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  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:Optional[Any]   ,	snake_case__			:int=None)						->   Optional[int]:
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
       _A        =  nn.Parameter(snake_case__)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
              _A        =  nn.Parameter(snake_case__)






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:int   ,	snake_case__			:Optional[Any])						->   Union[str, Any]:
       # set torch weights for 1-to-1 comparison
       _A        =  np.asarray(weights[0])
       _A        =  np.asarray(weights[1])
       _A        =  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  snake_case  (       snake_case__			:Any   ,	snake_case__			:Tuple   ,	snake_case__			:Dict)						->   Any:
       # set torch weights for 1-to-1 comparison
       _A        =  np.asarray(weights[0])
       _A        =  np.asarray(weights[1])
       _A        =  np.asarray(weights[2])
       _A        =  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  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:Any   ,	snake_case__			:Optional[Any])						->   Optional[Any]:
       # layernorm 1
       _A        =  weights[0][0][0]
       _A        =  np.asarray(layer_norm_a[0])
       _A        =  np.asarray(layer_norm_a[1])
       set_param(
           torch_block.attention.layer_norm   ,	torch.tensor(snake_case__)   ,	torch.tensor(snake_case__)   ,	)

       # lsh weights + output
       _A        =  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
       _A        =  weights[2][0][1][2]

       # Chunked Feed Forward
       if len(snake_case__) == 4:
              _A        =  intermediate_weights[2]

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

       # intermediate dense
       _A        =  np.asarray(intermediate_weights[1][0])
       _A        =  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
       _A        =  np.asarray(intermediate_weights[4][0])
       _A        =  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  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:List[str]   ,	snake_case__			:List[Any])						->   Optional[int]:
       # reformer model
       _A        =  torch_model.reformer

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

       if isinstance(weights[3]   ,	snake_case__):
              _A        =  torch_model_reformer.embeddings.position_embeddings
              for emb_idx in range(len(position_embeddings.weights)):
                     _A        =  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'''
                     _A        =  nn.Parameter(torch.tensor(snake_case__))

       _A        =  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):
              _A        =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
              set_block_weights_in_torch(snake_case__   ,	snake_case__   ,	snake_case__)

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

       # output embeddings
       _A        =  np.asarray(weights[9][0])
       _A        =  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  snake_case  (       snake_case__			:Dict   ,	snake_case__			:Dict   ,	snake_case__			:Union[str, Any])						->   Optional[Any]:
       # Initialise PyTorch model
       _A        =  ReformerConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  ReformerModelWithLMHead(snake_case__)

       with open(snake_case__   ,	"""rb""") as f:
              _A        =  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)





from math import isqrt, loga






def  snake_case  (       snake_case__			:int)						->   list[int]:
       _A        =  [True] * max_number
       for i in range(2   ,	isqrt(max_number - 1) + 1):
              if is_prime[i]:
                     for j in range(i**2   ,	snake_case__   ,	snake_case__):
                            _A        =  False

       return [i for i in range(2   ,	snake_case__) if is_prime[i]]






def  snake_case  (       snake_case__			:int = 800_800   ,	snake_case__			:int = 800_800)						->   int:
       _A        =  degree * loga(snake_case__)
       _A        =  int(snake_case__)
       _A        =  calculate_prime_numbers(snake_case__)

       _A        =  0
       _A        =  0
       _A        =  len(snake_case__) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left])
                  + prime_numbers[left] * loga(prime_numbers[right])
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
    print(F'''{solution() = }''')




