
'''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()
lowercase					:							Dict   =      logging.get_logger(__name__)

set_seed(770)


lowercase					:							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",
}


lowercase					:							Dict   =      {
    "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",
    },
}

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=False			) ->			int:
	_snake_case     		=	model_type
	if use_small:
		key += "_small"
	return os.path.join(UpperCAmelCase__    ,       REMOTE_MODEL_PATHS[key]['file_name']			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	os.makedirs(UpperCAmelCase__    ,       exist_ok=UpperCAmelCase__			)
	hf_hub_download(repo_id=UpperCAmelCase__    ,       filename=UpperCAmelCase__    ,       local_dir=UpperCAmelCase__			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=False    ,       __A="text"			) ->			str:
	if model_type == "text":
		_snake_case     		=	BarkSemanticModel
		_snake_case     		=	BarkSemanticConfig
		_snake_case     		=	BarkSemanticGenerationConfig
	elif model_type == "coarse":
		_snake_case     		=	BarkCoarseModel
		_snake_case     		=	BarkCoarseConfig
		_snake_case     		=	BarkCoarseGenerationConfig
	elif model_type == "fine":
		_snake_case     		=	BarkFineModel
		_snake_case     		=	BarkFineConfig
		_snake_case     		=	BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	_snake_case     		=	F'{model_type}_small' if use_small else model_type
	_snake_case     		=	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     		=	torch.load(UpperCAmelCase__    ,       map_location=UpperCAmelCase__			)
	# this is a hack
	_snake_case     		=	checkpoint["""model_args"""]
	if "input_vocab_size" not in model_args:
		_snake_case     		=	model_args["""vocab_size"""]
		_snake_case     		=	model_args["""vocab_size"""]
		del model_args["vocab_size"]

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

	_snake_case     		=	ConfigClass(**checkpoint['model_args']			)
	_snake_case     		=	ModelClass(config=UpperCAmelCase__			)
	_snake_case     		=	GenerationConfigClass()

	_snake_case     		=	model_generation_config
	_snake_case     		=	checkpoint["""model"""]
	# fixup checkpoint
	_snake_case     		=	"""_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     		=	k[len(UpperCAmelCase__			) :]
			for old_layer_name in new_layer_name_dict:
				_snake_case     		=	new_k.replace(UpperCAmelCase__    ,       new_layer_name_dict[old_layer_name]			)

			_snake_case     		=	state_dict.pop(UpperCAmelCase__			)

	_snake_case     		=	set(state_dict.keys()			) - set(model.state_dict().keys()			)
	_snake_case     		=	{k for k in extra_keys if not k.endswith('.attn.bias'			)}
	_snake_case     		=	set(model.state_dict().keys()			) - set(state_dict.keys()			)
	_snake_case     		=	{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     		=	model.num_parameters(exclude_embeddings=UpperCAmelCase__			)
	_snake_case     		=	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__			(     __A    ,       __A=False    ,       __A="text"			) ->			str:
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	_snake_case     		=	"""cpu"""  # do conversion on cpu

	_snake_case     		=	_get_ckpt_path(UpperCAmelCase__    ,       use_small=UpperCAmelCase__			)
	_snake_case     		=	_load_model(UpperCAmelCase__    ,       UpperCAmelCase__    ,       model_type=UpperCAmelCase__    ,       use_small=UpperCAmelCase__			)

	# load bark initial model
	_snake_case     		=	_bark_load_model(UpperCAmelCase__    ,       'cpu'    ,       model_type=UpperCAmelCase__    ,       use_small=UpperCAmelCase__			)

	if model_type == "text":
		_snake_case     		=	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     		=	5
	_snake_case     		=	10

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

		_snake_case     		=	model(UpperCAmelCase__			)

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

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

		_snake_case     		=	model(UpperCAmelCase__    ,       UpperCAmelCase__			)
		_snake_case     		=	bark_model(UpperCAmelCase__    ,       UpperCAmelCase__			)

		_snake_case     		=	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__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       ) ->			List[str]:
	_snake_case     		=	os.path.join(UpperCAmelCase__    ,       UpperCAmelCase__			)

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

	_snake_case     		=	BarkSemanticModel.from_pretrained(UpperCAmelCase__			)
	_snake_case     		=	BarkCoarseModel.from_pretrained(UpperCAmelCase__			)
	_snake_case     		=	BarkFineModel.from_pretrained(UpperCAmelCase__			)
	_snake_case     		=	EncodecModel.from_pretrained('facebook/encodec_24khz'			)

	_snake_case     		=	BarkConfig.from_sub_model_configs(
	    UpperCAmelCase__    ,       UpperCAmelCase__    ,       UpperCAmelCase__    ,       UpperCAmelCase__			)

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

	_snake_case     		=	BarkModel(UpperCAmelCase__			)

	_snake_case     		=	semantic
	_snake_case     		=	coarseAcoustic
	_snake_case     		=	fineAcoustic
	_snake_case     		=	codec

	_snake_case     		=	bark_generation_config

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


if __name__ == "__main__":
					lowercase					:							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.")

					lowercase					:							int   =      parser.parse_args()

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






'''simple docstring'''







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = "x"    ,       __A = 10**-10    ,       __A = 1    ,       ) ->			complex:
	_snake_case     		=	symbols(__A			)
	_snake_case     		=	lambdify(__A    ,       __A			)
	_snake_case     		=	lambdify(__A    ,       diff(__A    ,       __A			)			)

	_snake_case     		=	starting_point

	while True:
		if diff_function(__A			) != 0:
			_snake_case     		=	prev_guess - multiplicity * func(__A			) / diff_function(
			    __A			)
		else:
			raise ZeroDivisionError('Could not find root'			) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess			) < precision:
			return next_guess

		_snake_case     		=	next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

					# Find root of polynomial
					# Find fourth Root of 5
					print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
					)

					# Find root of cos(x)
					print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

