


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)







class     __lowercase    :



		"""simple docstring"""

		def __init__(      self						,	A						,	A=13						,	A=7						,	A=True						,	A=True						,	A=True						,	A=True						,	A=99						,	A=64						,	A=5						,	A=4						,	A=37						,	A="gelu"						,	A=0.1						,	A=0.1						,	A=5_12						,	A=16						,	A=2						,	A=0.02						,	A=3						,	A=4						,	A=None						,	)   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   parent
					lowerCamelCase  						=   batch_size
					lowerCamelCase  						=   seq_length
					lowerCamelCase  						=   is_training
					lowerCamelCase  						=   use_input_mask
					lowerCamelCase  						=   use_token_type_ids
					lowerCamelCase  						=   use_labels
					lowerCamelCase  						=   vocab_size
					lowerCamelCase  						=   hidden_size
					lowerCamelCase  						=   num_hidden_layers
					lowerCamelCase  						=   num_attention_heads
					lowerCamelCase  						=   intermediate_size
					lowerCamelCase  						=   hidden_act
					lowerCamelCase  						=   hidden_dropout_prob
					lowerCamelCase  						=   attention_probs_dropout_prob
					lowerCamelCase  						=   max_position_embeddings
					lowerCamelCase  						=   type_vocab_size
					lowerCamelCase  						=   type_sequence_label_size
					lowerCamelCase  						=   initializer_range
					lowerCamelCase  						=   num_labels
					lowerCamelCase  						=   num_choices
					lowerCamelCase  						=   scope
					lowerCamelCase  						=   vocab_size - 1







		def  __A					(      self     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

					lowerCamelCase  						=   None
					if self.use_input_mask:
								lowerCamelCase  						=   random_attention_mask([self.batch_size, self.seq_length]     )

					lowerCamelCase  						=   None
					if self.use_labels:
								lowerCamelCase  						=   ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels     )

					lowerCamelCase  						=   self.get_config()

					return config, input_ids, input_mask, token_labels







		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					return GPTNeoXConfig(
					    vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	is_decoder=A						,	initializer_range=self.initializer_range						,	pad_token_id=self.pad_token_id						,	)







		def  __A					(      self     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   self.prepare_config_and_inputs()

					lowerCamelCase  						=   True

					return config, input_ids, input_mask, token_labels







		def  __A					(      self						,	A						,	A						,	A     )   ->      str:







					'''simple docstring'''







					lowerCamelCase  						=   GPTNeoXModel(config=A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   model(A						,	attention_mask=A     )
					lowerCamelCase  						=   model(A     )
					self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)     )







		def  __A					(      self						,	A						,	A						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   True
					lowerCamelCase  						=   GPTNeoXModel(A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   model(A						,	attention_mask=A     )
					self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)     )







		def  __A					(      self						,	A						,	A						,	A						,	A     )   ->      Optional[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   GPTNeoXForCausalLM(config=A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   model(A						,	attention_mask=A						,	labels=A     )
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)     )







		def  __A					(      self						,	A						,	A						,	A						,	A     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.num_labels
					lowerCamelCase  						=   GPTNeoXForQuestionAnswering(A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   model(A						,	attention_mask=A     )
					self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)     )
					self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)     )







		def  __A					(      self						,	A						,	A						,	A						,	A     )   ->      Optional[int]:







					'''simple docstring'''







					lowerCamelCase  						=   self.num_labels
					lowerCamelCase  						=   GPTNeoXForSequenceClassification(A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   ids_tensor([self.batch_size]						,	self.type_sequence_label_size     )
					lowerCamelCase  						=   model(A						,	attention_mask=A						,	labels=A     )
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)     )







		def  __A					(      self						,	A						,	A						,	A						,	A     )   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   self.num_labels
					lowerCamelCase  						=   GPTNeoXForTokenClassification(A     )
					model.to(A     )
					model.eval()
					lowerCamelCase  						=   model(A						,	attention_mask=A						,	labels=A     )
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)     )







		def  __A					(      self						,	A						,	A						,	A     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   True
					lowerCamelCase  						=   GPTNeoXForCausalLM(config=A     )
					model.to(A     )
					model.eval()

					# first forward pass
					lowerCamelCase  						=   model(A						,	attention_mask=A						,	use_cache=A     )
					lowerCamelCase  						=   outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					lowerCamelCase  						=   ids_tensor((self.batch_size, 3)						,	config.vocab_size     )
					lowerCamelCase  						=   ids_tensor((self.batch_size, 3)						,	vocab_size=2     )

					# append to next input_ids and
					lowerCamelCase  						=   torch.cat([input_ids, next_tokens]						,	dim=-1     )
					lowerCamelCase  						=   torch.cat([input_mask, next_mask]						,	dim=-1     )

					lowerCamelCase  						=   model(A						,	attention_mask=A						,	output_hidden_states=A     )
					lowerCamelCase  						=   output_from_no_past["hidden_states"][0]
					lowerCamelCase  						=   model(
					    A						,	attention_mask=A						,	past_key_values=A						,	output_hidden_states=A						,	)["hidden_states"][0]

					# select random slice
					lowerCamelCase  						=   ids_tensor((1,)						,	output_from_past.shape[-1]     ).item()
					lowerCamelCase  						=   output_from_no_past[:, -3:, random_slice_idx].detach()
					lowerCamelCase  						=   output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(A						,	A						,	atol=1e-3     )     )







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   self.prepare_config_and_inputs()
					lowerCamelCase  						=   config_and_inputs
					lowerCamelCase  						=   {"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict







@require_torch
class     __lowercase    (				_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				unittest.TestCase   ):



		"""simple docstring"""

		UpperCamelCase			: List[str]           =						(
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCamelCase			: Union[str, Any]           =						(GPTNeoXForCausalLM,) if is_torch_available() else ()
		UpperCamelCase			: str           =						(
		    {
		        "feature-extraction": GPTNeoXModel,
		        "question-answering": GPTNeoXForQuestionAnswering,
		        "text-classification": GPTNeoXForSequenceClassification,
		        "text-generation": GPTNeoXForCausalLM,
		        "token-classification": GPTNeoXForTokenClassification,
		        "zero-shot": GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		UpperCamelCase			: Optional[Any]           =						False
		UpperCamelCase			: List[Any]           =						False
		UpperCamelCase			: Optional[Any]           =						False
		UpperCamelCase			: str           =						False







		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   GPTNeoXModelTester(self     )
					lowerCamelCase  						=   ConfigTester(self						,	config_class=A						,	hidden_size=64						,	num_attention_heads=8     )







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					self.config_tester.run_common_tests()







		def  __A					(      self     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(A						,	A						,	A     )







		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(A						,	A						,	A     )







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







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs_for_decoder()

					lowerCamelCase  						=   None

					self.model_tester.create_and_check_model_as_decoder(A						,	A						,	A     )







		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(A						,	A						,	A     )







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_causal_lm(*A     )







		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*A     )







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*A     )







		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*A     )







		@unittest.skip(reason="""Feed forward chunking is not implemented"""     )
		def  __A					(      self     )   ->      List[Any]:







					'''simple docstring'''







					pass







		@parameterized.expand([("""linear""",), ("""dynamic""",)]     )
		def  __A					(      self						,	A     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   self.model_tester.prepare_config_and_inputs_for_common()
					lowerCamelCase  						=   ids_tensor([1, 10]						,	config.vocab_size     )
					lowerCamelCase  						=   ids_tensor([1, int(config.max_position_embeddings * 1.5     )]						,	config.vocab_size     )

					set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
					lowerCamelCase  						=   GPTNeoXModel(A     )
					original_model.to(A     )
					original_model.eval()
					lowerCamelCase  						=   original_model(A     ).last_hidden_state
					lowerCamelCase  						=   original_model(A     ).last_hidden_state

					set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
					lowerCamelCase  						=   {"type": scaling_type, "factor": 10.0}
					lowerCamelCase  						=   GPTNeoXModel(A     )
					scaled_model.to(A     )
					scaled_model.eval()
					lowerCamelCase  						=   scaled_model(A     ).last_hidden_state
					lowerCamelCase  						=   scaled_model(A     ).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
								self.assertTrue(torch.allclose(A						,	A						,	atol=1e-5     )     )
					else:
								self.assertFalse(torch.allclose(A						,	A						,	atol=1e-5     )     )

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(A						,	A						,	atol=1e-5     )     )







@require_torch
class     __lowercase    (				unittest.TestCase   ):



		"""simple docstring"""

		@slow
		def  __A					(      self     )   ->      int:







					'''simple docstring'''







					lowerCamelCase  						=   AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""     )
					for checkpointing in [True, False]:
								lowerCamelCase  						=   GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""     )

								if checkpointing:
											model.gradient_checkpointing_enable()
								else:
											model.gradient_checkpointing_disable()
								model.to(A     )

								lowerCamelCase  						=   tokenizer("""My favorite food is"""						,	return_tensors="""pt"""     ).to(A     )
								# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
								# See: https://github.com/huggingface/transformers/pull/24193
								lowerCamelCase  						=   "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

								lowerCamelCase  						=   model.generate(**A						,	do_sample=A						,	max_new_tokens=20     )
								lowerCamelCase  						=   tokenizer.batch_decode(A     )[0]

								self.assertEqual(A						,	A     )








'''simple docstring'''


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_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict=True	,__lowerCamelCase					:					List[Any]="pt" ):
   lowercase_			:Dict    			= {"add_prefix_space": True} if isinstance(__lowerCamelCase	,__lowerCamelCase ) and not line.startswith(" " ) else {}
   lowercase_			:str    			= padding_side
   return tokenizer(
       [line]	,max_length=__lowerCamelCase	,padding="max_length" if pad_to_max_length else None	,truncation=__lowerCamelCase	,return_tensors=__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,**__lowerCamelCase	,)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					str=None	,):
   lowercase_			:Optional[int]    			= input_ids.ne(__lowerCamelCase ).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_   (				_lowerCAmelCase							):





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





        """simple docstring"""




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





     def __len__( self		:					Tuple						):





        """simple docstring"""




        return len(self.src_lens						)





     def __getitem__( self		:					str	,      lowercase		:					Dict						):





        """simple docstring"""




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

        # Pad source and target to the right
        lowercase_			:List[str]    			= (
            self.tokenizer.question_encoder if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer
        )
        lowercase_			:int    			= self.tokenizer.generator if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer

        lowercase_			:List[str]    			= encode_line(lowercase	,      lowercase	,      self.max_source_length	,      "right"						)
        lowercase_			:Any    			= encode_line(lowercase	,      lowercase	,      self.max_target_length	,      "right"						)

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





     @staticmethod
     def 							lowercase__      ( lowercase		:					Union[str, Any]						):





        """simple docstring"""




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







     def 							lowercase__      ( self		:					str	,      lowercase		:					List[Any]						):





        """simple docstring"""




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







lowerCAmelCase					:     List[str]												=getLogger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[List] ):
   return list(itertools.chain.from_iterable(__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:List[str]    			= get_git_info()
   save_json(__lowerCamelCase	,os.path.join(__lowerCamelCase	,"git_log.json" ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[Any]=4	,**__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=__lowerCamelCase	,**__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple ):
   with open(__lowerCamelCase ) as f:
      return json.load(__lowerCamelCase )
def 							UpperCAmelCase_  (						):
   lowercase_			:Dict    			= git.Repo(search_parent_directories=__lowerCamelCase )
   lowercase_			:List[str]    			= {
       "repo_id": str(__lowerCamelCase ),
       "repo_sha": str(repo.head.object.hexsha ),
       "repo_branch": str(repo.active_branch ),
       "hostname": str(socket.gethostname() ),
   }
   return repo_infos
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Callable	,__lowerCamelCase					:					Iterable ):
   return list(map(__lowerCamelCase	,__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"wb" ) as f:
      return pickle.dump(__lowerCamelCase	,__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):

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

   def white_space_fix(__lowerCamelCase					:					Dict ):
      return " ".join(text.split() )

   def remove_punc(__lowerCamelCase					:					Optional[Any] ):
      lowercase_			:Any    			= set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

   def lower(__lowerCamelCase					:					List[Any] ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase ) ) ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Optional[int] ):
   lowercase_			:Tuple    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Dict    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Tuple    			= Counter(__lowerCamelCase ) & Counter(__lowerCamelCase )
   lowercase_			:Tuple    			= sum(common.values() )
   if num_same == 0:
      return 0
   lowercase_			:Union[str, Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:List[Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:Tuple    			= (2 * precision * recall) / (precision + recall)
   return fa
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Union[str, Any] ):
   return normalize_answer(__lowerCamelCase ) == normalize_answer(__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					List[str] ):
   assert len(__lowerCamelCase ) == len(__lowerCamelCase )
   lowercase_			:Any    			= 0
   for hypo, pred in zip(__lowerCamelCase	,__lowerCamelCase ):
      em += exact_match_score(__lowerCamelCase	,__lowerCamelCase )
   if len(__lowerCamelCase ) > 0:
      em /= len(__lowerCamelCase )
   return {"em": em}
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   return model_prefix.startswith("rag" )


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


