



'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
snake_case_			:     Dict  	=  logging.get_logger(__name__)

snake_case_			:     List[Any]  	=  {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
snake_case_			:     Union[str, Any]  	=  [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def UpperCamelCase   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):

						for attribute in key.split('.'							):
												_UpperCamelCase :		Any			    =       getattr(_lowerCamelCase  ,  _lowerCamelCase							)

						if weight_type is not None:
												_UpperCamelCase :		List[str]			    =       getattr(_lowerCamelCase  ,  _lowerCamelCase							).shape
						else:
												_UpperCamelCase :		Union[str, Any]			    =       hf_pointer.shape

						assert hf_shape == value.shape, (
						    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
						    f' {value.shape} for {full_name}'
						)

						if weight_type == "weight":
												_UpperCamelCase :		List[Any]			    =       value
						elif weight_type == "weight_g":
												_UpperCamelCase :		Union[str, Any]			    =       value
						elif weight_type == "weight_v":
												_UpperCamelCase :		Tuple			    =       value
						elif weight_type == "bias":
												_UpperCamelCase :		int			    =       value
						else:
												_UpperCamelCase :		Union[str, Any]			    =       value

						logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)






def UpperCamelCase   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       []
						_UpperCamelCase :		Tuple			    =       fairseq_model.state_dict()

						_UpperCamelCase :		Tuple			    =       hf_model.feature_extractor

						# if encoder has different dim to decoder -> use proj_weight
						_UpperCamelCase :		Any			    =       None

						for name, value in fairseq_dict.items():
												_UpperCamelCase :		Optional[Any]			    =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  hf_model.config.feat_extract_norm == 'group'  ,  )
																		_UpperCamelCase :		Optional[Any]			    =       True
												elif name.split('.'							)[0] == "proj":
																		_UpperCamelCase :		int			    =       fairseq_model.proj
																		_UpperCamelCase :		List[Any]			    =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
																														_UpperCamelCase :		int			    =       True
																														if "*" in mapped_key:
																																				_UpperCamelCase :		Dict			    =       name.split(_lowerCamelCase							)[0].split('.'							)[-2]
																																				_UpperCamelCase :		Dict			    =       mapped_key.replace('*'  ,  _lowerCamelCase							)
																														if "weight_g" in name:
																																				_UpperCamelCase :		Optional[Any]			    =       "weight_g"
																														elif "weight_v" in name:
																																				_UpperCamelCase :		Tuple			    =       "weight_v"
																														elif "bias" in name:
																																				_UpperCamelCase :		List[str]			    =       "bias"
																														elif "weight" in name:
																																				_UpperCamelCase :		Dict			    =       "weight"
																														else:
																																				_UpperCamelCase :		List[Any]			    =       None
																														set_recursively(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase							)
																								continue
												if not is_used:
																		unused_weights.append(_lowerCamelCase							)

						logger.warning(f'Unused weights: {unused_weights}'							)

						return proj_weight






def UpperCamelCase   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       full_name.split('conv_layers.'							)[-1]
						_UpperCamelCase :		List[str]			    =       name.split('.'							)
						_UpperCamelCase :		int			    =       int(items[0]							)
						_UpperCamelCase :		Any			    =       int(items[1]							)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																		)
																		_UpperCamelCase :		Dict			    =       value
																		logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																		)
																		_UpperCamelCase :		Union[str, Any]			    =       value
																		logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																		    " found."
																		)
																		_UpperCamelCase :		Union[str, Any]			    =       value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f'{full_name} has size {value.shape}, but'
																		    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																		)
																		_UpperCamelCase :		int			    =       value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
						else:
												unused_weights.append(_lowerCamelCase							)






def UpperCamelCase   (					UpperCAmelCase_							):
						_UpperCamelCase :		Optional[Any]			    =       emb.weight.shape
						_UpperCamelCase :		List[Any]			    =       nn.Linear(_lowerCamelCase  ,  _lowerCamelCase  ,  bias=_lowerCamelCase							)
						_UpperCamelCase :		Optional[int]			    =       emb.weight.data
						return lin_layer






def UpperCamelCase   (					UpperCAmelCase_							):

						with open(_lowerCamelCase  ,  'r'  ,  encoding='utf-8'							) as f:
												_UpperCamelCase :		str			    =       f.readlines()
												_UpperCamelCase :		int			    =       [line.split(' '							)[0] for line in lines]

						_UpperCamelCase :		Optional[int]			    =       len(_lowerCamelCase							)

						_UpperCamelCase :		Union[str, Any]			    =       {
						    "<s>": 0,
						    "<pad>": 1,
						    "</s>": 2,
						    "<unk>": 3,
						}

						vocab_dict.update(dict(zip(_lowerCamelCase  ,  range(4  ,  num_words + 4							)							)							)							)
						return vocab_dict






@torch.no_grad()
def UpperCamelCase   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  ):
						_UpperCamelCase :		str			    =       WavaVecaConfig.from_pretrained(_lowerCamelCase							)
						_UpperCamelCase :		Any			    =       SpeechaTextaConfig.from_pretrained(
						    _lowerCamelCase  ,  vocab_size=_lowerCamelCase  ,  decoder_layers=_lowerCamelCase  ,  do_stable_layer_norm=_lowerCamelCase							)

						_UpperCamelCase :		str			    =       WavaVecaFeatureExtractor(
						    feature_size=1  ,  sampling_rate=1_6_0_0_0  ,  padding_value=0  ,  do_normalize=_lowerCamelCase  ,  return_attention_mask=_lowerCamelCase  ,  )

						_UpperCamelCase :		Tuple			    =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]  ,  arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
						_UpperCamelCase :		Tuple			    =       model[0].eval()

						# set weights for wav2vec2 encoder
						_UpperCamelCase :		str			    =       WavaVecaModel(_lowerCamelCase							)
						_UpperCamelCase :		List[str]			    =       recursively_load_weights_wavaveca(model.encoder  ,  _lowerCamelCase							)

						_UpperCamelCase :		int			    =       SpeechaTextaForCausalLM(_lowerCamelCase							)
						_UpperCamelCase :		Optional[int]			    =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,  strict=_lowerCamelCase							)

						# set output linear layer
						unexpected_keys.remove('embed_out'							)
						_UpperCamelCase :		Union[str, Any]			    =       nn.Parameter(model.decoder.embed_out.detach()							)

						# layer norm is init to identity matrix so leaving it is fine
						logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'							)
						logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'							)

						_UpperCamelCase :		int			    =       SpeechEncoderDecoderModel(encoder=_lowerCamelCase  ,  decoder=_lowerCamelCase							)
						_UpperCamelCase :		List[str]			    =       False

						# add projection layer
						_UpperCamelCase :		str			    =       nn.Parameter(projection_layer.weight							)
						_UpperCamelCase :		int			    =       nn.Parameter(projection_layer.bias							)

						_UpperCamelCase :		int			    =       create_vocab_dict(_lowerCamelCase							)

						with open(os.path.join(_lowerCamelCase  ,  'vocab.json'							)  ,  'w'							) as fp:
												json.dump(_lowerCamelCase  ,  _lowerCamelCase							)

						_UpperCamelCase :		str			    =       SpeechaTextaTokenizer(os.path.join(_lowerCamelCase  ,  'vocab.json'							)							)
						tokenizer.save_pretrained(_lowerCamelCase							)

						_UpperCamelCase :		Tuple			    =       hf_wavavec.config.to_dict()
						_UpperCamelCase :		Any			    =       tokenizer.pad_token_id
						_UpperCamelCase :		List[Any]			    =       tokenizer.bos_token_id
						_UpperCamelCase :		List[str]			    =       tokenizer.eos_token_id
						_UpperCamelCase :		Dict			    =       "speech_to_text_2"
						_UpperCamelCase :		List[str]			    =       "wav2vec2"

						_UpperCamelCase :		Tuple			    =       SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase							)

						hf_wavavec.save_pretrained(_lowerCamelCase							)
						feature_extractor.save_pretrained(_lowerCamelCase							)


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
					parser.add_argument(
					    '--encoder_config_path',
					    default='facebook/wav2vec2-large-lv60',
					    type=str,
					    help='Path to hf encoder wav2vec2 checkpoint config',
					)
					parser.add_argument(
					    '--decoder_config_path',
					    default='facebook/s2t-small-mustc-en-fr-st',
					    type=str,
					    help='Path to hf decoder s2t checkpoint config',
					)
					parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
					parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

					snake_case_			:     List[Any]  	=  parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    vocab_size=args.vocab_size,
					    num_decoder_layers=args.num_decoder_layers,
					)







'''simple docstring'''
class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       None
							_UpperCamelCase :		List[Any]			    =       None
							_UpperCamelCase :		int			    =       graph

							self._normalize_graph(lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       len(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       None





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							if sources is int:
													_UpperCamelCase :		Optional[int]			    =       [sources]
							if sinks is int:
													_UpperCamelCase :		Union[str, Any]			    =       [sinks]

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

							_UpperCamelCase :		List[str]			    =       sources[0]
							_UpperCamelCase :		str			    =       sinks[0]

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

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

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





	def      UpperCamelCase_  (			self :    List[str]			):







							'''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 :    Any			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       algorithm(self			)






class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       flow_network
							_UpperCamelCase :		List[str]			    =       flow_network.verticesCount
							_UpperCamelCase :		List[str]			    =       flow_network.sourceIndex
							_UpperCamelCase :		Any			    =       flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							_UpperCamelCase :		List[Any]			    =       flow_network.graph
							_UpperCamelCase :		Any			    =       False





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							if not self.executed:
													self._algorithm()
													_UpperCamelCase :		Any			    =       True





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							pass






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)
							# use this to save your result
							_UpperCamelCase :		Tuple			    =       -1





	def      UpperCamelCase_  (			self :    Dict			):







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

							return self.maximum_flow






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       [[0] * self.verticies_count for i in range(self.verticies_count			)]

							_UpperCamelCase :		Optional[Any]			    =       [0] * self.verticies_count
							_UpperCamelCase :		List[str]			    =       [0] * self.verticies_count





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       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
							_UpperCamelCase :		List[str]			    =       [
							    i
							    for i in range(self.verticies_count			)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							_UpperCamelCase :		int			    =       0
							while i < len(lowerCamelCase__			):
													_UpperCamelCase :		List[Any]			    =       vertices_list[i]
													_UpperCamelCase :		str			    =       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__			)			)
																			_UpperCamelCase :		Dict			    =       0
													else:
																			i += 1

							_UpperCamelCase :		Optional[Any]			    =       sum(self.preflow[self.source_index]			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Dict			):







							'''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 :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       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 :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       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):
																			_UpperCamelCase :		List[Any]			    =       self.heights[to_index]

							if min_height is not None:
													_UpperCamelCase :		Any			    =       min_height + 1


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  [0]
					snake_case_			:     int  	=  [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],
					# ]
					snake_case_			:     int  	=  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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


