






"""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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None):
					# set parameter of one layer
					assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
					a__          =  nn.Parameter(lowerCamelCase_)
					if bias is not None:
										assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
										a__          =  nn.Parameter(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# 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(lowerCamelCase_).transpose(1 ,     2).contiguous().view(-1 ,     lowerCamelCase_) ,     )
					set_param(
					    torch_layer.self_attention.value ,     torch.tensor(lowerCamelCase_).transpose(1 ,     2).contiguous().view(-1 ,     lowerCamelCase_) ,     )
					set_param(
					    torch_layer.output.dense ,     torch.tensor(lowerCamelCase_).view(-1 ,     lowerCamelCase_).contiguous().transpose(0 ,     1) ,     )





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# 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(lowerCamelCase_).transpose(1 ,     2).contiguous().view(-1 ,     lowerCamelCase_) ,     )
					set_param(
					    torch_layer.self_attention.key ,     torch.tensor(lowerCamelCase_).transpose(1 ,     2).contiguous().view(-1 ,     lowerCamelCase_) ,     )
					set_param(
					    torch_layer.self_attention.value ,     torch.tensor(lowerCamelCase_).transpose(1 ,     2).contiguous().view(-1 ,     lowerCamelCase_) ,     )
					set_param(
					    torch_layer.output.dense ,     torch.tensor(lowerCamelCase_).view(-1 ,     lowerCamelCase_).contiguous().transpose(0 ,     1) ,     )





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# 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(lowerCamelCase_) ,     torch.tensor(lowerCamelCase_) ,     )

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

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

					# Chunked Feed Forward
					if len(lowerCamelCase_) == 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(lowerCamelCase_) ,     torch.tensor(lowerCamelCase_) ,     )

					# 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(lowerCamelCase_).transpose(0 ,     1).contiguous() ,     torch.tensor(lowerCamelCase_) ,     )

					# 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(lowerCamelCase_).transpose(0 ,     1).contiguous() ,     torch.tensor(lowerCamelCase_) ,     )





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# reformer model
					a__          =  torch_model.reformer

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

					if isinstance(weights[3] ,     lowerCamelCase_):
										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(lowerCamelCase_))

					a__          =  weights[5]
					assert len(torch_model_reformer.encoder.layers) * 4 == len(
					    lowerCamelCase_), "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(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

					# 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(lowerCamelCase_) ,     torch.tensor(lowerCamelCase_) ,     )

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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					# Initialise PyTorch model
					a__          =  ReformerConfig.from_json_file(lowerCamelCase_)
					print(f'Building PyTorch model from configuration: {config}')
					a__          =  ReformerModelWithLMHead(lowerCamelCase_)

					with open(lowerCamelCase_ ,     '''rb''') as f:
										a__          =  pickle.load(lowerCamelCase_)['''weights''']

					set_model_weights_in_torch(lowerCamelCase_ ,     lowerCamelCase_ ,     config.hidden_size)

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


if __name__ == "__main__":
	__a       :	Dict             = 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       :	Optional[Any]             = parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


