





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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_=None   ):
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
       _lowerCamelCase				:  str						=		nn.Parameter(lowercase_   )
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
              _lowerCamelCase				:  str						=		nn.Parameter(lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       # set torch weights for 1-to-1 comparison
       _lowerCamelCase				:  int						=		np.asarray(weights[0]   )
       _lowerCamelCase				:  Tuple						=		np.asarray(weights[1]   )
       _lowerCamelCase				:  int						=		np.asarray(weights[2]   )

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




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       # set torch weights for 1-to-1 comparison
       _lowerCamelCase				:  List[Any]						=		np.asarray(weights[0]   )
       _lowerCamelCase				:  Optional[Any]						=		np.asarray(weights[1]   )
       _lowerCamelCase				:  Optional[Any]						=		np.asarray(weights[2]   )
       _lowerCamelCase				:  List[str]						=		np.asarray(weights[3]   )

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




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       # layernorm 1
       _lowerCamelCase				:  Optional[int]						=		weights[0][0][0]
       _lowerCamelCase				:  List[Any]						=		np.asarray(layer_norm_a[0]   )
       _lowerCamelCase				:  Union[str, Any]						=		np.asarray(layer_norm_a[1]   )
       set_param(
           torch_block.attention.layer_norm			,     torch.tensor(lowercase_   )			,     torch.tensor(lowercase_   )			,     )

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

       # intermediate weighs
       _lowerCamelCase				:  Union[str, Any]						=		weights[2][0][1][2]

       # Chunked Feed Forward
       if len(lowercase_   ) == 4:
              _lowerCamelCase				:  Any						=		intermediate_weights[2]

       # layernorm 2
       _lowerCamelCase				:  int						=		np.asarray(intermediate_weights[0][0]   )
       _lowerCamelCase				:  List[Any]						=		np.asarray(intermediate_weights[0][1]   )
       set_param(
           torch_block.feed_forward.layer_norm			,     torch.tensor(lowercase_   )			,     torch.tensor(lowercase_   )			,     )

       # intermediate dense
       _lowerCamelCase				:  Any						=		np.asarray(intermediate_weights[1][0]   )
       _lowerCamelCase				:  Tuple						=		np.asarray(intermediate_weights[1][1]   )
       set_param(
           torch_block.feed_forward.dense.dense			,     torch.tensor(lowercase_   ).transpose(0			,     1   ).contiguous()			,     torch.tensor(lowercase_   )			,     )

       # intermediate out
       _lowerCamelCase				:  Optional[Any]						=		np.asarray(intermediate_weights[4][0]   )
       _lowerCamelCase				:  Tuple						=		np.asarray(intermediate_weights[4][1]   )
       set_param(
           torch_block.feed_forward.output.dense			,     torch.tensor(lowercase_   ).transpose(0			,     1   ).contiguous()			,     torch.tensor(lowercase_   )			,     )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       # reformer model
       _lowerCamelCase				:  Tuple						=		torch_model.reformer

       # word embeds
       _lowerCamelCase				:  Any						=		np.asarray(weights[1]   )
       set_param(
           torch_model_reformer.embeddings.word_embeddings			,     torch.tensor(lowercase_   )			,     )

       if isinstance(weights[3]			,     lowercase_   ):
              _lowerCamelCase				:  Union[str, Any]						=		torch_model_reformer.embeddings.position_embeddings
              for emb_idx in range(len(position_embeddings.weights   )   ):
                     _lowerCamelCase				:  Dict						=		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"""
                     _lowerCamelCase				:  Tuple						=		nn.Parameter(torch.tensor(lowercase_   )   )

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

       # output layer norm
       _lowerCamelCase				:  str						=		np.asarray(weights[7][0]   )
       _lowerCamelCase				:  Union[str, Any]						=		np.asarray(weights[7][1]   )
       set_param(
           torch_model_reformer.encoder.layer_norm			,     torch.tensor(lowercase_   )			,     torch.tensor(lowercase_   )			,     )

       # output embeddings
       _lowerCamelCase				:  List[Any]						=		np.asarray(weights[9][0]   )
       _lowerCamelCase				:  List[Any]						=		np.asarray(weights[9][1]   )
       set_param(
           torch_model.lm_head.decoder			,     torch.tensor(lowercase_   ).transpose(0			,     1   ).contiguous()			,     torch.tensor(lowercase_   )			,     )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       # Initialise PyTorch model
       _lowerCamelCase				:  List[str]						=		ReformerConfig.from_json_file(lowercase_   )
       print(F"""Building PyTorch model from configuration: {config}"""   )
       _lowerCamelCase				:  Tuple						=		ReformerModelWithLMHead(lowercase_   )

       with open(lowercase_			,     '''rb'''   ) as f:
              _lowerCamelCase				:  int						=		pickle.load(lowercase_   )['''weights''']

       set_model_weights_in_torch(lowercase_			,     lowercase_			,     config.hidden_size   )

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
       torch.save(model.state_dict()			,     lowercase_   )


if __name__ == "__main__":
       _lowerCamelCase      							=   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.'
       )
       _lowerCamelCase      							=   parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








from __future__ import annotations

from scipy.special import comb  # type: ignore







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             _lowerCamelCase				:  List[Any]						=		len(a__) - 1


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."
             _lowerCamelCase				:  list[float]						=		[]
             for i in range(len(self.list_of_points)):
                    # basis function for each i
                    output_values.append(
                        comb(self.degree   ,				a__) * ((1 - t) ** (self.degree - i)) * (t**i))
             # the basis must sum up to 1 for it to produce a valid Bezier curve.
             assert round(sum(a__)   ,				5) == 1
             return output_values


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."

             _lowerCamelCase				:  Any						=		self.basis_function(a__)
             _lowerCamelCase				:  List[Any]						=		0.0
             _lowerCamelCase				:  List[Any]						=		0.0
             for i in range(len(self.list_of_points)):
                    # For all points, sum up the product of i-th basis function and i-th point.
                    x += basis_function[i] * self.list_of_points[i][0]
                    y += basis_function[i] * self.list_of_points[i][1]
             return (x, y)



      def 					__snake_case      (		self   ,				a__ = 0.01):



             """simple docstring"""





             from matplotlib import pyplot as plt  # type: ignore

             _lowerCamelCase				:  list[float]						=		[]  # x coordinates of points to plot
             _lowerCamelCase				:  list[float]						=		[]  # y coordinates of points to plot

             _lowerCamelCase				:  List[Any]						=		0.0
             while t <= 1:
                    _lowerCamelCase				:  Tuple						=		self.bezier_curve_function(a__)
                    to_plot_x.append(value[0])
                    to_plot_y.append(value[1])
                    t += step_size

             _lowerCamelCase				:  List[Any]						=		[i[0] for i in self.list_of_points]
             _lowerCamelCase				:  Union[str, Any]						=		[i[1] for i in self.list_of_points]

             plt.plot(
                 a__   ,				a__   ,				color='''blue'''   ,				label='''Curve of Degree ''' + str(self.degree)   ,				)
             plt.scatter(a__   ,				a__   ,				color='''red'''   ,				label='''Control Points''')
             plt.legend()
             plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

