






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()
lowerCamelCase			:					Dict         =	logging.get_logger(__name__)

lowerCamelCase			:					int         =	{
    '''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''',
}
lowerCamelCase			:					List[str]         =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
					for attribute in key.split("."       ):
										__lowerCAmelCase								=					getattr(__snake_case					, __snake_case       )

					if weight_type is not None:
										__lowerCAmelCase								=					getattr(__snake_case					, __snake_case       ).shape
					else:
										__lowerCAmelCase								=					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":
										__lowerCAmelCase								=					value
					elif weight_type == "weight_g":
										__lowerCAmelCase								=					value
					elif weight_type == "weight_v":
										__lowerCAmelCase								=					value
					elif weight_type == "bias":
										__lowerCAmelCase								=					value
					else:
										__lowerCAmelCase								=					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       ):
					__lowerCAmelCase								=					[]
					__lowerCAmelCase								=					fairseq_model.state_dict()

					__lowerCAmelCase								=					hf_model.feature_extractor
					__lowerCAmelCase								=					hf_model.adapter

					for name, value in fairseq_dict.items():
										__lowerCAmelCase								=					False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case					, __snake_case					, __snake_case					, __snake_case					, hf_model.config.feat_extract_norm == "group"					, )
															__lowerCAmelCase								=					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       )
															__lowerCAmelCase								=					True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
																									__lowerCAmelCase								=					True
																									if "*" in mapped_key:
																														__lowerCAmelCase								=					name.split(__snake_case       )[0].split("."       )[-2]
																														__lowerCAmelCase								=					mapped_key.replace("*"					, __snake_case       )
																									if "weight_g" in name:
																														__lowerCAmelCase								=					"weight_g"
																									elif "weight_v" in name:
																														__lowerCAmelCase								=					"weight_v"
																									elif "bias" in name:
																														__lowerCAmelCase								=					"bias"
																									elif "weight" in name:
																														__lowerCAmelCase								=					"weight"
																									else:
																														__lowerCAmelCase								=					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       ):
					__lowerCAmelCase								=					full_name.split("conv_layers."       )[-1]
					__lowerCAmelCase								=					name.split("."       )
					__lowerCAmelCase								=					int(items[0]       )
					__lowerCAmelCase								=					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."""
															)
															__lowerCAmelCase								=					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."""
															)
															__lowerCAmelCase								=					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."
															)
															__lowerCAmelCase								=					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."""
															)
															__lowerCAmelCase								=					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       ):
					__lowerCAmelCase								=					full_name.split("adaptor."       )[-1]
					__lowerCAmelCase								=					name.split("."       )

					if items[1].isdigit():
										__lowerCAmelCase								=					int(items[1]       )
					else:
										__lowerCAmelCase								=					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."""
																				__lowerCAmelCase								=					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."""
																				__lowerCAmelCase								=					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."""
																				__lowerCAmelCase								=					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."""
																				__lowerCAmelCase								=					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."""
															__lowerCAmelCase								=					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."""
															__lowerCAmelCase								=					value
															logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
					else:
										unused_weights.append(__snake_case       )





def       __lowerCAmelCase      (						__snake_case       ):
					__lowerCAmelCase			, __lowerCAmelCase								=					emb.weight.shape
					__lowerCAmelCase								=					nn.Linear(__snake_case					, __snake_case					, bias=__snake_case       )
					__lowerCAmelCase								=					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					, ):
					__lowerCAmelCase								=					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					, )
					__lowerCAmelCase								=					MBartConfig.from_pretrained(__snake_case       )

					# load model
					__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					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,
					    }					, )
					__lowerCAmelCase								=					model[0].eval()

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

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

					recursively_load_weights_wavaveca(model.encoder					, __snake_case       )

					# load decoder weights
					__lowerCAmelCase								=					MBartForCausalLM(__snake_case       )
					__lowerCAmelCase			, __lowerCAmelCase								=					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}"""       )

					__lowerCAmelCase								=					SpeechEncoderDecoderModel(encoder=__snake_case					, decoder=__snake_case       )
					__lowerCAmelCase								=					False

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

					__lowerCAmelCase								=					hf_wavavec.config.to_dict()
					__lowerCAmelCase								=					tokenizer.pad_token_id
					__lowerCAmelCase								=					tokenizer.bos_token_id
					__lowerCAmelCase								=					tokenizer.eos_token_id
					__lowerCAmelCase								=					"mbart50"
					__lowerCAmelCase								=					"wav2vec2"

					__lowerCAmelCase								=					tokenizer.eos_token_id
					__lowerCAmelCase								=					25_0004
					__lowerCAmelCase								=					tokenizer.eos_token_id

					__lowerCAmelCase								=					SpeechEncoderDecoderConfig.from_dict(__snake_case       )

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


if __name__ == "__main__":
	lowerCamelCase			:					Dict         =	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=250004, type=int, help='''`decoder_start_token_id` of model config''')

	lowerCamelCase			:					List[str]         =	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,
	)














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


if __name__ == "__main__":
 lowerCamelCase			:					List[str]         =	input('''Enter numbers separated by a comma:\n''').strip()
 lowerCamelCase			:					Tuple         =	[int(item) for item in user_input.split(''',''')]
 print(pancake_sort(unsorted))






