
"""simple docstring"""





class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    name
							_UpperCamelCase									=    val



		def __str__(   self)			->			Optional[int]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.val})'''




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






							'''simple docstring'''
							return self.val < other.val






class        _UpperCAmelCase:



		def __init__(   self							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    {}
							_UpperCamelCase									=    {}
							_UpperCamelCase									=    self.build_heap(__a)



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






							'''simple docstring'''
							return self.get_value(__a)



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






							'''simple docstring'''
							return (idx - 1) // 2



		def 		UpperCAmelCase				(   self							,						__a)			->			Any:






							'''simple docstring'''
							return idx * 2 + 1



		def 		UpperCAmelCase				(   self							,						__a)			->			int:






							'''simple docstring'''
							return idx * 2 + 2



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							return self.heap_dict[key]



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    len(__a) - 1
							_UpperCamelCase									=    self.get_parent_idx(__a)

							for idx, i in enumerate(__a):
												_UpperCamelCase									=    idx
												_UpperCamelCase									=    i.val

							for i in range(__a							,						-1							,						-1):
												self.sift_down(__a							,						__a)
							return array



		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			int:






							'''simple docstring'''
							while True:
												_UpperCamelCase									=    self.get_left_child_idx(__a)  # noqa: E741
												_UpperCamelCase									=    self.get_right_child_idx(__a)

												_UpperCamelCase									=    idx
												if l < len(__a) and array[l] < array[idx]:
																	_UpperCamelCase									=    l
												if r < len(__a) and array[r] < array[smallest]:
																	_UpperCamelCase									=    r

												if smallest != idx:
																	_UpperCamelCase					,    _UpperCamelCase									=    array[smallest], array[idx]
																	(
																	    (
																	    _UpperCamelCase
																	)					,    (
																	    _UpperCamelCase
																	)					,    
																	)									=    (
																	    self.idx_of_element[array[smallest]],
																	    self.idx_of_element[array[idx]],
																	)
																	_UpperCamelCase									=    smallest
												else:
																	break



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_parent_idx(__a)
							while p >= 0 and self.heap[p] > self.heap[idx]:
												_UpperCamelCase					,    _UpperCamelCase									=    self.heap[idx], self.heap[p]
												_UpperCamelCase					,    _UpperCamelCase									=    (
												    self.idx_of_element[self.heap[idx]],
												    self.idx_of_element[self.heap[p]],
												)
												_UpperCamelCase									=    p
												_UpperCamelCase									=    self.get_parent_idx(__a)



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






							'''simple docstring'''
							return self.heap[0]



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.heap[-1], self.heap[0]
							_UpperCamelCase					,    _UpperCamelCase									=    (
							    self.idx_of_element[self.heap[-1]],
							    self.idx_of_element[self.heap[0]],
							)

							_UpperCamelCase									=    self.heap.pop()
							del self.idx_of_element[x]
							self.sift_down(0							,						self.heap)
							return x



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							self.heap.append(__a)
							_UpperCamelCase									=    len(self.heap) - 1
							_UpperCamelCase									=    node.val
							self.sift_up(len(self.heap) - 1)



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






							'''simple docstring'''
							return len(self.heap) == 0




		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							assert (
							    self.heap[self.idx_of_element[node]].val > new_value
							), "newValue must be less that current value"
							_UpperCamelCase									=    new_value
							_UpperCamelCase									=    new_value
							self.sift_up(self.idx_of_element[node])





_a								=						Node("""R""", -1)
_a								=						Node("""B""", 6)
_a								=						Node("""A""", 3)
_a								=						Node("""X""", 1)
_a								=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_a								=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
				print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
				print(i)

if __name__ == "__main__":
				import doctest

				doctest.testmod()








"""simple docstring"""





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_a								=						logging.get_logger(__name__)

_a								=						{
    """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""",
}
_a								=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





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

					if weight_type is not None:
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      ).shape
					else:
										_UpperCamelCase									=    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									=    value
					elif weight_type == "weight_g":
										_UpperCamelCase									=    value
					elif weight_type == "weight_v":
										_UpperCamelCase									=    value
					elif weight_type == "bias":
										_UpperCamelCase									=    value
					else:
										_UpperCamelCase									=    value

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    fairseq_model.state_dict()

					_UpperCamelCase									=    hf_model.feature_extractor
					_UpperCamelCase									=    hf_model.adapter

					for name, value in fairseq_dict.items():
										_UpperCamelCase									=    False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case,	__snake_case,	__snake_case,	__snake_case,	hf_model.config.feat_extract_norm == '''group''',	)
															_UpperCamelCase									=    True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']      ):
															load_adapter(__snake_case,	__snake_case,	__snake_case,	__snake_case      )
															_UpperCamelCase									=    True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
																									_UpperCamelCase									=    True
																									if "*" in mapped_key:
																														_UpperCamelCase									=    name.split(__snake_case      )[0].split('''.'''      )[-2]
																														_UpperCamelCase									=    mapped_key.replace('''*''',	__snake_case      )
																									if "weight_g" in name:
																														_UpperCamelCase									=    '''weight_g'''
																									elif "weight_v" in name:
																														_UpperCamelCase									=    '''weight_v'''
																									elif "bias" in name:
																														_UpperCamelCase									=    '''bias'''
																									elif "weight" in name:
																														_UpperCamelCase									=    '''weight'''
																									else:
																														_UpperCamelCase									=    None
																									set_recursively(__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																				continue
										if not is_used:
															unused_weights.append(__snake_case      )

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''conv_layers.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )
					_UpperCamelCase									=    int(items[0]      )
					_UpperCamelCase									=    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									=    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									=    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									=    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									=    value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''adaptor.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )

					if items[1].isdigit():
										_UpperCamelCase									=    int(items[1]      )
					else:
										_UpperCamelCase									=    None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
					elif isinstance(__snake_case,	__snake_case      ):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	)				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    WavaVecaConfig.from_pretrained(
					    __snake_case,	add_adapter=__snake_case,	adapter_stride=__snake_case,	adapter_kernel_size=__snake_case,	use_auth_token=__snake_case,	output_hidden_size=__snake_case,	)
					_UpperCamelCase									=    MBartConfig.from_pretrained(__snake_case      )

					# load model
					_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path],	arg_overrides={
					        '''config_yaml''': config_yaml_path,
					        '''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      ),
					        '''w2v_path''': checkpoint_path,
					        '''load_pretrained_decoder_from''': None,
					    },	)
					_UpperCamelCase									=    model[0].eval()

					# load feature extractor
					_UpperCamelCase									=    WavaVecaFeatureExtractor.from_pretrained(__snake_case,	use_auth_token=__snake_case      )

					# set weights for wav2vec2 encoder
					_UpperCamelCase									=    WavaVecaModel(__snake_case      )

					recursively_load_weights_wavaveca(model.encoder,	__snake_case      )

					# load decoder weights
					_UpperCamelCase									=    MBartForCausalLM(__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),	strict=__snake_case      )
					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									=    SpeechEncoderDecoderModel(encoder=__snake_case,	decoder=__snake_case      )
					_UpperCamelCase									=    False

					_UpperCamelCase									=    MBartaaTokenizer(__snake_case      )
					tokenizer.save_pretrained(__snake_case      )

					_UpperCamelCase									=    hf_wavavec.config.to_dict()
					_UpperCamelCase									=    tokenizer.pad_token_id
					_UpperCamelCase									=    tokenizer.bos_token_id
					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    '''mbart50'''
					_UpperCamelCase									=    '''wav2vec2'''

					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    25_00_04
					_UpperCamelCase									=    tokenizer.eos_token_id

					_UpperCamelCase									=    SpeechEncoderDecoderConfig.from_dict(__snake_case      )

					hf_wavavec.save_pretrained(__snake_case      )
					feature_extractor.save_pretrained(__snake_case      )


if __name__ == "__main__":
				_a								=						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("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-xls-r-1b""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/mbart-large-50-one-to-many-mmt""",
				    type=str,
				    help="""Path to hf decoder checkpoint config""",
				)
				parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
				parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
				parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
				parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

				_a								=						parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)






