



"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """encoder.embed_positions._float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(__UpperCamelCase					, __UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			list(s_dict.keys()    )
						for key in keys:
												if "transformer_layers" in key:
																		snake_case_   : Tuple						=			s_dict.pop(__UpperCamelCase    )
												elif "subsample" in key:
																		snake_case_   : Dict						=			s_dict.pop(__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : Optional[int]						=			emb.weight.shape
						snake_case_   : List[Any]						=			nn.Linear(__UpperCamelCase					, __UpperCamelCase					, bias=__UpperCamelCase    )
						snake_case_   : str						=			emb.weight.data
						return lin_layer
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : int						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )
						snake_case_   : Optional[int]						=			mam_aaa["""args"""]
						snake_case_   : int						=			mam_aaa["""model"""]
						snake_case_   : List[str]						=			state_dict["""decoder.output_projection.weight"""]

						remove_ignore_keys_(__UpperCamelCase    )
						rename_keys(__UpperCamelCase    )

						snake_case_   : Tuple						=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

						snake_case_   : List[str]						=			args.share_decoder_input_output_embed

						snake_case_   : int						=			[int(__UpperCamelCase    ) for i in args.conv_kernel_sizes.split(""","""    )]
						snake_case_   : Optional[Any]						=			SpeechaTextConfig(
						    vocab_size=__UpperCamelCase					, max_source_positions=args.max_source_positions					, max_target_positions=args.max_target_positions					, encoder_layers=args.encoder_layers					, decoder_layers=args.decoder_layers					, encoder_attention_heads=args.encoder_attention_heads					, decoder_attention_heads=args.decoder_attention_heads					, encoder_ffn_dim=args.encoder_ffn_embed_dim					, decoder_ffn_dim=args.decoder_ffn_embed_dim					, d_model=args.encoder_embed_dim					, dropout=args.dropout					, attention_dropout=args.attention_dropout					, activation_dropout=args.activation_dropout					, activation_function="""relu"""					, num_conv_layers=len(__UpperCamelCase    )					, conv_channels=args.conv_channels					, conv_kernel_sizes=__UpperCamelCase					, input_feat_per_channel=args.input_feat_per_channel					, input_channels=args.input_channels					, tie_word_embeddings=__UpperCamelCase					, num_beams=5					, max_length=2_0_0					, use_cache=__UpperCamelCase					, decoder_start_token_id=2					, early_stopping=__UpperCamelCase					, )

						snake_case_   : int						=			SpeechaTextForConditionalGeneration(__UpperCamelCase    )
						snake_case_			,		snake_case_   : Dict						=			model.model.load_state_dict(__UpperCamelCase					, strict=__UpperCamelCase    )
						if len(__UpperCamelCase    ) > 0 and not set(__UpperCamelCase    ) <= {
						    "encoder.embed_positions.weights",
						    "decoder.embed_positions.weights",
						}:
												raise ValueError(
												    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
												    F' but all the following weights are missing {missing}'    )

						if tie_embeds:
												snake_case_   : Optional[int]						=			make_linear_from_emb(model.model.decoder.embed_tokens    )
						else:
												snake_case_   : Dict						=			lm_head_weights

						model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					__lowerCAmelCase       :					List[str]      					=					parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Any						=			1  # To kept the Calculated Value
						# Since C(n, k) = C(n, n-k)
						if k > (n - k):
												snake_case_   : Optional[int]						=			n - k
						# Calculate C(n,k)
						for i in range(__UpperCamelCase    ):
												result *= n - i
												result //= i + 1
						return result
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return binomial_coefficient(2 * node_count					, __UpperCamelCase    ) // (node_count + 1)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""    )
						snake_case_   : Optional[int]						=			1
						for i in range(1					, n + 1    ):
												result *= i
						return result
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return catalan_number(__UpperCamelCase    ) * factorial(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[Any]      					=					int(input('''Enter the number of nodes: ''').strip() or 0)
					if node_count <= 0:
										raise ValueError('''We need some nodes to work with.''')
					print(
					    F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
					    F'''binary trees and {catalan_number(node_count)} binary search trees.'''
					)

