



'''simple docstring'''





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    List[str]  ,_snake_case	:    Optional[int]=None							):





    '''simple docstring'''


    assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
    lowercase__									=    nn.Parameter(_snake_case							)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
        lowercase__									=    nn.Parameter(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Any  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    np.asarray(weights[0]							)
    lowercase__									=    np.asarray(weights[1]							)
    lowercase__									=    np.asarray(weights[2]							)

    set_param(
        torch_layer.self_attention.query_key  ,torch.tensor(_snake_case							).transpose(1  ,2							).contiguous().view(-1  ,_snake_case							)  ,)
    set_param(
        torch_layer.self_attention.value  ,torch.tensor(_snake_case							).transpose(1  ,2							).contiguous().view(-1  ,_snake_case							)  ,)
    set_param(
        torch_layer.output.dense  ,torch.tensor(_snake_case							).view(-1  ,_snake_case							).contiguous().transpose(0  ,1							)  ,)




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Optional[Any]  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    np.asarray(weights[0]							)
    lowercase__									=    np.asarray(weights[1]							)
    lowercase__									=    np.asarray(weights[2]							)
    lowercase__									=    np.asarray(weights[3]							)

    set_param(
        torch_layer.self_attention.query  ,torch.tensor(_snake_case							).transpose(1  ,2							).contiguous().view(-1  ,_snake_case							)  ,)
    set_param(
        torch_layer.self_attention.key  ,torch.tensor(_snake_case							).transpose(1  ,2							).contiguous().view(-1  ,_snake_case							)  ,)
    set_param(
        torch_layer.self_attention.value  ,torch.tensor(_snake_case							).transpose(1  ,2							).contiguous().view(-1  ,_snake_case							)  ,)
    set_param(
        torch_layer.output.dense  ,torch.tensor(_snake_case							).view(-1  ,_snake_case							).contiguous().transpose(0  ,1							)  ,)




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    List[str]  ,_snake_case	:    List[Any]							):





    '''simple docstring'''


    lowercase__									=    weights[0][0][0]
    lowercase__									=    np.asarray(layer_norm_a[0]							)
    lowercase__									=    np.asarray(layer_norm_a[1]							)
    set_param(
        torch_block.attention.layer_norm  ,torch.tensor(_snake_case							)  ,torch.tensor(_snake_case							)  ,)

    # lsh weights + output
    lowercase__									=    weights[0][1]
    if len(_snake_case							) < 4:
        set_layer_weights_in_torch_lsh(_snake_case  ,torch_block.attention  ,_snake_case							)
    else:
        set_layer_weights_in_torch_local(_snake_case  ,torch_block.attention  ,_snake_case							)

    # intermediate weighs
    lowercase__									=    weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_snake_case							) == 4:
        lowercase__									=    intermediate_weights[2]

    # layernorm 2
    lowercase__									=    np.asarray(intermediate_weights[0][0]							)
    lowercase__									=    np.asarray(intermediate_weights[0][1]							)
    set_param(
        torch_block.feed_forward.layer_norm  ,torch.tensor(_snake_case							)  ,torch.tensor(_snake_case							)  ,)

    # intermediate dense
    lowercase__									=    np.asarray(intermediate_weights[1][0]							)
    lowercase__									=    np.asarray(intermediate_weights[1][1]							)
    set_param(
        torch_block.feed_forward.dense.dense  ,torch.tensor(_snake_case							).transpose(0  ,1							).contiguous()  ,torch.tensor(_snake_case							)  ,)

    # intermediate out
    lowercase__									=    np.asarray(intermediate_weights[4][0]							)
    lowercase__									=    np.asarray(intermediate_weights[4][1]							)
    set_param(
        torch_block.feed_forward.output.dense  ,torch.tensor(_snake_case							).transpose(0  ,1							).contiguous()  ,torch.tensor(_snake_case							)  ,)




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    List[str]  ,_snake_case	:    Optional[Any]							):





    '''simple docstring'''


    lowercase__									=    torch_model.reformer

    # word embeds
    lowercase__									=    np.asarray(weights[1]							)
    set_param(
        torch_model_reformer.embeddings.word_embeddings  ,torch.tensor(_snake_case							)  ,)

    if isinstance(weights[3]  ,_snake_case							):
        lowercase__									=    torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights							)							):
            lowercase__									=    np.asarray(weights[3][emb_idx][0]							)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), f'''{position_embeddings[emb_idx]} emb does not match'''
            lowercase__									=    nn.Parameter(torch.tensor(_snake_case							)							)

    lowercase__									=    weights[5]
    assert len(torch_model_reformer.encoder.layers							) * 4 == len(
        _snake_case							), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
        lowercase__									=    trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(_snake_case  ,_snake_case  ,_snake_case							)

    # output layer norm
    lowercase__									=    np.asarray(weights[7][0]							)
    lowercase__									=    np.asarray(weights[7][1]							)
    set_param(
        torch_model_reformer.encoder.layer_norm  ,torch.tensor(_snake_case							)  ,torch.tensor(_snake_case							)  ,)

    # output embeddings
    lowercase__									=    np.asarray(weights[9][0]							)
    lowercase__									=    np.asarray(weights[9][1]							)
    set_param(
        torch_model.lm_head.decoder  ,torch.tensor(_snake_case							).transpose(0  ,1							).contiguous()  ,torch.tensor(_snake_case							)  ,)




def 							lowerCamelCase	(					_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    ReformerConfig.from_json_file(_snake_case							)
    print(f'''Building PyTorch model from configuration: {config}'''							)
    lowercase__									=    ReformerModelWithLMHead(_snake_case							)

    with open(_snake_case  ,"rb"							) as f:
        lowercase__									=    pickle.load(_snake_case							)["weights"]

    set_model_weights_in_torch(_snake_case  ,_snake_case  ,config.hidden_size							)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
    torch.save(model.state_dict()  ,_snake_case							)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained Reformer model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''





from __future__ import annotations

import math




def 							lowerCamelCase	(					_snake_case	:    float  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    u
    for i in range(1  ,_snake_case							):
        lowercase__									=    temp * (u - i)
    return temp




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    int(input("enter the numbers of values: "							)							)
    lowercase__									=    []
    for _ in range(_snake_case							):
        y.append([]							)
    for i in range(_snake_case							):
        for j in range(_snake_case							):
            y[i].append(_snake_case							)
            lowercase__									=    0

    print("enter the values of parameters in a list: "							)
    lowercase__									=    list(map(_snake_case  ,input().split()							)							)

    print("enter the values of corresponding parameters: "							)
    for i in range(_snake_case							):
        lowercase__									=    float(input()							)

    lowercase__									=    int(input("enter the value to interpolate: "							)							)
    lowercase__									=    (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1  ,_snake_case							):
        for j in range(n - i							):
            lowercase__									=    y[j + 1][i - 1] - y[j][i - 1]

    lowercase__									=    y[0][0]
    for i in range(1  ,_snake_case							):
        summ += (ucal(_snake_case  ,_snake_case							) * y[0][i]) / math.factorial(_snake_case							)

    print(f'''the value at {value} is {summ}'''							)


if __name__ == "__main__":
 main()




