





"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 10							,     SCREAMING_SNAKE_CASE = 1000							,     SCREAMING_SNAKE_CASE = True     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)"""     )
						return min_val if option else max_val

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return int((number_a + number_a) / 2     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError("""argument value for lower and higher must be(lower > higher)"""     )

						if not lower < to_guess < higher:
												raise ValueError(
												    """guess value must be within the range of lower and higher value"""     )

						def answer(SCREAMING_SNAKE_CASE     ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print("""started..."""     )

						UpperCamelCase				:		Union[str, Any]				   =			lower
						UpperCamelCase				:		Any				   =			higher

						UpperCamelCase				:		List[str]				   =			[]

						while True:
												UpperCamelCase				:		Optional[int]				   =			get_avg(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
												last_numbers.append(SCREAMING_SNAKE_CASE     )

												if answer(SCREAMING_SNAKE_CASE     ) == "low":
																		UpperCamelCase				:		List[Any]				   =			number
												elif answer(SCREAMING_SNAKE_CASE     ) == "high":
																		UpperCamelCase				:		Optional[Any]				   =			number
												else:
																		break

						print(f"""guess the number : {last_numbers[-1]}"""     )
						print(f"""details : {last_numbers!s}"""     )

def      UpperCamelCase							():
						UpperCamelCase				:		List[str]				   =			int(input("""Enter lower value : """     ).strip()     )
						UpperCamelCase				:		Dict				   =			int(input("""Enter high value : """     ).strip()     )
						UpperCamelCase				:		List[str]				   =			int(input("""Enter value to guess : """     ).strip()     )
						guess_the_number(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		main()










"""simple docstring"""


import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


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


__magic_name__       :					List[str]						=			["""model.decoder.embed_positions.weights"""]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if "emb" in name:
												UpperCamelCase				:		Tuple				   =			name.replace("""emb"""							,     """model.decoder.embed_tokens"""     )
						if "transformer" in name:
												UpperCamelCase				:		List[str]				   =			name.replace("""transformer"""							,     """model.decoder"""     )
						if "cross_attention" in name:
												UpperCamelCase				:		str				   =			name.replace("""cross_attention"""							,     """encoder_attn"""     )
						if "linear1" in name:
												UpperCamelCase				:		Optional[int]				   =			name.replace("""linear1"""							,     """fc1"""     )
						if "linear2" in name:
												UpperCamelCase				:		int				   =			name.replace("""linear2"""							,     """fc2"""     )
						if "norm1" in name:
												UpperCamelCase				:		List[Any]				   =			name.replace("""norm1"""							,     """self_attn_layer_norm"""     )
						if "norm_cross" in name:
												UpperCamelCase				:		int				   =			name.replace("""norm_cross"""							,     """encoder_attn_layer_norm"""     )
						if "norm2" in name:
												UpperCamelCase				:		str				   =			name.replace("""norm2"""							,     """final_layer_norm"""     )
						if "out_norm" in name:
												UpperCamelCase				:		Tuple				   =			name.replace("""out_norm"""							,     """model.decoder.layer_norm"""     )
						if "linears" in name:
												UpperCamelCase				:		str				   =			name.replace("""linears"""							,     """lm_heads"""     )
						if "condition_provider.conditioners.description.output_proj" in name:
												UpperCamelCase				:		Dict				   =			name.replace("""condition_provider.conditioners.description.output_proj"""							,     """enc_to_dec_proj"""     )
						return name

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			list(state_dict.keys()     )
						UpperCamelCase				:		Optional[Any]				   =			{}
						for key in keys:
												UpperCamelCase				:		Optional[int]				   =			state_dict.pop(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Dict				   =			rename_keys(SCREAMING_SNAKE_CASE     )
												if "in_proj_weight" in key:
																		# split fused qkv proj
																		UpperCamelCase				:		Optional[int]				   =			val[:hidden_size, :]
																		UpperCamelCase				:		List[Any]				   =			val[hidden_size : 2 * hidden_size, :]
																		UpperCamelCase				:		Dict				   =			val[-hidden_size:, :]
												elif "enc_to_dec_proj" in key:
																		UpperCamelCase				:		Any				   =			val
												else:
																		UpperCamelCase				:		str				   =			val
						return state_dict, enc_dec_proj_state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if checkpoint == "small":
												# default config values
												UpperCamelCase				:		Tuple				   =			1024
												UpperCamelCase				:		Dict				   =			24
												UpperCamelCase				:		Optional[int]				   =			16
						elif checkpoint == "medium":
												UpperCamelCase				:		Optional[Any]				   =			1536
												UpperCamelCase				:		int				   =			48
												UpperCamelCase				:		int				   =			24
						elif checkpoint == "large":
												UpperCamelCase				:		Any				   =			2048
												UpperCamelCase				:		List[Any]				   =			48
												UpperCamelCase				:		str				   =			32
						else:
												raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""     )
						UpperCamelCase				:		Optional[int]				   =			MusicgenDecoderConfig(
						    hidden_size=SCREAMING_SNAKE_CASE							,     ffn_dim=hidden_size * 4							,     num_hidden_layers=SCREAMING_SNAKE_CASE							,     num_attention_heads=SCREAMING_SNAKE_CASE							,     )
						return config

@torch.no_grad()
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE="cpu"     ):
						UpperCamelCase				:		Optional[Any]				   =			MusicGen.get_pretrained(SCREAMING_SNAKE_CASE							,     device=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			fairseq_model.lm.state_dict()
						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			rename_state_dict(
						    SCREAMING_SNAKE_CASE							,     hidden_size=decoder_config.hidden_size     )

						UpperCamelCase				:		int				   =			TaEncoderModel.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		List[str]				   =			EncodecModel.from_pretrained("""facebook/encodec_32khz"""     )
						UpperCamelCase				:		Optional[int]				   =			MusicgenForCausalLM(SCREAMING_SNAKE_CASE     ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			decoder.load_state_dict(SCREAMING_SNAKE_CASE							,     strict=SCREAMING_SNAKE_CASE     )

						for key in missing_keys.copy():
												if key.startswith(("""text_encoder""", """audio_encoder""")     ) or key in EXPECTED_MISSING_KEYS:
																		missing_keys.remove(SCREAMING_SNAKE_CASE     )

						if len(SCREAMING_SNAKE_CASE     ) > 0:
												raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}"""     )

						if len(SCREAMING_SNAKE_CASE     ) > 0:
												raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}"""     )

						# init the composite model
						UpperCamelCase				:		Tuple				   =			MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE							,     audio_encoder=SCREAMING_SNAKE_CASE							,     decoder=SCREAMING_SNAKE_CASE     )

						# load the pre-trained enc-dec projection (from the decoder state dict)
						model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE     )

						# check we can do a forward pass
						UpperCamelCase				:		int				   =			torch.arange(0							,     8							,     dtype=torch.long     ).reshape(2							,     -1     )
						UpperCamelCase				:		Tuple				   =			input_ids.reshape(2 * 4							,     -1     )

						with torch.no_grad():
												UpperCamelCase				:		Any				   =			model(input_ids=SCREAMING_SNAKE_CASE							,     decoder_input_ids=SCREAMING_SNAKE_CASE     ).logits

						if logits.shape != (8, 1, 2048):
												raise ValueError("""Incorrect shape for logits"""     )

						# now construct the processor
						UpperCamelCase				:		Any				   =			AutoTokenizer.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		Tuple				   =			AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""							,     padding_side="""left"""     )

						UpperCamelCase				:		int				   =			MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE     )

						# set the appropriate bos/pad token ids
						UpperCamelCase				:		Optional[int]				   =			2048
						UpperCamelCase				:		Tuple				   =			2048

						# set other default generation config params
						UpperCamelCase				:		Optional[int]				   =			int(30 * audio_encoder.config.frame_rate     )
						UpperCamelCase				:		int				   =			True
						UpperCamelCase				:		Dict				   =			3.0

						if pytorch_dump_folder is not None:
												Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
												logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}"""     )
												model.save_pretrained(SCREAMING_SNAKE_CASE     )
												processor.save_pretrained(SCREAMING_SNAKE_CASE     )

						if repo_id:
												logger.info(f"""Pushing model {checkpoint} to {repo_id}"""     )
												model.push_to_hub(SCREAMING_SNAKE_CASE     )
												processor.push_to_hub(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		__magic_name__       :					Any						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--checkpoint""",
		    default="""small""",
		    type=str,
		    help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder""",
		    required=True,
		    default=None,
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument(
		    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
		)
		parser.add_argument(
		    """--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
		)

		__magic_name__       :					Optional[Any]						=			parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



