


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    lowercase_	(	_A    :					Optional[int]					,      _A    :					int					,      _A    :					Optional[int]=None		):

      """simple docstring"""




      assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
      lowerCamelCase__	:       List[Any]					=						nn.Parameter(_A		)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
            lowerCamelCase__	:       Any					=						nn.Parameter(_A		)

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Union[str, Any]					,      _A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[0]		)
      lowerCamelCase__	:       Dict					=						np.asarray(weights[1]		)
      lowerCamelCase__	:       Optional[int]					=						np.asarray(weights[2]		)

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

def    lowercase_	(	_A    :					Dict					,      _A    :					Tuple					,      _A    :					Optional[int]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						np.asarray(weights[0]		)
      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[1]		)
      lowerCamelCase__	:       str					=						np.asarray(weights[2]		)
      lowerCamelCase__	:       Optional[int]					=						np.asarray(weights[3]		)

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

def    lowercase_	(	_A    :					Tuple					,      _A    :					str					,      _A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						weights[0][0][0]
      lowerCamelCase__	:       str					=						np.asarray(layer_norm_a[0]		)
      lowerCamelCase__	:       Any					=						np.asarray(layer_norm_a[1]		)
      set_param(
          torch_block.attention.layer_norm					,      torch.tensor(_A		)					,      torch.tensor(_A		)					,      )

      # lsh weights + output
      lowerCamelCase__	:       Union[str, Any]					=						weights[0][1]
      if len(_A		) < 4:
            set_layer_weights_in_torch_lsh(_A					,      torch_block.attention					,      _A		)
      else:
            set_layer_weights_in_torch_local(_A					,      torch_block.attention					,      _A		)

      # intermediate weighs
      lowerCamelCase__	:       Dict					=						weights[2][0][1][2]

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

      # layernorm 2
      lowerCamelCase__	:       List[str]					=						np.asarray(intermediate_weights[0][0]		)
      lowerCamelCase__	:       Union[str, Any]					=						np.asarray(intermediate_weights[0][1]		)
      set_param(
          torch_block.feed_forward.layer_norm					,      torch.tensor(_A		)					,      torch.tensor(_A		)					,      )

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

      # intermediate out
      lowerCamelCase__	:       str					=						np.asarray(intermediate_weights[4][0]		)
      lowerCamelCase__	:       Tuple					=						np.asarray(intermediate_weights[4][1]		)
      set_param(
          torch_block.feed_forward.output.dense					,      torch.tensor(_A		).transpose(0					,      1		).contiguous()					,      torch.tensor(_A		)					,      )

def    lowercase_	(	_A    :					List[Any]					,      _A    :					List[str]					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						torch_model.reformer

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

      if isinstance(weights[3]					,      _A		):
            lowerCamelCase__	:       Union[str, Any]					=						torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights		)		):
                  lowerCamelCase__	:       Optional[int]					=						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__	:       str					=						nn.Parameter(torch.tensor(_A		)		)

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

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

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

def    lowercase_	(	_A    :					Any					,      _A    :					Union[str, Any]					,      _A    :					Optional[int]		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						ReformerConfig.from_json_file(_A		)
      print(F"Building PyTorch model from configuration: {config}"		)
      lowerCamelCase__	:       List[Any]					=						ReformerModelWithLMHead(_A		)

      with open(_A					,      "rb"		) as f:
            lowerCamelCase__	:       Tuple					=						pickle.load(_A		)["weights"]

      set_model_weights_in_torch(_A					,      _A					,      config.hidden_size		)

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


if __name__ == "__main__":
    A   :					Tuple	=      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."
    )
    A   :					Union[str, Any]	=      parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


