





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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=True  ,  UpperCAmelCase="pt"				):
					_snake_case       :					List[Any]				       =   {'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_				) and not line.startswith(" "				) else {}
					_snake_case       :					str				       =   padding_side
					return tokenizer(
					    [line]  ,  max_length=SCREAMING_SNAKE_CASE_  ,  padding="max_length" if pad_to_max_length else None  ,  truncation=SCREAMING_SNAKE_CASE_  ,  return_tensors=SCREAMING_SNAKE_CASE_  ,  add_special_tokens=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_  ,  )







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  ):
					_snake_case       :					Any				       =   input_ids.ne(SCREAMING_SNAKE_CASE_				).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        _a(    __A    ):







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case="train" ,						__snake_case=None ,						__snake_case=None ,						__snake_case=None ,						__snake_case="" ,						)      ->      Dict:


							'''simple docstring'''

							super().__init__()
							_snake_case       :					Optional[Any]				       =   Path(__snake_case				).joinpath(type_path + ".source"				)
							_snake_case       :					Union[str, Any]				       =   Path(__snake_case				).joinpath(type_path + ".target"				)
							_snake_case       :					str				       =   self.get_char_lens(self.src_file				)
							_snake_case       :					str				       =   max_source_length
							_snake_case       :					Union[str, Any]				       =   max_target_length
							assert min(self.src_lens				) > 0, f"""found empty line in {self.src_file}"""
							_snake_case       :					Tuple				       =   tokenizer
							_snake_case       :					int				       =   prefix
							if n_obs is not None:
												_snake_case       :					Union[str, Any]				       =   self.src_lens[:n_obs]
							_snake_case       :					List[str]				       =   src_lang
							_snake_case       :					Union[str, Any]				       =   tgt_lang







		def __len__(    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							return len(self.src_lens				)







		def __getitem__(    self ,						__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   index + 1  # linecache starts at 1
							_snake_case       :					int				       =   self.prefix + linecache.getline(str(self.src_file				) ,						__snake_case				).rstrip("\n"				)
							_snake_case       :					Any				       =   linecache.getline(str(self.tgt_file				) ,						__snake_case				).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 ,						__snake_case				):
												source_line += self.tokenizer.eos_token
												tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							_snake_case       :					Any				       =   (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer ,						__snake_case				) else self.tokenizer
							)
							_snake_case       :					Optional[int]				       =   self.tokenizer.generator if isinstance(self.tokenizer ,						__snake_case				) else self.tokenizer

							_snake_case       :					Tuple				       =   encode_line(__snake_case ,						__snake_case ,						self.max_source_length ,						"right"				)
							_snake_case       :					str				       =   encode_line(__snake_case ,						__snake_case ,						self.max_target_length ,						"right"				)

							_snake_case       :					int				       =   source_inputs['input_ids'].squeeze()
							_snake_case       :					Tuple				       =   target_inputs['input_ids'].squeeze()
							_snake_case       :					Dict				       =   source_inputs['attention_mask'].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}







		@staticmethod
		def        lowercase       (    __snake_case				)      ->      int:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   torch.stack([x["input_ids"] for x in batch]				)
							_snake_case       :					int				       =   torch.stack([x["attention_mask"] for x in batch]				)
							_snake_case       :					List[str]				       =   torch.stack([x["decoder_input_ids"] for x in batch]				)
							_snake_case       :					Optional[int]				       =   (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer ,						__snake_case				)
							    else self.tokenizer.pad_token_id
							)
							_snake_case       :					Any				       =   (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer ,						__snake_case				)
							    else self.tokenizer.pad_token_id
							)
							_snake_case       :					List[str]				       =   trim_batch(__snake_case ,						__snake_case				)
							_snake_case       :					List[str]				       =   trim_batch(__snake_case ,						__snake_case ,						attention_mask=__snake_case				)
							_snake_case       :					Tuple				       =   {
							    'input_ids': source_ids,
							    'attention_mask': source_mask,
							    'decoder_input_ids': y,
							}
							return batch






__lowerCAmelCase :int     =					getLogger(__name__)







def    A				(							UpperCAmelCase				):
					return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_				)				)







def    A				(							UpperCAmelCase				):
					_snake_case       :					Union[str, Any]				       =   get_git_info()
					save_json(SCREAMING_SNAKE_CASE_  ,  os.path.join(SCREAMING_SNAKE_CASE_  ,  "git_log.json"				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=4  ,  **UpperCAmelCase				):
					with open(SCREAMING_SNAKE_CASE_  ,  "w"				) as f:
										json.dump(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  indent=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_				)







def    A				(							UpperCAmelCase				):
					with open(SCREAMING_SNAKE_CASE_				) as f:
										return json.load(SCREAMING_SNAKE_CASE_				)







def    A				(							):
					_snake_case       :					int				       =   git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_				)
					_snake_case       :					Any				       =   {
					    'repo_id': str(SCREAMING_SNAKE_CASE_				),
					    'repo_sha': str(repo.head.object.hexsha				),
					    'repo_branch': str(repo.active_branch				),
					    'hostname': str(socket.gethostname()				),
					}
					return repo_infos







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return list(map(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					with open(SCREAMING_SNAKE_CASE_  ,  "wb"				) as f:
										return pickle.dump(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_				)







def    A				(							UpperCAmelCase				):

					def remove_articles(UpperCAmelCase				):
										return re.sub(R"\b(a|an|the)\b"  ,  " "  ,  SCREAMING_SNAKE_CASE_				)

					def white_space_fix(UpperCAmelCase				):
										return " ".join(text.split()				)

					def remove_punc(UpperCAmelCase				):
										_snake_case       :					Union[str, Any]				       =   set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

					def lower(UpperCAmelCase				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_				)				)				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   normalize_answer(SCREAMING_SNAKE_CASE_				).split()
					_snake_case       :					Tuple				       =   normalize_answer(SCREAMING_SNAKE_CASE_				).split()
					_snake_case       :					Optional[Any]				       =   Counter(SCREAMING_SNAKE_CASE_				) & Counter(SCREAMING_SNAKE_CASE_				)
					_snake_case       :					Any				       =   sum(common.values()				)
					if num_same == 0:
										return 0
					_snake_case       :					Optional[Any]				       =   1.0 * num_same / len(SCREAMING_SNAKE_CASE_				)
					_snake_case       :					int				       =   1.0 * num_same / len(SCREAMING_SNAKE_CASE_				)
					_snake_case       :					Union[str, Any]				       =   (2 * precision * recall) / (precision + recall)
					return fa







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return normalize_answer(SCREAMING_SNAKE_CASE_				) == normalize_answer(SCREAMING_SNAKE_CASE_				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					assert len(SCREAMING_SNAKE_CASE_				) == len(SCREAMING_SNAKE_CASE_				)
					_snake_case       :					str				       =   0
					for hypo, pred in zip(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_				):
										em += exact_match_score(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_				)
					if len(SCREAMING_SNAKE_CASE_				) > 0:
										em /= len(SCREAMING_SNAKE_CASE_				)
					return {"em": em}







def    A				(							UpperCAmelCase				):
					return model_prefix.startswith("rag"				)







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






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".join(UpperCAmelCase				): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															_snake_case       :					Dict				       =   k.T
															_snake_case       :					List[str]				       =   o.T
															_snake_case       :					List[Any]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   old[
										    "decoder/relpos_bias/rel_embedding"
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_snake_case       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_snake_case       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   state_dict["shared.weight"]

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print("Using shared word embeddings as lm_head."				)
															_snake_case       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   TaConfig.from_json_file(UpperCAmelCase				)
					print(F"""Building PyTorch model from configuration: {config}"""				)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   TaForConditionalGeneration(UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
					model.save_pretrained(UpperCAmelCase				)

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase				)
					print("Done"				)


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)