




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	List[Any]							)					-> List[Any]:
      return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Tuple			,			__UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Any="attention"							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			SCREAMING_SNAKE_CASE_							=			np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]							)
      SCREAMING_SNAKE_CASE_							=			k_tmp.reshape(k_tmp.shape[0]			,			k_tmp.shape[1] * k_tmp.shape[2]							)
      SCREAMING_SNAKE_CASE_							=			np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]							)
      SCREAMING_SNAKE_CASE_							=			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,			o_tmp.shape[2]							)
      SCREAMING_SNAKE_CASE_							=			np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]							)
      SCREAMING_SNAKE_CASE_							=			q_tmp.reshape(q_tmp.shape[0]			,			q_tmp.shape[1] * q_tmp.shape[2]							)
      SCREAMING_SNAKE_CASE_							=			np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]							)
      SCREAMING_SNAKE_CASE_							=			v_tmp.reshape(v_tmp.shape[0]			,			v_tmp.shape[1] * v_tmp.shape[2]							)
      return k, o, q, v

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Optional[Any]=False							)					-> List[Any]:
      if split_mlp_wi:
            SCREAMING_SNAKE_CASE_							=			params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
            SCREAMING_SNAKE_CASE_							=			params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
            SCREAMING_SNAKE_CASE_							=			(wi_a, wi_a)
      else:
            SCREAMING_SNAKE_CASE_							=			params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

      SCREAMING_SNAKE_CASE_							=			params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
      return wi, wo

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Optional[int]							)					-> List[str]:
      return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	dict			,			*, __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	bool			,			__UpperCAmelCase						:	bool = False							)					-> Any:
      SCREAMING_SNAKE_CASE_							=			traverse_util.flatten_dict(variables['target']							)
      SCREAMING_SNAKE_CASE_							=			{'/'.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
      SCREAMING_SNAKE_CASE_							=			'encoder/encoder/mlp/wi_0/kernel' in old
      print('Split MLP:'			,			__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			collections.OrderedDict()

      # Shared embeddings.
      SCREAMING_SNAKE_CASE_							=			old['token_embedder/embedding']

      # Encoder.
      for i in range(__UpperCAmelCase							):
            # Block i, layer 0 (Self Attention).
            SCREAMING_SNAKE_CASE_							=			tax_layer_norm_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'encoder'			,			'pre_attention_layer_norm'							)
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			tax_attention_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'encoder'			,			'attention'							)
            SCREAMING_SNAKE_CASE_							=			layer_norm
            SCREAMING_SNAKE_CASE_							=			k.T
            SCREAMING_SNAKE_CASE_							=			o.T
            SCREAMING_SNAKE_CASE_							=			q.T
            SCREAMING_SNAKE_CASE_							=			v.T

            # Block i, layer 1 (MLP).
            SCREAMING_SNAKE_CASE_							=			tax_layer_norm_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'encoder'			,			'pre_mlp_layer_norm'							)
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			tax_mlp_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'encoder'			,			__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			layer_norm
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_							=			wi[0].T
                  SCREAMING_SNAKE_CASE_							=			wi[1].T
            else:
                  SCREAMING_SNAKE_CASE_							=			wi.T
            SCREAMING_SNAKE_CASE_							=			wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  SCREAMING_SNAKE_CASE_							=			tax_relpos_bias_lookup(
                      __UpperCAmelCase			,			__UpperCAmelCase			,			'encoder'							).T

      SCREAMING_SNAKE_CASE_							=			old['encoder/encoder_norm/scale']

      if not scalable_attention:
            SCREAMING_SNAKE_CASE_							=			tax_relpos_bias_lookup(
                __UpperCAmelCase			,			0			,			'encoder'							).T
            SCREAMING_SNAKE_CASE_							=			tax_relpos_bias_lookup(
                __UpperCAmelCase			,			0			,			'decoder'							).T

      if not is_encoder_only:
            # Decoder.
            for i in range(__UpperCAmelCase							):
                  # Block i, layer 0 (Self Attention).
                  SCREAMING_SNAKE_CASE_							=			tax_layer_norm_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			'pre_self_attention_layer_norm'							)
                  SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			tax_attention_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			'self_attention'							)
                  SCREAMING_SNAKE_CASE_							=			layer_norm
                  SCREAMING_SNAKE_CASE_							=			k.T
                  SCREAMING_SNAKE_CASE_							=			o.T
                  SCREAMING_SNAKE_CASE_							=			q.T
                  SCREAMING_SNAKE_CASE_							=			v.T

                  # Block i, layer 1 (Cross Attention).
                  SCREAMING_SNAKE_CASE_							=			tax_layer_norm_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			'pre_cross_attention_layer_norm'							)
                  SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			tax_attention_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			'encoder_decoder_attention'							)
                  SCREAMING_SNAKE_CASE_							=			layer_norm
                  SCREAMING_SNAKE_CASE_							=			k.T
                  SCREAMING_SNAKE_CASE_							=			o.T
                  SCREAMING_SNAKE_CASE_							=			q.T
                  SCREAMING_SNAKE_CASE_							=			v.T

                  # Block i, layer 2 (MLP).
                  SCREAMING_SNAKE_CASE_							=			tax_layer_norm_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			'pre_mlp_layer_norm'							)
                  SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			tax_mlp_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'			,			__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_							=			layer_norm
                  if split_mlp_wi:
                        SCREAMING_SNAKE_CASE_							=			wi[0].T
                        SCREAMING_SNAKE_CASE_							=			wi[1].T
                  else:
                        SCREAMING_SNAKE_CASE_							=			wi.T
                  SCREAMING_SNAKE_CASE_							=			wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        SCREAMING_SNAKE_CASE_							=			tax_relpos_bias_lookup(__UpperCAmelCase			,			__UpperCAmelCase			,			'decoder'							).T

            SCREAMING_SNAKE_CASE_							=			old['decoder/decoder_norm/scale']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  SCREAMING_SNAKE_CASE_							=			old['decoder/logits_dense/kernel'].T

      return new

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	bool							)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			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:
            SCREAMING_SNAKE_CASE_							=			state_dict['shared.weight']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  SCREAMING_SNAKE_CASE_							=			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.'							)
                  SCREAMING_SNAKE_CASE_							=			state_dict['shared.weight']

      return state_dict

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	str			,			__UpperCAmelCase						:	str							)					-> int:
      SCREAMING_SNAKE_CASE_							=			checkpoints.load_tax_checkpoint(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			convert_tax_to_pytorch(
          __UpperCAmelCase			,			num_layers=config.num_layers			,			is_encoder_only=__UpperCAmelCase			,			scalable_attention=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			make_state_dict(__UpperCAmelCase			,			__UpperCAmelCase							)
      model.load_state_dict(__UpperCAmelCase			,			strict=__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	bool = False			,			__UpperCAmelCase						:	bool = False			,			)					-> List[Any]:
      SCREAMING_SNAKE_CASE_							=			MTaConfig.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:
            SCREAMING_SNAKE_CASE_							=			UMTaEncoderModel(__UpperCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE_							=			UMTaForConditionalGeneration(__UpperCAmelCase							)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__UpperCAmelCase			,			__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__ :   int					=							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
   )
   parser.add_argument(
       '--scalable_attention',
       action='store_true',
       help='Whether the model uses scaled attention (umt5 model)',
       default=False,
   )
   lowerCamelCase__ :   List[Any]					=							parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )





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_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Optional[Any]=True			,			__UpperCAmelCase						:	Any="pt"							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			{'add_prefix_space': True} if isinstance(__UpperCAmelCase			,			__UpperCAmelCase							) and not line.startswith(' '							) else {}
      SCREAMING_SNAKE_CASE_							=			padding_side
      return tokenizer(
          [line]			,			max_length=__UpperCAmelCase			,			padding='max_length' if pad_to_max_length else None			,			truncation=__UpperCAmelCase			,			return_tensors=__UpperCAmelCase			,			add_special_tokens=__UpperCAmelCase			,			**__UpperCAmelCase			,			)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Dict=None			,			)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			input_ids.ne(__UpperCAmelCase							).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_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







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



 def __len__(							self  :		Tuple						):
       return len(self.src_lens						)



 def __getitem__(							self  :		List[str]				,				_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			index + 1  # linecache starts at 1
       SCREAMING_SNAKE_CASE_							=			self.prefix + linecache.getline(str(self.src_file						)				,				_lowerCAmelCase						).rstrip('\n'						)
       SCREAMING_SNAKE_CASE_							=			linecache.getline(str(self.tgt_file						)				,				_lowerCAmelCase						).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				,				_lowerCAmelCase						):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

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

       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_source_length				,				'right'						)
       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_target_length				,				'right'						)

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



 @staticmethod
 def       lowerCAmelCase_  (							_lowerCAmelCase  :		Optional[int]						):
       return [len(_lowerCAmelCase						) for x in Path(_lowerCAmelCase						).open().readlines()]




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['input_ids'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['attention_mask'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['decoder_input_ids'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer				,				_lowerCAmelCase						)
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE_							=			(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer				,				_lowerCAmelCase						)
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE_							=			trim_batch(_lowerCAmelCase				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			trim_batch(_lowerCAmelCase				,				_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{
           'input_ids': source_ids,
           'attention_mask': source_mask,
           'decoder_input_ids': y,
       }
       return batch


lowerCamelCase__ :   List[str]					=							getLogger(__name__)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[List]							)					-> Tuple:
      return list(itertools.chain.from_iterable(__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> None:
      SCREAMING_SNAKE_CASE_							=			get_git_info()
      save_json(__UpperCAmelCase			,			os.path.join(__UpperCAmelCase			,			'git_log.json'							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Tuple			,			__UpperCAmelCase						:	Optional[int]=4			,			**__UpperCAmelCase						:	Tuple							)					-> str:
      with open(__UpperCAmelCase			,			'w'							) as f:
            json.dump(__UpperCAmelCase			,			__UpperCAmelCase			,			indent=__UpperCAmelCase			,			**__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]							)					-> int:
      with open(__UpperCAmelCase							) as f:
            return json.load(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      )					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			git.Repo(search_parent_directories=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			{
          'repo_id': str(__UpperCAmelCase							),
          'repo_sha': str(repo.head.object.hexsha							),
          'repo_branch': str(repo.active_branch							),
          'hostname': str(socket.gethostname()							),
      }
      return repo_infos

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Callable			,			__UpperCAmelCase						:	Iterable							)					-> List:
      return list(map(__UpperCAmelCase			,			__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Dict							)					-> Dict:
      with open(__UpperCAmelCase			,			'wb'							) as f:
            return pickle.dump(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Any:

      def remove_articles(__UpperCAmelCase						:	Any							):
            return re.sub(r'\b(a|an|the)\b'			,			' '			,			__UpperCAmelCase							)

      def white_space_fix(__UpperCAmelCase						:	List[str]							):
            return " ".join(text.split()							)

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

      def lower(__UpperCAmelCase						:	Union[str, Any]							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase							)							)							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Any							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			Counter(__UpperCAmelCase							) & Counter(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			sum(common.values()							)
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			(2 * precision * recall) / (precision + recall)
      return fa

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Optional[int]							)					-> Any:
      return normalize_answer(__UpperCAmelCase							) == normalize_answer(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	List[str]							)					-> Dict:
      assert len(__UpperCAmelCase							) == len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			0
      for hypo, pred in zip(__UpperCAmelCase			,			__UpperCAmelCase							):
            em += exact_match_score(__UpperCAmelCase			,			__UpperCAmelCase							)
      if len(__UpperCAmelCase							) > 0:
            em /= len(__UpperCAmelCase							)
      return {"em": em}

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]							)					-> Dict:
      return model_prefix.startswith('rag'							)





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	List[str]							)					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      SCREAMING_SNAKE_CASE_							=			'dropout_rate'
      for p in extra_params:
            if getattr(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							):
                  if not hasattr(__UpperCAmelCase			,			__UpperCAmelCase							) and not hasattr(__UpperCAmelCase			,			equivalent_param[p]							):
                        logger.info('config doesn\'t have a `{}` attribute'.format(__UpperCAmelCase							)							)
                        delattr(__UpperCAmelCase			,			__UpperCAmelCase							)
                        continue
                  SCREAMING_SNAKE_CASE_							=			p if hasattr(__UpperCAmelCase			,			__UpperCAmelCase							) else equivalent_param[p]
                  setattr(__UpperCAmelCase			,			__UpperCAmelCase			,			getattr(__UpperCAmelCase			,			__UpperCAmelCase							)							)
                  delattr(__UpperCAmelCase			,			__UpperCAmelCase							)
      return hparams, config