




'''simple docstring'''


import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_MAPPING,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				)
				from transformers.models.efficientformer.modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_vision_available():
				from PIL import Image

				from transformers import EfficientFormerImageProcessor

class 			A    :







							def __init__(  self       :  int							,			lowerCAmelCase_       :  Any							,			lowerCAmelCase_       :  int = 13							,			lowerCAmelCase_       :  int = 64							,			lowerCAmelCase_       :  int = 2							,			lowerCAmelCase_       :  int = 3							,			lowerCAmelCase_       :  int = 3							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  int = 1_28							,			lowerCAmelCase_       :  Optional[int]=[16, 32, 64, 1_28]							,			lowerCAmelCase_       :  int = 7							,			lowerCAmelCase_       :  int = 4							,			lowerCAmelCase_       :  int = 37							,			lowerCAmelCase_       :  str = "gelu"							,			lowerCAmelCase_       :  float = 0.1							,			lowerCAmelCase_       :  float = 0.1							,			lowerCAmelCase_       :  int = 10							,			lowerCAmelCase_       :  float = 0.0_2							,			lowerCAmelCase_       :  int = 2							,			lowerCAmelCase_       :  int = 1							,			lowerCAmelCase_       :  int = 1_28							,			lowerCAmelCase_       :  List[int] = [2, 2, 2, 2]							,			lowerCAmelCase_       :  int = 2							,			lowerCAmelCase_       :  int = 2							,			)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				parent
														_a	     =				batch_size
														_a	     =				image_size
														_a	     =				patch_size
														_a	     =				num_channels
														_a	     =				is_training
														_a	     =				use_labels
														_a	     =				hidden_size
														_a	     =				num_hidden_layers
														_a	     =				num_attention_heads
														_a	     =				intermediate_size
														_a	     =				hidden_act
														_a	     =				hidden_dropout_prob
														_a	     =				attention_probs_dropout_prob
														_a	     =				type_sequence_label_size
														_a	     =				initializer_range
														_a	     =				encoder_stride
														_a	     =				num_attention_outputs
														_a	     =				embed_dim
														_a	     =				embed_dim + 1
														_a	     =				resolution
														_a	     =				depths
														_a	     =				hidden_sizes
														_a	     =				dim
														_a	     =				mlp_expansion_ratio







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


														"""simple docstring"""


														_a	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

														_a	     =				None
														if self.use_labels:
																					_a	     =				ids_tensor([self.batch_size]							,			self.type_sequence_label_size					)

														_a	     =				self.get_config()
														return config, pixel_values, labels







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							int:


														"""simple docstring"""


														return EfficientFormerConfig(
														    image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			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							,			is_decoder=lowerCAmelCase_							,			initializer_range=self.initializer_range							,			encoder_stride=self.encoder_stride							,			resolution=self.resolution							,			depths=self.depths							,			hidden_sizes=self.hidden_sizes							,			dim=self.dim							,			mlp_expansion_ratio=self.mlp_expansion_ratio							,			)







							def    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Tuple							,			lowerCAmelCase_       :  Union[str, Any]					)			->							Optional[int]:


														"""simple docstring"""


														_a	     =				EfficientFormerModel(config=lowerCAmelCase_					)
														model.to(lowerCAmelCase_					)
														model.eval()
														_a	     =				model(lowerCAmelCase_					)
														self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)					)







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  Union[str, Any]							,			lowerCAmelCase_       :  Union[str, Any]							,			lowerCAmelCase_       :  List[str]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				self.type_sequence_label_size
														_a	     =				EfficientFormerForImageClassification(lowerCAmelCase_					)
														model.to(lowerCAmelCase_					)
														model.eval()
														_a	     =				model(lowerCAmelCase_							,			labels=lowerCAmelCase_					)
														self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)					)

														# test greyscale images
														_a	     =				1
														_a	     =				EfficientFormerForImageClassification(lowerCAmelCase_					)
														model.to(lowerCAmelCase_					)
														model.eval()

														_a	     =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
														_a	     =				model(lowerCAmelCase_					)
														self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)					)







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Dict:


														"""simple docstring"""


														_a	     =				self.prepare_config_and_inputs()
														(
														    (
														    _a
														)     ,							(
														    _a
														)     ,							(
														    _a
														)     ,							
														)	     =				config_and_inputs
														_a	     =				{'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_torch
class 			A    (		_a     ,_a     ,unittest.TestCase    ):
							lowercase_							=				(
							    (
							        EfficientFormerModel,
							        EfficientFormerForImageClassificationWithTeacher,
							        EfficientFormerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_							=				(
							    {
							        'feature-extraction': EfficientFormerModel,
							        'image-classification': (
							            EfficientFormerForImageClassification,
							            EfficientFormerForImageClassificationWithTeacher,
							        ),
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_							=				False

							lowercase_							=				False
							lowercase_							=				False
							lowercase_							=				False







							def    __lowerCAmelCase			(  self       :  str					)			->							Dict:


														"""simple docstring"""


														_a	     =				EfficientFormerModelTester(self					)
														_a	     =				ConfigTester(
														    self							,			config_class=lowerCAmelCase_							,			has_text_modality=lowerCAmelCase_							,			hidden_size=37					)







							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							int:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''					)
							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							List[Any]:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''					)
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Tuple:


														"""simple docstring"""


														pass







							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							List[Any]:


														"""simple docstring"""


														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a	     =				model_class(lowerCAmelCase_					)
																					_a	     =				inspect.signature(model.forward					)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_a	     =				[*signature.parameters.keys()]

																					_a	     =				['''pixel_values''']
																					self.assertListEqual(arg_names[:1]							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Any:


														"""simple docstring"""


														def check_hidden_states_output(lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  Tuple					):
																					_a	     =				model_class(lowerCAmelCase_					)
																					model.to(lowerCAmelCase_					)
																					model.eval()

																					with torch.no_grad():
																												_a	     =				model(**self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_					)					)

																					_a	     =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					_a	     =				getattr(
																					    self.model_tester							,			'''expected_num_hidden_layers'''							,			self.model_tester.num_hidden_layers + 1					)
																					self.assertEqual(len(lowerCAmelCase_					)							,			lowerCAmelCase_					)

																					if hasattr(self.model_tester							,			'''encoder_seq_length'''					):
																												_a	     =				self.model_tester.encoder_seq_length
																												if hasattr(self.model_tester							,			'''chunk_length'''					) and self.model_tester.chunk_length > 1:
																																			_a	     =				seq_length * self.model_tester.chunk_length
																					else:
																												_a	     =				self.model_tester.seq_length

																					self.assertListEqual(
																					    list(hidden_states[-1].shape[-2:]					)							,			[seq_length, self.model_tester.hidden_size]							,			)

																					if config.is_encoder_decoder:
																												_a	     =				outputs.decoder_hidden_states

																												self.assertIsInstance(lowerCAmelCase_							,			(list, tuple)					)
																												self.assertEqual(len(lowerCAmelCase_					)							,			lowerCAmelCase_					)
																												_a	     =				getattr(self.model_tester							,			'''seq_length'''							,			lowerCAmelCase_					)
																												_a	     =				getattr(self.model_tester							,			'''decoder_seq_length'''							,			lowerCAmelCase_					)

																												self.assertListEqual(
																												    list(hidden_states[-1].shape[-2:]					)							,			[decoder_seq_length, self.model_tester.hidden_size]							,			)

														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a	     =				True
																					check_hidden_states_output(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					_a	     =				True

																					check_hidden_states_output(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  List[Any]=False					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				super()._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_							,			return_labels=lowerCAmelCase_					)

														if return_labels:
																					if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																												del inputs_dict["labels"]

														return inputs_dict







							def    __lowerCAmelCase			(  self       :  Dict					)			->							int:


														"""simple docstring"""


														_a	     =				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_					)







							@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''					)
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_					)







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


														"""simple docstring"""


														_a	     =				self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							List[Any]:


														"""simple docstring"""


														if not self.model_tester.is_training:
																					return

														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()
														_a	     =				True

														for model_class in self.all_model_classes:
																					# EfficientFormerForImageClassificationWithTeacher supports inference-only
																					if (
																					    model_class in get_values(lowerCAmelCase_					)
																					    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
																					):
																												continue
																					_a	     =				model_class(lowerCAmelCase_					)
																					model.to(lowerCAmelCase_					)
																					model.train()
																					_a	     =				self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_							,			return_labels=lowerCAmelCase_					)
																					_a	     =				model(**lowerCAmelCase_					).loss
																					loss.backward()







							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Dict:


														"""simple docstring"""


														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()

														_a	     =				[
														    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
														    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
														    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
														]

														for model_class in self.all_model_classes:
																					if (
																					    model_class
																					    not in [
																					        *get_values(lowerCAmelCase_					),
																					    ]
																					    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
																					):
																												continue

																					for problem_type in problem_types:
																												with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'					):
																																			_a	     =				problem_type['''title''']
																																			_a	     =				problem_type['''num_labels''']

																																			_a	     =				model_class(lowerCAmelCase_					)
																																			model.to(lowerCAmelCase_					)
																																			model.train()

																																			_a	     =				self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_							,			return_labels=lowerCAmelCase_					)

																																			if problem_type["num_labels"] > 1:
																																										_a	     =				inputs['''labels'''].unsqueeze(1					).repeat(1							,			problem_type['''num_labels''']					)

																																			_a	     =				inputs['''labels'''].to(problem_type['''dtype''']					)

																																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																			# they have the same size." which is a symptom something in wrong for the regression problem.
																																			# See https://github.com/huggingface/transformers/issues/11780
																																			with warnings.catch_warnings(record=lowerCAmelCase_					) as warning_list:
																																										_a	     =				model(**lowerCAmelCase_					).loss
																																			for w in warning_list:
																																										if "Using a target size that is different to the input size" in str(w.message					):
																																																	raise ValueError(
																																																	    F'Something is going wrong in the regression problem: intercepted {w.message}'					)

																																			loss.backward()







							@slow
							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Optional[Any]:


														"""simple docstring"""


														for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_a	     =				EfficientFormerModel.from_pretrained(lowerCAmelCase_					)
																					self.assertIsNotNone(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							List[str]:


														"""simple docstring"""


														_a     ,							_a	     =				self.model_tester.prepare_config_and_inputs_for_common()
														_a	     =				True

														_a	     =				getattr(self.model_tester							,			'''seq_length'''							,			lowerCAmelCase_					)
														_a	     =				getattr(self.model_tester							,			'''encoder_seq_length'''							,			lowerCAmelCase_					)
														_a	     =				getattr(self.model_tester							,			'''key_length'''							,			lowerCAmelCase_					)
														_a	     =				getattr(self.model_tester							,			'''chunk_length'''							,			lowerCAmelCase_					)

														if chunk_length is not None and hasattr(self.model_tester							,			'''num_hashes'''					):
																					_a	     =				encoder_seq_length * self.model_tester.num_hashes

														for model_class in self.all_model_classes:
																					_a	     =				True
																					_a	     =				False
																					_a	     =				True
																					_a	     =				model_class(lowerCAmelCase_					)
																					model.to(lowerCAmelCase_					)
																					model.eval()
																					with torch.no_grad():
																												_a	     =				model(**self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_					)					)
																					_a	     =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																					self.assertEqual(len(lowerCAmelCase_					)							,			self.model_tester.num_attention_outputs					)

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					_a	     =				True
																					_a	     =				model_class(lowerCAmelCase_					)
																					model.to(lowerCAmelCase_					)
																					model.eval()
																					with torch.no_grad():
																												_a	     =				model(**self._prepare_for_class(lowerCAmelCase_							,			lowerCAmelCase_					)					)
																					_a	     =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																					self.assertEqual(len(lowerCAmelCase_					)							,			self.model_tester.num_attention_outputs					)

																					if chunk_length is not None:
																												self.assertListEqual(
																												    list(attentions[0].shape[-4:]					)							,			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]							,			)
																					else:
																												self.assertListEqual(
																												    list(attentions[0].shape[-3:]					)							,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							,			)



def 		snake_case_    ():





							'''simple docstring'''


							_a	     =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
							return image

@require_torch
@require_vision
class 			A    (		unittest.TestCase    ):







							@cached_property
							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							int:


														"""simple docstring"""


														return (
														    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''					)
														    if is_vision_available()
														    else None
														)







							@slow
							def    __lowerCAmelCase			(  self       :  Any					)			->							List[str]:


														"""simple docstring"""


														_a	     =				EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''					).to(
														    lowerCAmelCase_					)

														_a	     =				self.default_image_processor
														_a	     =				prepare_img()
														_a	     =				image_processor(images=lowerCAmelCase_							,			return_tensors='''pt'''					).to(lowerCAmelCase_					)

														# forward pass
														with torch.no_grad():
																					_a	     =				model(**lowerCAmelCase_					)

														# verify the logits
														_a	     =				(1, 10_00)
														self.assertEqual(outputs.logits.shape							,			lowerCAmelCase_					)

														_a	     =				torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]					).to(lowerCAmelCase_					)
														self.assertTrue(torch.allclose(outputs.logits[0][:3]							,			lowerCAmelCase_							,			atol=1e-4					)					)







							@slow
							def    __lowerCAmelCase			(  self       :  str					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				EfficientFormerForImageClassificationWithTeacher.from_pretrained(
														    '''snap-research/efficientformer-l1-300'''					).to(lowerCAmelCase_					)

														_a	     =				self.default_image_processor
														_a	     =				prepare_img()
														_a	     =				image_processor(images=lowerCAmelCase_							,			return_tensors='''pt'''					).to(lowerCAmelCase_					)

														# forward pass
														with torch.no_grad():
																					_a	     =				model(**lowerCAmelCase_					)

														# verify the logits
														_a	     =				(1, 10_00)
														self.assertEqual(outputs.logits.shape							,			lowerCAmelCase_					)

														_a	     =				torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]					).to(lowerCAmelCase_					)
														self.assertTrue(torch.allclose(outputs.logits[0][:3]							,			lowerCAmelCase_							,			atol=1e-4					)					)







'''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)

