import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[
							    '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(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE				)






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							emb.weight.shape
							A__	     =							nn.Linear(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    bias=_SCREAMING_SNAKE_CASE				)
							A__	     =							emb.weight.data
							return lin_layer






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							remove_ignore_keys_(_SCREAMING_SNAKE_CASE				)
							rename_keys(_SCREAMING_SNAKE_CASE				)

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

							A__	     =							args.share_decoder_input_output_embed

							A__	     =							[int(_SCREAMING_SNAKE_CASE				) for i in args.conv_kernel_sizes.split(','				)]
							A__	     =							SpeechaTextConfig(
							    vocab_size=_SCREAMING_SNAKE_CASE       ,    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(_SCREAMING_SNAKE_CASE				)       ,    conv_channels=args.conv_channels       ,    conv_kernel_sizes=_SCREAMING_SNAKE_CASE       ,    input_feat_per_channel=args.input_feat_per_channel       ,    input_channels=args.input_channels       ,    tie_word_embeddings=_SCREAMING_SNAKE_CASE       ,    num_beams=5       ,    max_length=200       ,    use_cache=_SCREAMING_SNAKE_CASE       ,    decoder_start_token_id=2       ,    early_stopping=_SCREAMING_SNAKE_CASE       ,    )

							A__	     =							SpeechaTextForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
							A__     ,  A__	     =							model.model.load_state_dict(_SCREAMING_SNAKE_CASE       ,    strict=_SCREAMING_SNAKE_CASE				)
							if len(_SCREAMING_SNAKE_CASE				) > 0 and not set(_SCREAMING_SNAKE_CASE				) <= {
							    "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:
														A__	     =							make_linear_from_emb(model.model.decoder.embed_tokens				)
							else:
														A__	     =							lm_head_weights

							model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__lowerCamelCase											=  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											=  parser.parse_args()
				convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(*merge_sort(unsorted), sep=",")





