






'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       str:
							# Return True if there is node that has not iterated.
							lowerCamelCase__					:	Optional[Any]         =       [False] * len(UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       []
							queue.append(UpperCamelCase      )
							lowerCamelCase__					:	List[str]         =       True

							while queue:
														lowerCamelCase__					:	Optional[Any]         =       queue.pop(0      )
														for ind in range(len(graph[u]      )      ):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(UpperCamelCase      )
																												lowerCamelCase__					:	Dict         =       True
																												lowerCamelCase__					:	List[str]         =       u

							return visited[t]





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Optional[Any]:
							# This array is filled by BFS and to store path
							lowerCamelCase__					:	Tuple         =       [-1] * (len(UpperCamelCase      ))
							lowerCamelCase__					:	Dict         =       0
							while bfs(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      ):
														lowerCamelCase__					:	Optional[Any]         =       float("""Inf"""      )
														lowerCamelCase__					:	Optional[int]         =       sink

														while s != source:
																					# Find the minimum value in select path
																					lowerCamelCase__					:	Optional[int]         =       min(UpperCamelCase				,			graph[parent[s]][s]      )
																					lowerCamelCase__					:	Optional[Any]         =       parent[s]

														max_flow += path_flow
														lowerCamelCase__					:	List[Any]         =       sink

														while v != source:
																					lowerCamelCase__					:	Optional[int]         =       parent[v]
																					graph[u][v] -= path_flow
																					graph[v][u] += path_flow
																					lowerCamelCase__					:	Dict         =       parent[v]
							return max_flow


_A	:			str         =[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

_A     ,      _A	:			Optional[Any]         =0, 5



print(ford_fulkerson(graph, source, sink))









'''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()
_A	:			List[Any]         =logging.get_logger(__name__)


_A	:			Dict         =['''model.decoder.embed_positions.weights''']





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





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





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





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=None				,			UpperCamelCase=None				,			UpperCamelCase="cpu"      )      ->       Optional[Any]:
							lowerCamelCase__					:	Optional[int]         =       MusicGen.get_pretrained(UpperCamelCase				,			device=UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       decoder_config_from_checkpoint(UpperCamelCase      )

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

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

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       decoder.load_state_dict(UpperCamelCase				,			strict=UpperCamelCase      )

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

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

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

							# init the composite model
							lowerCamelCase__					:	Optional[Any]         =       MusicgenForConditionalGeneration(text_encoder=UpperCamelCase				,			audio_encoder=UpperCamelCase				,			decoder=UpperCamelCase      )

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

							# check we can do a forward pass
							lowerCamelCase__					:	Dict         =       torch.arange(0				,			8				,			dtype=torch.long      ).reshape(2				,			-1      )
							lowerCamelCase__					:	Optional[Any]         =       input_ids.reshape(2 * 4				,			-1      )

							with torch.no_grad():
														lowerCamelCase__					:	Union[str, Any]         =       model(input_ids=UpperCamelCase				,			decoder_input_ids=UpperCamelCase      ).logits

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

							# now construct the processor
							lowerCamelCase__					:	str         =       AutoTokenizer.from_pretrained("""t5-base"""      )
							lowerCamelCase__					:	Union[str, Any]         =       AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""				,			padding_side="""left"""      )

							lowerCamelCase__					:	Optional[int]         =       MusicgenProcessor(feature_extractor=UpperCamelCase				,			tokenizer=UpperCamelCase      )

							# set the appropriate bos/pad token ids
							lowerCamelCase__					:	Union[str, Any]         =       2048
							lowerCamelCase__					:	List[str]         =       2048

							# set other default generation config params
							lowerCamelCase__					:	Optional[Any]         =       int(30 * audio_encoder.config.frame_rate      )
							lowerCamelCase__					:	Union[str, Any]         =       True
							lowerCamelCase__					:	List[Any]         =       3.0

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

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


if __name__ == "__main__":
			_A	:			Dict         =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.'''
			)

			_A	:			List[str]         =parser.parse_args()
			convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

