






'''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 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
						UpperCamelCase__         = nn.Parameter(lowercase_					)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
												UpperCamelCase__         = nn.Parameter(lowercase_					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.asarray(weights[0]					)
						UpperCamelCase__         = np.asarray(weights[1]					)
						UpperCamelCase__         = 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 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.asarray(weights[0]					)
						UpperCamelCase__         = np.asarray(weights[1]					)
						UpperCamelCase__         = np.asarray(weights[2]					)
						UpperCamelCase__         = 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 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





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

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

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

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

						# intermediate dense
						UpperCamelCase__         = np.asarray(intermediate_weights[1][0]					)
						UpperCamelCase__         = 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
						UpperCamelCase__         = np.asarray(intermediate_weights[4][0]					)
						UpperCamelCase__         = 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 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = torch_model.reformer

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

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

						UpperCamelCase__         = 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					):
												UpperCamelCase__         = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(lowercase_						,      lowercase_						,      lowercase_					)

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

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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = ReformerConfig.from_json_file(lowercase_					)
						print(f"Building PyTorch model from configuration: {config}"					)
						UpperCamelCase__         = ReformerModelWithLMHead(lowercase_					)

						with open(lowercase_						,      """rb"""					) as f:
												UpperCamelCase__         = 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_				: Any    	=							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_				: Union[str, Any]    	=							parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











'''simple docstring'''


from collections import defaultdict







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = 1
						UpperCamelCase__         = True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(_A					)
						if ret % 2 == 0:
												cuts.append(_A					)
						return ret







def 	__magic_name__( ):
						'''simple docstring'''





						dfs(1					)


if __name__ == "__main__":
					lowerCamelCase_	,     lowerCamelCase_				: int    	=							10, 9
					lowerCamelCase_				: int    	=							defaultdict(list)
					lowerCamelCase_				: dict[int, bool]    	=							{}
					lowerCamelCase_				: list[int]    	=							[]
					lowerCamelCase_				: Union[str, Any]    	=							0
					lowerCamelCase_				: Tuple    	=							[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)
