





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     None
			__lowercase											=     None
			__lowercase											=     graph

			self._normalize_graph(__lowerCamelCase					,      __lowerCamelCase   )
			__lowercase											=     len(__lowerCamelCase   )
			__lowercase											=     None




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     List[str]   )       -> List[str]:

			'''simple docstring'''




			if sources is int:
					__lowercase											=     [sources]
			if sinks is int:
					__lowercase											=     [sinks]

			if len(__lowerCamelCase   ) == 0 or len(__lowerCamelCase   ) == 0:
					return

			__lowercase											=     sources[0]
			__lowercase											=     sinks[0]

			# make fake vertex if there are more
			# than one source or sink
			if len(__lowerCamelCase   ) > 1 or len(__lowerCamelCase   ) > 1:
					__lowercase											=     0
					for i in sources:
							max_input_flow += sum(self.graph[i]   )

					__lowercase											=     len(self.graph   ) + 1
					for room in self.graph:
							room.insert(0					,      0   )
					self.graph.insert(0					,      [0] * size   )
					for i in sources:
							__lowercase											=     max_input_flow
					__lowercase											=     0

					__lowercase											=     len(self.graph   ) + 1
					for room in self.graph:
							room.append(0   )
					self.graph.append([0] * size   )
					for i in sinks:
							__lowercase											=     max_input_flow
					__lowercase											=     size - 1




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Any:

			'''simple docstring'''




			if self.maximum_flow_algorithm is None:
					raise Exception('You need to set maximum flow algorithm before.'   )
			if self.source_index is None or self.sink_index is None:
					return 0

			self.maximum_flow_algorithm.execute()
			return self.maximum_flow_algorithm.getMaximumFlow()






	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Tuple   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     algorithm(self   )





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     int					,      __lowerCamelCase							:     List[Any]   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     flow_network
			__lowercase											=     flow_network.verticesCount
			__lowercase											=     flow_network.sourceIndex
			__lowercase											=     flow_network.sinkIndex
			# it's just a reference, so you shouldn't change
			# it in your algorithms, use deep copy before doing that
			__lowercase											=     flow_network.graph
			__lowercase											=     False




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			if not self.executed:
					self._algorithm()
					__lowercase											=     True






	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			pass





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Tuple					,      __lowerCamelCase							:     List[str]   )       -> Optional[int]:

			'''simple docstring'''




			super().__init__(__lowerCamelCase   )
			# use this to save your result
			__lowercase											=     -1






	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[Any]:

			'''simple docstring'''




			if not self.executed:
					raise Exception('You should execute algorithm before using its result!'   )

			return self.maximum_flow





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]					,      __lowerCamelCase							:     Optional[Any]   )       -> List[str]:

			'''simple docstring'''




			super().__init__(__lowerCamelCase   )

			__lowercase											=     [[0] * self.verticies_count for i in range(self.verticies_count   )]

			__lowercase											=     [0] * self.verticies_count
			__lowercase											=     [0] * self.verticies_count




	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> str:

			'''simple docstring'''




			__lowercase											=     self.verticies_count

			# push some substance to graph
			for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]   ):
					self.preflow[self.source_index][nextvertex_index] += bandwidth
					self.preflow[nextvertex_index][self.source_index] -= bandwidth
					self.excesses[nextvertex_index] += bandwidth

			# Relabel-to-front selection rule
			__lowercase											=     [
			    i
			    for i in range(self.verticies_count   )
			    if i != self.source_index and i != self.sink_index
			]

			# move through list
			__lowercase											=     0
			while i < len(__lowerCamelCase   ):
					__lowercase											=     vertices_list[i]
					__lowercase											=     self.heights[vertex_index]
					self.process_vertex(__lowerCamelCase   )
					if self.heights[vertex_index] > previous_height:
							# if it was relabeled, swap elements
							# and start from 0 index
							vertices_list.insert(0					,      vertices_list.pop(__lowerCamelCase   )   )
							__lowercase											=     0
					else:
							i += 1

			__lowercase											=     sum(self.preflow[self.source_index]   )




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Optional[int]   )       -> Union[str, Any]:

			'''simple docstring'''




			while self.excesses[vertex_index] > 0:
					for neighbour_index in range(self.verticies_count   ):
							# if it's neighbour and current vertex is higher
							if (
							    self.graph[vertex_index][neighbour_index]
							    - self.preflow[vertex_index][neighbour_index]
							    > 0
							    and self.heights[vertex_index] > self.heights[neighbour_index]
							):
									self.push(__lowerCamelCase					,      __lowerCamelCase   )

					self.relabel(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     Union[str, Any]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     min(
			    self.excesses[from_index]					,      self.graph[from_index][to_index] - self.preflow[from_index][to_index]					,      )
			self.preflow[from_index][to_index] += preflow_delta
			self.preflow[to_index][from_index] -= preflow_delta
			self.excesses[from_index] -= preflow_delta
			self.excesses[to_index] += preflow_delta






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     int   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     None
			for to_index in range(self.verticies_count   ):
					if (
					    self.graph[vertex_index][to_index]
					    - self.preflow[vertex_index][to_index]
					    > 0
					) and (min_height is None or self.heights[to_index] < min_height):
							__lowercase											=     self.heights[to_index]

			if min_height is not None:
					__lowercase											=     min_height + 1


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   [0]
		SCREAMING_SNAKE_CASE_   :      Tuple			=   [3]
		# graph = [
		#     [0, 0, 4, 6, 0, 0],
		#     [0, 0, 5, 2, 0, 0],
		#     [0, 0, 0, 0, 4, 4],
		#     [0, 0, 0, 0, 6, 6],
		#     [0, 0, 0, 0, 0, 0],
		#     [0, 0, 0, 0, 0, 0],
		# ]
		SCREAMING_SNAKE_CASE_   :      str			=   [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		SCREAMING_SNAKE_CASE_   :      List[Any]			=   FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		SCREAMING_SNAKE_CASE_   :      Dict			=   flow_network.find_maximum_flow()

		print(F"""maximum flow is {maximum_flow}""")











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()
SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      Any			=   ['''model.decoder.embed_positions.weights''']



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		if "emb" in name:
				__lowercase											=     name.replace('emb'						,   'model.decoder.embed_tokens'							)
		if "transformer" in name:
				__lowercase											=     name.replace('transformer'						,   'model.decoder'							)
		if "cross_attention" in name:
				__lowercase											=     name.replace('cross_attention'						,   'encoder_attn'							)
		if "linear1" in name:
				__lowercase											=     name.replace('linear1'						,   'fc1'							)
		if "linear2" in name:
				__lowercase											=     name.replace('linear2'						,   'fc2'							)
		if "norm1" in name:
				__lowercase											=     name.replace('norm1'						,   'self_attn_layer_norm'							)
		if "norm_cross" in name:
				__lowercase											=     name.replace('norm_cross'						,   'encoder_attn_layer_norm'							)
		if "norm2" in name:
				__lowercase											=     name.replace('norm2'						,   'final_layer_norm'							)
		if "out_norm" in name:
				__lowercase											=     name.replace('out_norm'						,   'model.decoder.layer_norm'							)
		if "linears" in name:
				__lowercase											=     name.replace('linears'						,   'lm_heads'							)
		if "condition_provider.conditioners.description.output_proj" in name:
				__lowercase											=     name.replace('condition_provider.conditioners.description.output_proj'						,   'enc_to_dec_proj'							)
		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Tuple[Dict, Dict]:
		__lowercase											=     list(state_dict.keys()							)
		__lowercase											=     {}
		for key in keys:
				__lowercase											=     state_dict.pop(snake_case							)
				__lowercase											=     rename_keys(snake_case							)
				if "in_proj_weight" in key:
						# split fused qkv proj
						__lowercase											=     val[:hidden_size, :]
						__lowercase											=     val[hidden_size : 2 * hidden_size, :]
						__lowercase											=     val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						__lowercase											=     val
				else:
						__lowercase											=     val
		return state_dict, enc_dec_proj_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    MusicgenDecoderConfig:
		if checkpoint == "small":
				# default config values
				__lowercase											=     1_024
				__lowercase											=     24
				__lowercase											=     16
		elif checkpoint == "medium":
				__lowercase											=     1_536
				__lowercase											=     48
				__lowercase											=     24
		elif checkpoint == "large":
				__lowercase											=     2_048
				__lowercase											=     48
				__lowercase											=     32
		else:
				raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."							)
		__lowercase											=     MusicgenDecoderConfig(
		    hidden_size=snake_case						,   ffn_dim=hidden_size * 4						,   num_hidden_layers=snake_case						,   num_attention_heads=snake_case						,   )
		return config



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=None						,   snake_case="cpu"							)		->    Optional[int]:
		__lowercase											=     MusicGen.get_pretrained(snake_case						,   device=snake_case							)
		__lowercase											=     decoder_config_from_checkpoint(snake_case							)

		__lowercase											=     fairseq_model.lm.state_dict()
		__lowercase						,      __lowercase											=     rename_state_dict(
		    snake_case						,   hidden_size=decoder_config.hidden_size							)

		__lowercase											=     TaEncoderModel.from_pretrained('t5-base'							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_32khz'							)
		__lowercase											=     MusicgenForCausalLM(snake_case							).eval()

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

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

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

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

		# init the composite model
		__lowercase											=     MusicgenForConditionalGeneration(text_encoder=snake_case						,   audio_encoder=snake_case						,   decoder=snake_case							)

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

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

		with torch.no_grad():
				__lowercase											=     model(input_ids=snake_case						,   decoder_input_ids=snake_case							).logits

		if logits.shape != (8, 1, 2_048):
				raise ValueError('Incorrect shape for logits'							)

		# now construct the processor
		__lowercase											=     AutoTokenizer.from_pretrained('t5-base'							)
		__lowercase											=     AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'						,   padding_side='left'							)

		__lowercase											=     MusicgenProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)

		# set the appropriate bos/pad token ids
		__lowercase											=     2_048
		__lowercase											=     2_048

		# set other default generation config params
		__lowercase											=     int(30 * audio_encoder.config.frame_rate							)
		__lowercase											=     True
		__lowercase											=     3.0

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      Any			=   parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




