






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Union[str, Any]  ,					a						:	List[Any]  ,					a						:	Any  ,					a						:	Tuple=True  ,					a						:	List[Any]="pt" ):
							snake_case__	  =       {"""add_prefix_space""": True} if isinstance(a  ,					a ) and not line.startswith(""" """ ) else {}
							snake_case__	  =       padding_side
							return tokenizer(
							    [line]  ,					max_length=a  ,					padding="""max_length""" if pad_to_max_length else None  ,					truncation=a  ,					return_tensors=a  ,					add_special_tokens=a  ,					**a  ,					)

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Any=None  ,					):
							snake_case__	  =       input_ids.ne(a ).any(dim=0 )
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def __init__(      self    :    int			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]="train"			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    List[Any]=None			,		UpperCamelCase__    :    Optional[int]=None			,		UpperCamelCase__    :    Optional[Any]=""			,		):






											'''simple docstring'''







											super().__init__()
											snake_case__	  =       Path(UpperCamelCase__).joinpath(type_path + """.source""")
											snake_case__	  =       Path(UpperCamelCase__).joinpath(type_path + """.target""")
											snake_case__	  =       self.get_char_lens(self.src_file)
											snake_case__	  =       max_source_length
											snake_case__	  =       max_target_length
											assert min(self.src_lens) > 0, F'''found empty line in {self.src_file}'''
											snake_case__	  =       tokenizer
											snake_case__	  =       prefix
											if n_obs is not None:
																		snake_case__	  =       self.src_lens[:n_obs]
											snake_case__	  =       src_lang
											snake_case__	  =       tgt_lang



				def __len__(      self    :    List[Any]):






											'''simple docstring'''







											return len(self.src_lens)



				def __getitem__(      self    :    Optional[int]			,		UpperCamelCase__    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       index + 1  # linecache starts at 1
											snake_case__	  =       self.prefix + linecache.getline(str(self.src_file)			,		UpperCamelCase__).rstrip("""\n""")
											snake_case__	  =       linecache.getline(str(self.tgt_file)			,		UpperCamelCase__).rstrip("""\n""")
											assert source_line, F'''empty source line for index {index}'''
											assert tgt_line, F'''empty tgt line for index {index}'''

											# Need to add eos token manually for T5
											if isinstance(self.tokenizer			,		UpperCamelCase__):
																		source_line += self.tokenizer.eos_token
																		tgt_line += self.tokenizer.eos_token

											# Pad source and target to the right
											snake_case__	  =       (
											    self.tokenizer.question_encoder if isinstance(self.tokenizer			,		UpperCamelCase__) else self.tokenizer
											)
											snake_case__	  =       self.tokenizer.generator if isinstance(self.tokenizer			,		UpperCamelCase__) else self.tokenizer

											snake_case__	  =       encode_line(UpperCamelCase__			,		UpperCamelCase__			,		self.max_source_length			,		"""right""")
											snake_case__	  =       encode_line(UpperCamelCase__			,		UpperCamelCase__			,		self.max_target_length			,		"""right""")

											snake_case__	  =       source_inputs["""input_ids"""].squeeze()
											snake_case__	  =       target_inputs["""input_ids"""].squeeze()
											snake_case__	  =       source_inputs["""attention_mask"""].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}



				@staticmethod
				def 					__magic_name__							(      UpperCamelCase__    :    Dict):






											'''simple docstring'''







											return [len(UpperCamelCase__) for x in Path(UpperCamelCase__).open().readlines()]



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       torch.stack([x["""input_ids"""] for x in batch])
											snake_case__	  =       torch.stack([x["""attention_mask"""] for x in batch])
											snake_case__	  =       torch.stack([x["""decoder_input_ids"""] for x in batch])
											snake_case__	  =       (
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer			,		UpperCamelCase__)
											    else self.tokenizer.pad_token_id
											)
											snake_case__	  =       (
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer			,		UpperCamelCase__)
											    else self.tokenizer.pad_token_id
											)
											snake_case__	  =       trim_batch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__  , snake_case__	  =       trim_batch(UpperCamelCase__			,		UpperCamelCase__			,		attention_mask=UpperCamelCase__)
											snake_case__	  =       {
											    """input_ids""": source_ids,
											    """attention_mask""": source_mask,
											    """decoder_input_ids""": y,
											}
											return batch


a__       =     getLogger(__name__)

def _UpperCAmelCase   (	a						:	List[List] ):
							return list(itertools.chain.from_iterable(a ) )

def _UpperCAmelCase   (	a						:	str ):
							snake_case__	  =       get_git_info()
							save_json(a  ,					os.path.join(a  ,					"""git_log.json""" ) )

def _UpperCAmelCase   (	a						:	int  ,					a						:	List[Any]  ,					a						:	Optional[int]=4  ,					**a						:	int ):
							with open(a  ,					"""w""" ) as f:
														json.dump(a  ,					a  ,					indent=a  ,					**a )

def _UpperCAmelCase   (	a						:	List[str] ):
							with open(a ) as f:
														return json.load(a )

def _UpperCAmelCase   (	):
							snake_case__	  =       git.Repo(search_parent_directories=a )
							snake_case__	  =       {
							    """repo_id""": str(a ),
							    """repo_sha""": str(repo.head.object.hexsha ),
							    """repo_branch""": str(repo.active_branch ),
							    """hostname""": str(socket.gethostname() ),
							}
							return repo_infos

def _UpperCAmelCase   (	a						:	Callable  ,					a						:	Iterable ):
							return list(map(a  ,					a ) )

def _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Dict ):
							with open(a  ,					"""wb""" ) as f:
														return pickle.dump(a  ,					a )

def _UpperCAmelCase   (	a						:	Any ):

							def remove_articles(a						:	List[str] ):
														return re.sub(r"""\b(a|an|the)\b"""  ,					""" """  ,					a )

							def white_space_fix(a						:	Union[str, Any] ):
														return " ".join(text.split() )

							def remove_punc(a						:	List[Any] ):
														snake_case__	  =       set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(a						:	Optional[Any] ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a ) ) ) )

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	Optional[int] ):
							snake_case__	  =       normalize_answer(a ).split()
							snake_case__	  =       normalize_answer(a ).split()
							snake_case__	  =       Counter(a ) & Counter(a )
							snake_case__	  =       sum(common.values() )
							if num_same == 0:
														return 0
							snake_case__	  =       1.0 * num_same / len(a )
							snake_case__	  =       1.0 * num_same / len(a )
							snake_case__	  =       (2 * precision * recall) / (precision + recall)
							return fa

def _UpperCAmelCase   (	a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
							return normalize_answer(a ) == normalize_answer(a )

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	List[str] ):
							assert len(a ) == len(a )
							snake_case__	  =       0
							for hypo, pred in zip(a  ,					a ):
														em += exact_match_score(a  ,					a )
							if len(a ) > 0:
														em /= len(a )
							return {"em": em}

def _UpperCAmelCase   (	a						:	Tuple ):
							return model_prefix.startswith("""rag""" )



def _UpperCAmelCase   (	a						:	int  ,					a						:	Any  ,					a						:	Union[str, Any] ):
							snake_case__	  =       {p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							snake_case__	  =       """dropout_rate"""
							for p in extra_params:
														if getattr(a  ,					a  ,					a ):
																					if not hasattr(a  ,					a ) and not hasattr(a  ,					equivalent_param[p] ):
																												logger.info("""config doesn't have a `{}` attribute""".format(a ) )
																												delattr(a  ,					a )
																												continue
																					snake_case__	  =       p if hasattr(a  ,					a ) else equivalent_param[p]
																					setattr(a  ,					a  ,					getattr(a  ,					a ) )
																					delattr(a  ,					a )
							return hparams, config








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   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

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

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

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

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

							# intermediate dense
							snake_case__	  =       np.asarray(intermediate_weights[1][0] )
							snake_case__	  =       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
							snake_case__	  =       np.asarray(intermediate_weights[4][0] )
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] ):
							# reformer model
							snake_case__	  =       torch_model.reformer

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

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

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

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

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

							with open(a  ,					"""rb""" ) as f:
														snake_case__	  =       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__       =     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__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
