




'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					snake_case_		     =							[
					    '''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 						__magic_name__       (      __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							list(s_dict.keys()      )
					for key in keys:
										if "transformer_layers" in key:
															snake_case_		     =							s_dict.pop(__UpperCAmelCase      )
										elif "subsample" in key:
															snake_case_		     =							s_dict.pop(__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_     ,snake_case_		     =							emb.weight.shape
					snake_case_		     =							nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase      )
					snake_case_		     =							emb.weight.data
					return lin_layer


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							torch.load(__UpperCAmelCase, map_location='''cpu'''      )
					snake_case_		     =							mam_aaa['''args''']
					snake_case_		     =							mam_aaa['''model''']
					snake_case_		     =							state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase      )
					rename_keys(__UpperCAmelCase      )

					snake_case_		     =							state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_		     =							args.share_decoder_input_output_embed

					snake_case_		     =							[int(__UpperCAmelCase      ) for i in args.conv_kernel_sizes.split(''','''      )]
					snake_case_		     =							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=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )

					snake_case_		     =							SpeechaTextForConditionalGeneration(__UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							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_		     =							make_linear_from_emb(model.model.decoder.embed_tokens      )
					else:
										snake_case_		     =							lm_head_weights

					model.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
							a    :							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.')
							a    :							List[Any]										=    parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

