
def 			UpperCamelCase					(    __lowerCamelCase     : int = 50    ):
       snake_case       :   str							=      [1] * (length + 1)

       for row_length in range(length + 1    ):
              for tile_length in range(2				,					5    ):
                     for tile_start in range(row_length - tile_length + 1    ):
                            ways_number[row_length] += ways_number[
                                row_length - tile_start - tile_length
                            ]

       return ways_number[length]


if __name__ == "__main__":
   print(F'{solution() = }')







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()
__snake_case								=  logging.get_logger(__name__)

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					for attribute in key.split('.'		):
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		)

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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE__													= hf_model.feature_extractor
					SCREAMING_SNAKE_CASE__													= hf_model.adapter

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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


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

					if items[1].isdigit():
										SCREAMING_SNAKE_CASE__													= int(items[1]		)
					else:
										SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= 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.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer weight was initialized from {full_name}.'		)
					elif isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										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.'
															SCREAMING_SNAKE_CASE__													= 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.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= emb.weight.shape
					SCREAMING_SNAKE_CASE__													= nn.Linear(UpperCamelCase_	,	UpperCamelCase_	,	bias=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= emb.weight.data
					return lin_layer






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= WavaVecaConfig.from_pretrained(
					    UpperCamelCase_	,	add_adapter=UpperCamelCase_	,	adapter_stride=UpperCamelCase_	,	adapter_kernel_size=UpperCamelCase_	,	use_auth_token=UpperCamelCase_	,	output_hidden_size=UpperCamelCase_	,	)
					SCREAMING_SNAKE_CASE__													= MBartConfig.from_pretrained(UpperCamelCase_		)

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

					# load feature extractor
					SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_	,	use_auth_token=UpperCamelCase_		)

					# set weights for wav2vec2 encoder
					SCREAMING_SNAKE_CASE__													= WavaVecaModel(UpperCamelCase_		)

					recursively_load_weights_wavaveca(model.encoder	,	UpperCamelCase_		)

					# load decoder weights
					SCREAMING_SNAKE_CASE__													= MBartForCausalLM(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,	strict=UpperCamelCase_		)
					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}'		)

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderModel(encoder=UpperCamelCase_	,	decoder=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= False

					SCREAMING_SNAKE_CASE__													= MBartaaTokenizer(UpperCamelCase_		)
					tokenizer.save_pretrained(UpperCamelCase_		)

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

					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 250004
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_		)

					hf_wavavec.save_pretrained(UpperCamelCase_		)
					feature_extractor.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
				__snake_case								=  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=10_24, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

				__snake_case								=  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,
				)





