															snake_case_					:		Optional[int] 					=					state_dict.pop(__UpperCamelCase			)

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

					return model







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=False				,    SCREAMING_SNAKE_CASE__ :  Tuple="text"			):



					"""simple docstring"""





					if model_type not in ("text", "coarse", "fine"):
										raise NotImplementedError()

					snake_case_					:		str 					=					'''cpu'''  # do conversion on cpu

					snake_case_					:		int 					=					_get_ckpt_path(__UpperCamelCase				,    use_small=__UpperCamelCase			)
					snake_case_					:		Optional[int] 					=					_load_model(__UpperCamelCase				,    __UpperCamelCase				,    model_type=__UpperCamelCase				,    use_small=__UpperCamelCase			)

					# load bark initial model
					snake_case_					:		List[Any] 					=					_bark_load_model(__UpperCamelCase				,    """cpu"""				,    model_type=__UpperCamelCase				,    use_small=__UpperCamelCase			)

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

					if model.num_parameters(exclude_embeddings=__UpperCamelCase			) != 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
					snake_case_					:		Optional[Any] 					=					5
					snake_case_					:		Dict 					=					1_0

					if model_type in ["text", "coarse"]:
										snake_case_					:		Tuple 					=					torch.randint(2_5_6				,    (batch_size, sequence_length)				,    dtype=torch.int			)
										snake_case_					:		Tuple 					=					bark_model(__UpperCamelCase			)[0]

										snake_case_					:		int 					=					model(__UpperCamelCase			)

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

					else:
										snake_case_					:		str 					=					3
										snake_case_					:		List[str] 					=					8
										snake_case_					:		List[Any] 					=					torch.randint(2_5_6				,    (batch_size, sequence_length, n_codes_total)				,    dtype=torch.int			)

										snake_case_					:		List[Any] 					=					model(__UpperCamelCase				,    __UpperCamelCase			)
										snake_case_					:		Union[str, Any] 					=					bark_model(__UpperCamelCase				,    __UpperCamelCase			)

										snake_case_					:		Optional[Any] 					=					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(__UpperCamelCase			).mkdir(exist_ok=__UpperCamelCase			)
					model.save_pretrained(__UpperCamelCase			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    ):



					"""simple docstring"""





					snake_case_					:		int 					=					os.path.join(__UpperCamelCase				,    __UpperCamelCase			)

					snake_case_					:		List[str] 					=					BarkSemanticConfig.from_pretrained(os.path.join(__UpperCamelCase				,    """config.json"""			)			)
					snake_case_					:		Optional[Any] 					=					BarkCoarseConfig.from_pretrained(os.path.join(__UpperCamelCase				,    """config.json"""			)			)
					snake_case_					:		str 					=					BarkFineConfig.from_pretrained(os.path.join(__UpperCamelCase				,    """config.json"""			)			)
					snake_case_					:		int 					=					EncodecConfig.from_pretrained("""facebook/encodec_24khz"""			)

					snake_case_					:		Dict 					=					BarkSemanticModel.from_pretrained(__UpperCamelCase			)
					snake_case_					:		str 					=					BarkCoarseModel.from_pretrained(__UpperCamelCase			)
					snake_case_					:		Optional[int] 					=					BarkFineModel.from_pretrained(__UpperCamelCase			)
					snake_case_					:		int 					=					EncodecModel.from_pretrained("""facebook/encodec_24khz"""			)

					snake_case_					:		str 					=					BarkConfig.from_sub_model_configs(
					    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase			)

					snake_case_					:		Any 					=					BarkGenerationConfig.from_sub_model_configs(
					    semantic.generation_config				,    coarseAcoustic.generation_config				,    fineAcoustic.generation_config			)

					snake_case_					:		List[Any] 					=					BarkModel(__UpperCamelCase			)

					snake_case_					:		List[Any] 					=					semantic
					snake_case_					:		Tuple 					=					coarseAcoustic
					snake_case_					:		Union[str, Any] 					=					fineAcoustic
					snake_case_					:		Any 					=					codec

					snake_case_					:		Union[str, Any] 					=					bark_generation_config

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


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

			a_			   =					parser.parse_args()

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









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




