
"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					[
        '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(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					list(s_dict.keys() )
    for key in keys:
        if "transformer_layers" in key:
            UpperCAmelCase__	:      int     							=					s_dict.pop(_snake_case )
        elif "subsample" in key:
            UpperCAmelCase__	:      Union[str, Any]     							=					s_dict.pop(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      Optional[Any]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Tuple     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      List[str]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Union[str, Any]     							=					mam_aaa['args']
    UpperCAmelCase__	:      List[Any]     							=					mam_aaa['model']
    UpperCAmelCase__	:      str     							=					state_dict['decoder.output_projection.weight']

    remove_ignore_keys_(_snake_case )
    rename_keys(_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					state_dict['decoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      Optional[int]     							=					args.share_decoder_input_output_embed

    UpperCAmelCase__	:      Tuple     							=					[int(_snake_case ) for i in args.conv_kernel_sizes.split(',' )]
    UpperCAmelCase__	:      List[Any]     							=					SpeechaTextConfig(
        vocab_size=_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(_snake_case )    ,conv_channels=args.conv_channels    ,conv_kernel_sizes=_snake_case    ,input_feat_per_channel=args.input_feat_per_channel    ,input_channels=args.input_channels    ,tie_word_embeddings=_snake_case    ,num_beams=5    ,max_length=200    ,use_cache=_snake_case    ,decoder_start_token_id=2    ,early_stopping=_snake_case    ,)

    UpperCAmelCase__	:      Optional[Any]     							=					SpeechaTextForConditionalGeneration(_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    if len(_snake_case ) > 0 and not set(_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:
        UpperCAmelCase__	:      Any     							=					make_linear_from_emb(model.model.decoder.embed_tokens )
    else:
        UpperCAmelCase__	:      Union[str, Any]     							=					lm_head_weights

    model.save_pretrained(_snake_case )


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







from decimal import Decimal, getcontext
from math import ceil, factorial







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Undefined for non-integers''' )
     elif precision < 1:
          raise ValueError('''Undefined for non-natural numbers''' )

     UpperCAmelCase_     :				Tuple  =     precision
     UpperCAmelCase_     :				Optional[Any]  =     ceil(precision / 14 )
     UpperCAmelCase_     :				int  =     426880 * Decimal(10005 ).sqrt()
     UpperCAmelCase_     :				Tuple  =     1
     UpperCAmelCase_     :				List[Any]  =     13591409
     UpperCAmelCase_     :				Optional[Any]  =     Decimal(_lowercase )
     for k in range(1					,		_lowercase ):
          UpperCAmelCase_     :				List[str]  =     factorial(6 * k ) // (factorial(3 * k ) * factorial(_lowercase ) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
     return str(constant_term / partial_sum )[:-1]


if __name__ == "__main__":
     __a							     =					50
     print(F"""The first {n} digits of pi is: {pi(n)}""")