





"""simple docstring"""





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
    import torch





@require_flax
class 			snake_case		:

      def __init__(			self   :Optional[Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Dict=1_4						,							_lowerCamelCase   :Any=7						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :str=True						,							_lowerCamelCase   :Union[str, Any]=True						,							_lowerCamelCase   :Optional[int]=9_9						,							_lowerCamelCase   :List[str]=3_2						,							_lowerCamelCase   :List[str]=5						,							_lowerCamelCase   :Tuple=4						,							_lowerCamelCase   :Tuple=3_7						,							_lowerCamelCase   :Optional[int]="gelu"						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :Optional[int]=0.1						,							_lowerCamelCase   :int=5_1_2						,							_lowerCamelCase   :str=0.0_2						,							_lowerCamelCase   :Dict=None						,							):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       parent
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       batch_size
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length
       __SCREAMING_SNAKE_CASE :			Dict												=       is_training
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       use_input_mask
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_labels
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       d_model
       __SCREAMING_SNAKE_CASE :			Dict												=       num_hidden_layers
       __SCREAMING_SNAKE_CASE :			List[Any]												=       num_attention_heads
       __SCREAMING_SNAKE_CASE :			int												=       ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       activation_function
       __SCREAMING_SNAKE_CASE :			int												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			List[str]												=       initializer_range
       __SCREAMING_SNAKE_CASE :			str												=       None
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       0
       __SCREAMING_SNAKE_CASE :			Any												=       2
       __SCREAMING_SNAKE_CASE :			Tuple												=       1

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Any												=       np.clip(ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size						)						,							3						,							self.vocab_size						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       None
       if self.use_input_mask:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       random_attention_mask([self.batch_size, self.seq_length]						)

       __SCREAMING_SNAKE_CASE :			str												=       XGLMConfig(
           vocab_size=self.vocab_size						,							d_model=self.hidden_size						,							num_layers=self.num_hidden_layers						,							attention_heads=self.num_attention_heads						,							ffn_dim=self.ffn_dim						,							activation_function=self.activation_function						,							activation_dropout=self.activation_dropout						,							attention_dropout=self.attention_dropout						,							max_position_embeddings=self.max_position_embeddings						,							initializer_range=self.initializer_range						,							use_cache=_lowerCamelCase						,							bos_token_id=self.bos_token_id						,							eos_token_id=self.eos_token_id						,							pad_token_id=self.pad_token_id						,							)

       return (config, input_ids, input_mask)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			int												=       self.prepare_config_and_inputs()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       config_and_inputs
       __SCREAMING_SNAKE_CASE :			int												=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       self.prepare_config_and_inputs()

       __SCREAMING_SNAKE_CASE :			List[str]												=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       __SCREAMING_SNAKE_CASE :			int												=       ids_tensor([self.batch_size, self.seq_length]						,							vocab_size=2						)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       2_0
       __SCREAMING_SNAKE_CASE :			int												=       model_class_name(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model.init_cache(input_ids.shape[0]						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       jnp.ones((input_ids.shape[0], max_decoder_length)						,							dtype='''i4'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)
       __SCREAMING_SNAKE_CASE :			int												=       model(
           input_ids[:, :-1]						,							attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			Any												=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,							dtype='''i4'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(
           input_ids[:, -1:]						,							attention_mask=_lowerCamelCase						,							past_key_values=outputs_cache.past_key_values						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Any												=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1e-3						,							msg=f'''Max diff is {diff}'''						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       2_0
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model_class_name(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Any												=       jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]						,							axis=-1						,							)

       __SCREAMING_SNAKE_CASE :			List[str]												=       model.init_cache(input_ids.shape[0]						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)

       __SCREAMING_SNAKE_CASE :			Dict												=       model(
           input_ids[:, :-1]						,							attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			int												=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,							dtype='''i4'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(
           input_ids[:, -1:]						,							past_key_values=outputs_cache.past_key_values						,							attention_mask=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			Tuple												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1e-3						,							msg=f'''Max diff is {diff}'''						)







@require_sentencepiece
@require_flax
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      lowerCamelCase__						= (FlaxXGLMForCausalLM,) if is_flax_available() else ()

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       FlaxXGLMModelTester(self						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			int												=       XGLMTokenizer.from_pretrained('''XGLM'''						,							padding_side='''left'''						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       tokenizer(['''Hello this is a long string''', '''Hey''']						,							return_tensors='''np'''						,							padding=_lowerCamelCase						,							truncation=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
       __SCREAMING_SNAKE_CASE :			Any												=       1
       __SCREAMING_SNAKE_CASE :			int												=       False

       __SCREAMING_SNAKE_CASE :			int												=       jax.jit(model.generate						)

       __SCREAMING_SNAKE_CASE :			str												=       jit_generate(inputs['''input_ids''']						,							attention_mask=inputs['''attention_mask''']						).sequences

       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.batch_decode(_lowerCamelCase						,							skip_special_tokens=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       [
           '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
           '''Hey, I\'m a newbie to the forum and I\'''',
       ]

       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

      @is_pt_flax_cross_test
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__						):
         # prepare inputs
         __SCREAMING_SNAKE_CASE :			str												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			Optional[int]												=       {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         __SCREAMING_SNAKE_CASE :			List[str]												=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       getattr(_lowerCamelCase						,							_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_inputs['''input_ids'''].shape
         __SCREAMING_SNAKE_CASE :			Any												=       np.random.randint(0						,							seq_length - 1						,							size=(batch_size,)						)
         for batch_idx, start_index in enumerate(_lowerCamelCase						):
          __SCREAMING_SNAKE_CASE :			int												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
          __SCREAMING_SNAKE_CASE :			Tuple												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
         __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_model_class(_lowerCamelCase						).eval()
         # Flax models don't use the `use_cache` option and cache is not returned as a default.
         # So we disable `use_cache` here for PyTorch model.
         __SCREAMING_SNAKE_CASE :			str												=       False
         __SCREAMING_SNAKE_CASE :			List[str]												=       model_class(_lowerCamelCase						,							dtype=jnp.floataa						)
         __SCREAMING_SNAKE_CASE :			str												=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			List[str]												=       fx_state

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			Dict												=       pt_model(**_lowerCamelCase						).to_tuple()

         __SCREAMING_SNAKE_CASE :			Optional[Any]												=       fx_model(**_lowerCamelCase						).to_tuple()
         self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

         with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(_lowerCamelCase						)
          __SCREAMING_SNAKE_CASE :			Any												=       model_class.from_pretrained(_lowerCamelCase						,							from_pt=_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE :			Dict												=       fx_model_loaded(**_lowerCamelCase						).to_tuple()
         self.assertEqual(
             len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output_loaded, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output_loaded[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

      @is_pt_flax_cross_test
      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__						):
         # prepare inputs
         __SCREAMING_SNAKE_CASE :			List[str]												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			Dict												=       {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         __SCREAMING_SNAKE_CASE :			Any												=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __SCREAMING_SNAKE_CASE :			int												=       getattr(_lowerCamelCase						,							_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE :			str												=       pt_model_class(_lowerCamelCase						).eval()
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       False
         __SCREAMING_SNAKE_CASE :			int												=       model_class(_lowerCamelCase						,							dtype=jnp.floataa						)

         __SCREAMING_SNAKE_CASE :			Dict												=       load_flax_weights_in_pytorch_model(_lowerCamelCase						,							fx_model.params						)
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_inputs['''input_ids'''].shape
         __SCREAMING_SNAKE_CASE :			Dict												=       np.random.randint(0						,							seq_length - 1						,							size=(batch_size,)						)
         for batch_idx, start_index in enumerate(_lowerCamelCase						):
          __SCREAMING_SNAKE_CASE :			List[Any]												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
          __SCREAMING_SNAKE_CASE :			List[Any]												=       0
          __SCREAMING_SNAKE_CASE :			Dict												=       1

         # make sure weights are tied in PyTorch
         pt_model.tie_weights()

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			Any												=       pt_model(**_lowerCamelCase						).to_tuple()

         __SCREAMING_SNAKE_CASE :			Any												=       fx_model(**_lowerCamelCase						).to_tuple()
         self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

         with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(_lowerCamelCase						)
          __SCREAMING_SNAKE_CASE :			List[str]												=       pt_model_class.from_pretrained(_lowerCamelCase						,							from_flax=_lowerCamelCase						)

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			List[str]												=       pt_model_loaded(**_lowerCamelCase						).to_tuple()

         self.assertEqual(
             len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			int												=       model_class_name.from_pretrained('''facebook/xglm-564M'''						)
        __SCREAMING_SNAKE_CASE :			Dict												=       model(np.ones((1, 1)						)						)
        self.assertIsNotNone(_lowerCamelCase						)







'''simple docstring'''


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_snake_case      :      Optional[Any]   							=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 		snake_case_    (UpperCamelCase       :  Optional[int]      ):





							'''simple docstring'''


							if "token" in model_name_or_path:
														return "rag_token"
							if "sequence" in model_name_or_path:
														return "rag_sequence"
							if "bart" in model_name_or_path:
														return "bart"
							return None
def 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Optional[Any]      ):





							'''simple docstring'''


							return max(metric_fn(UpperCamelCase ,						UpperCamelCase      ) for gt in ground_truths      )
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  str      ):





							'''simple docstring'''


							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
							_a	     =				[]

							if args.gold_data_mode == "qa":
														_a	     =				pd.read_csv(UpperCamelCase ,						sep='''\t''' ,						header=UpperCamelCase      )
														for answer_list in data[1]:
																					_a	     =				ast.literal_eval(UpperCamelCase      )
																					answers.append(UpperCamelCase      )
							else:
														_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
														_a	     =				[[reference] for reference in references]

							_a	     =				_a	     =				_a	     =				0
							for prediction, ground_truths in zip(UpperCamelCase ,						UpperCamelCase      ):
														total += 1
														em += metric_max_over_ground_truths(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
														fa += metric_max_over_ground_truths(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )

							_a	     =				100.0 * em / total
							_a	     =				100.0 * fa / total

							logger.info(f'F1: {fa:.2f}'      )
							logger.info(f'EM: {em:.2f}'      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


							_a	     =				args.k
							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]

							_a	     =				_a	     =				0
							for hypo, reference in zip(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				set(hypo.split('''\t'''      )[:k]      )
														_a	     =				set(reference.split('''\t'''      )      )
														total += 1
														em += len(hypo_provenance & ref_provenance      ) / k

							_a	     =				100.0 * em / total
							logger.info(f'Precision@{k}: {em: .2f}'      )
def 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  Dict      ):





							'''simple docstring'''


							def strip_title(UpperCamelCase       :  Tuple      ):
														if title.startswith('''"'''      ):
																					_a	     =				title[1:]
														if title.endswith('''"'''      ):
																					_a	     =				title[:-1]
														return title

							_a	     =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    UpperCamelCase ,						return_tensors='''pt''' ,						padding=UpperCamelCase ,						truncation=UpperCamelCase ,						)['''input_ids'''].to(args.device      )

							_a	     =				rag_model.rag.question_encoder(UpperCamelCase      )
							_a	     =				question_enc_outputs[0]

							_a	     =				rag_model.retriever(
							    UpperCamelCase ,						question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy() ,						prefix=rag_model.rag.generator.config.prefix ,						n_docs=rag_model.config.n_docs ,						return_tensors='''pt''' ,						)
							_a	     =				rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
							_a	     =				[]
							for docs in all_docs:
														_a	     =				[strip_title(UpperCamelCase      ) for title in docs['''title''']]
														provenance_strings.append('''\t'''.join(UpperCamelCase      )      )
							return provenance_strings
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  Tuple      ):





							'''simple docstring'''


							with torch.no_grad():
														_a	     =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    UpperCamelCase ,						return_tensors='''pt''' ,						padding=UpperCamelCase ,						truncation=UpperCamelCase      )

														_a	     =				inputs_dict.input_ids.to(args.device      )
														_a	     =				inputs_dict.attention_mask.to(args.device      )
														_a	     =				rag_model.generate(  # rag_model overwrites generate
														    UpperCamelCase ,						attention_mask=UpperCamelCase ,						num_beams=args.num_beams ,						min_length=args.min_length ,						max_length=args.max_length ,						early_stopping=UpperCamelCase ,						num_return_sequences=1 ,						bad_words_ids=[[0, 0]] ,						)
														_a	     =				rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase ,						skip_special_tokens=UpperCamelCase      )

														if args.print_predictions:
																					for q, a in zip(UpperCamelCase ,						UpperCamelCase      ):
																												logger.info('''Q: {} - A: {}'''.format(UpperCamelCase ,						UpperCamelCase      )      )

														return answers
def 		snake_case_    ():





							'''simple docstring'''


							_a	     =				argparse.ArgumentParser()
							parser.add_argument(
							    '''--model_type''' ,						choices=['''rag_sequence''', '''rag_token''', '''bart'''] ,						type=UpperCamelCase ,						help=(
							        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
							        ''' model_name_or_path'''
							    ) ,						)
							parser.add_argument(
							    '''--index_name''' ,						default=UpperCamelCase ,						choices=['''exact''', '''compressed''', '''legacy'''] ,						type=UpperCamelCase ,						help='''RAG model retriever type''' ,						)
							parser.add_argument(
							    '''--index_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						help='''Path to the retrieval index''' ,						)
							parser.add_argument('''--n_docs''' ,						default=5 ,						type=UpperCamelCase ,						help='''Number of retrieved docs'''      )
							parser.add_argument(
							    '''--model_name_or_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' ,						)
							parser.add_argument(
							    '''--eval_mode''' ,						choices=['''e2e''', '''retrieval'''] ,						default='''e2e''' ,						type=UpperCamelCase ,						help=(
							        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
							        ''' precision@k.'''
							    ) ,						)
							parser.add_argument('''--k''' ,						default=1 ,						type=UpperCamelCase ,						help='''k for the precision@k calculation'''      )
							parser.add_argument(
							    '''--evaluation_set''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to a file containing evaluation samples''' ,						)
							parser.add_argument(
							    '''--gold_data_path''' ,						default=UpperCamelCase ,						type=UpperCamelCase ,						required=UpperCamelCase ,						help='''Path to a tab-separated file with gold samples''' ,						)
							parser.add_argument(
							    '''--gold_data_mode''' ,						default='''qa''' ,						type=UpperCamelCase ,						choices=['''qa''', '''ans'''] ,						help=(
							        '''Format of the gold data file'''
							        '''qa - a single line in the following format: question [tab] answer_list'''
							        '''ans - a single line of the gold file contains the expected answer string'''
							    ) ,						)
							parser.add_argument(
							    '''--predictions_path''' ,						type=UpperCamelCase ,						default='''predictions.txt''' ,						help='''Name of the predictions file, to be stored in the checkpoints directory''' ,						)
							parser.add_argument(
							    '''--eval_all_checkpoints''' ,						action='''store_true''' ,						help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' ,						)
							parser.add_argument(
							    '''--eval_batch_size''' ,						default=8 ,						type=UpperCamelCase ,						help='''Batch size per GPU/CPU for evaluation.''' ,						)
							parser.add_argument(
							    '''--recalculate''' ,						help='''Recalculate predictions even if the prediction file exists''' ,						action='''store_true''' ,						)
							parser.add_argument(
							    '''--num_beams''' ,						default=4 ,						type=UpperCamelCase ,						help='''Number of beams to be used when generating answers''' ,						)
							parser.add_argument('''--min_length''' ,						default=1 ,						type=UpperCamelCase ,						help='''Min length of the generated answers'''      )
							parser.add_argument('''--max_length''' ,						default=50 ,						type=UpperCamelCase ,						help='''Max length of the generated answers'''      )

							parser.add_argument(
							    '''--print_predictions''' ,						action='''store_true''' ,						help='''If True, prints predictions while evaluating.''' ,						)
							parser.add_argument(
							    '''--print_docs''' ,						action='''store_true''' ,						help='''If True, prints docs retried while generating.''' ,						)
							_a	     =				parser.parse_args()
							_a	     =				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )
							return args
def 		snake_case_    (UpperCamelCase       :  Dict      ):





							'''simple docstring'''


							_a	     =				{}
							if args.model_type is None:
														_a	     =				infer_model_type(args.model_name_or_path      )
														assert args.model_type is not None
							if args.model_type.startswith('''rag'''      ):
														_a	     =				RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
														_a	     =				args.n_docs
														if args.index_name is not None:
																					_a	     =				args.index_name
														if args.index_path is not None:
																					_a	     =				args.index_path
							else:
														_a	     =				BartForConditionalGeneration

							_a	     =				(
							    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
							    if args.eval_all_checkpoints
							    else [args.model_name_or_path]
							)

							logger.info('''Evaluate the following checkpoints: %s''' ,						UpperCamelCase      )

							_a	     =				get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
							_a	     =				evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

							for checkpoint in checkpoints:
														if os.path.exists(args.predictions_path      ) and (not args.recalculate):
																					logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path      )      )
																					score_fn(UpperCamelCase ,						args.predictions_path ,						args.gold_data_path      )
																					continue

														logger.info('''***** Running evaluation for {} *****'''.format(UpperCamelCase      )      )
														logger.info('''  Batch size = %d''' ,						args.eval_batch_size      )
														logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path      )      )

														if args.model_type.startswith('''rag'''      ):
																					_a	     =				RagRetriever.from_pretrained(UpperCamelCase ,						**UpperCamelCase      )
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						retriever=UpperCamelCase ,						**UpperCamelCase      )
																					model.retriever.init_retrieval()
														else:
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						**UpperCamelCase      )
														model.to(args.device      )

														with open(args.evaluation_set ,						'''r'''      ) as eval_file, open(args.predictions_path ,						'''w'''      ) as preds_file:
																					_a	     =				[]
																					for line in tqdm(UpperCamelCase      ):
																												questions.append(line.strip()      )
																												if len(UpperCamelCase      ) == args.eval_batch_size:
																																			_a	     =				evaluate_batch_fn(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
																																			preds_file.write('''\n'''.join(UpperCamelCase      ) + '''\n'''      )
																																			preds_file.flush()
																																			_a	     =				[]
																					if len(UpperCamelCase      ) > 0:
																												_a	     =				evaluate_batch_fn(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
																												preds_file.write('''\n'''.join(UpperCamelCase      )      )
																												preds_file.flush()

																					score_fn(UpperCamelCase ,						args.predictions_path ,						args.gold_data_path      )


if __name__ == "__main__":
				_snake_case      :      Optional[int]   							=		get_args()
				main(args)

