"""simple docstring"""





import inspect
import unittest
import warnings
from math import ceil, floor

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

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,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.config_class(**self.inputs_dict)
											self.parent.assertTrue(hasattr(_A      ,    """hidden_sizes"""))
											self.parent.assertTrue(hasattr(_A      ,    """num_attention_heads"""))




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							str      ,    _A							:							Optional[int]      ,    _A							:							Dict=1_3      ,    _A							:							Dict=6_4      ,    _A							:							Any=3      ,    _A							:							Optional[Any]=3      ,    _A							:							str=2      ,    _A							:							List[str]=1      ,    _A							:							Dict=1_6      ,    _A							:							List[str]=[1_2_8, 2_5_6, 3_8_4]      ,    _A							:							Dict=[4, 6, 8]      ,    _A							:							List[Any]=[2, 3, 4]      ,    _A							:							int=[1_6, 1_6, 1_6]      ,    _A							:							Union[str, Any]=0      ,    _A							:							Optional[Any]=[2, 2, 2]      ,    _A							:							Dict=[2, 2, 2]      ,    _A							:							Any=0.02      ,    _A							:							List[str]=True      ,    _A							:							int=True      ,    _A							:							Optional[Any]=2      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    parent
											_SCREAMING_SNAKE_CASE		:						Dict			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_channels
											_SCREAMING_SNAKE_CASE		:						int			  =    kernel_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    stride
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    padding
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    hidden_sizes
											_SCREAMING_SNAKE_CASE		:						int			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						str			  =    depths
											_SCREAMING_SNAKE_CASE		:						Dict			  =    key_dim
											_SCREAMING_SNAKE_CASE		:						int			  =    drop_path_rate
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    patch_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    attention_ratio
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mlp_ratio
											_SCREAMING_SNAKE_CASE		:						int			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						Dict			  =    [
											    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_labels
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    initializer_range





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											if self.use_labels:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ids_tensor([self.batch_size]      ,    self.num_labels)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.get_config()
											return config, pixel_values, labels





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return LevitConfig(
											    image_size=self.image_size      ,    num_channels=self.num_channels      ,    kernel_size=self.kernel_size      ,    stride=self.stride      ,    padding=self.padding      ,    patch_size=self.patch_size      ,    hidden_sizes=self.hidden_sizes      ,    num_attention_heads=self.num_attention_heads      ,    depths=self.depths      ,    key_dim=self.key_dim      ,    drop_path_rate=self.drop_path_rate      ,    mlp_ratio=self.mlp_ratio      ,    attention_ratio=self.attention_ratio      ,    initializer_range=self.initializer_range      ,    down_ops=self.down_ops      ,    )





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							Optional[Any]      ,    _A							:							Optional[int]      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    LevitModel(config=_A)
											model.to(_A)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    (self.image_size, self.image_size)
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_size[0], image_size[1]
											for _ in range(4):
																		_SCREAMING_SNAKE_CASE		:						str			  =    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,    (self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])      ,    )





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							Any      ,    _A							:							Optional[int]      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						int			  =    LevitForImageClassification(_A)
											model.to(_A)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						str			  =    model(_A      ,    labels=_A)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    {"""pixel_values""": pixel_values}
											return config, inputs_dict




@require_torch
class  _snake_case		(    __snake_case   ,       __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				a			=						(
				    {
				        "feature-extraction": LevitModel,
				        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				a			=						False
				a			=						False
				a			=						False
				a			=						False
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    LevitModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Any			  =    ConfigTester(self      ,    config_class=_A      ,    has_text_modality=_A      ,    hidden_size=3_7)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											return





				@unittest.skip(reason="""Levit does not use inputs_embeds""")
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											pass





				@unittest.skip(reason="""Levit does not support input and output embeddings""")
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											pass





				@unittest.skip(reason="""Levit does not output attentions""")
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs_for_common()

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

																		_SCREAMING_SNAKE_CASE		:						Dict			  =    ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											def check_hidden_states_output(_A							:							Optional[Any]      ,    _A							:							int      ,    _A							:							List[str]):
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    model_class(_A)
																		model.to(_A)
																		model.eval()

																		with torch.no_grad():
																									_SCREAMING_SNAKE_CASE		:						Any			  =    model(**self._prepare_for_class(_A      ,    _A))

																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    outputs.hidden_states

																		_SCREAMING_SNAKE_CASE		:						Dict			  =    len(self.model_tester.depths) + 1
																		self.assertEqual(len(_A)      ,    _A)

																		_SCREAMING_SNAKE_CASE		:						int			  =    (self.model_tester.image_size, self.model_tester.image_size)
																		_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    image_size[0], image_size[1]
																		for _ in range(4):
																									_SCREAMING_SNAKE_CASE		:						str			  =    floor(
																									    (
																									        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																									        / self.model_tester.stride
																									    )
																									    + 1)
																									_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    floor(
																									    (
																									        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																									        / self.model_tester.stride
																									    )
																									    + 1)
																		# verify the first hidden states (first block)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:])      ,    [
																		        height * width,
																		        self.model_tester.hidden_sizes[0],
																		    ]      ,    )

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_SCREAMING_SNAKE_CASE		:						int			  =    True
																		check_hidden_states_output(_A      ,    _A      ,    _A)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_SCREAMING_SNAKE_CASE		:						str			  =    True

																		check_hidden_states_output(_A      ,    _A      ,    _A)





				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							int      ,    _A							:							Optional[Any]      ,    _A							:							Optional[Any]=False):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    super()._prepare_for_class(_A      ,    _A      ,    return_labels=_A)

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

											return inputs_dict





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											if not self.model_tester.is_training:
																		return

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    True

											for model_class in self.all_model_classes:
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if (
																		    model_class in get_values(_A)
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																									continue
																		_SCREAMING_SNAKE_CASE		:						str			  =    model_class(_A)
																		model.to(_A)
																		model.train()
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self._prepare_for_class(_A      ,    _A      ,    return_labels=_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(**_A).loss
																		loss.backward()





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																		return

											_SCREAMING_SNAKE_CASE		:						int			  =    False
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    True

											for model_class in self.all_model_classes:
																		if model_class in get_values(_A) or not model_class.supports_gradient_checkpointing:
																									continue
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																									continue
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_class(_A)
																		model.gradient_checkpointing_enable()
																		model.to(_A)
																		model.train()
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    self._prepare_for_class(_A      ,    _A      ,    return_labels=_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(**_A).loss
																		loss.backward()





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [
											    {"""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(_A),
																		    ]
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																									continue

																		for problem_type in problem_types:
																									with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""):
																																_SCREAMING_SNAKE_CASE		:						Any			  =    problem_type["""title"""]
																																_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    problem_type["""num_labels"""]

																																_SCREAMING_SNAKE_CASE		:						Any			  =    model_class(_A)
																																model.to(_A)
																																model.train()

																																_SCREAMING_SNAKE_CASE		:						Dict			  =    self._prepare_for_class(_A      ,    _A      ,    return_labels=_A)

																																if problem_type["num_labels"] > 1:
																																							_SCREAMING_SNAKE_CASE		:						List[str]			  =    inputs["""labels"""].unsqueeze(1).repeat(1      ,    problem_type["""num_labels"""])

																																_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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=_A) as warning_list:
																																							_SCREAMING_SNAKE_CASE		:						Dict			  =    model(**_A).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							:							int):



											"""simple docstring"""







											for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    LevitModel.from_pretrained(_A)
																		self.assertIsNotNone(_A)



def      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE		:						int			  =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
							return image




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







				"""simple docstring"""






				@cached_property
				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])





				@slow
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
											    _A)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.default_image_processor
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    prepare_img()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    image_processor(images=_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**_A)

											# verify the logits
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.Size((1, 1_0_0_0))
											self.assertEqual(outputs.logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor([1.0_448, -0.3_745, -1.8_317]).to(_A)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,    _A      ,    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


lowerCAmelCase_     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							return max(metric_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for gt in ground_truths  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

							if args.gold_data_mode == "qa":
														_SCREAMING_SNAKE_CASE		:						int			  =    pd.read_csv(__SCREAMING_SNAKE_CASE     ,	sep="""\t"""     ,	header=__SCREAMING_SNAKE_CASE  )
														for answer_list in data[1]:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ast.literal_eval(__SCREAMING_SNAKE_CASE  )
																					answers.append(__SCREAMING_SNAKE_CASE  )
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [[reference] for reference in references]

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0
							for prediction, ground_truths in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														total += 1
														em += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														fa += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    1_00.0 * em / total
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_00.0 * fa / total

							logger.info(F"""F1: {fa:.2f}"""  )
							logger.info(F"""EM: {em:.2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    args.k
							_SCREAMING_SNAKE_CASE		:						int			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Any			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0
							for hypo, reference in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    set(hypo.split("""\t"""  )[:k]  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    set(reference.split("""\t"""  )  )
														total += 1
														em += len(hypo_provenance & ref_provenance  ) / k

							_SCREAMING_SNAKE_CASE		:						int			  =    1_00.0 * em / total
							logger.info(F"""Precision@{k}: {em: .2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							def strip_title(__SCREAMING_SNAKE_CASE  ):
														if title.startswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    title[1:]
														if title.endswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    title[:-1]
														return title

							_SCREAMING_SNAKE_CASE		:						Dict			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)["""input_ids"""].to(args.device  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    question_enc_outputs[0]

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rag_model.retriever(
							    __SCREAMING_SNAKE_CASE     ,	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"""     ,	)
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for docs in all_docs:
														_SCREAMING_SNAKE_CASE		:						str			  =    [strip_title(__SCREAMING_SNAKE_CASE  ) for title in docs["""title"""]]
														provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE  )  )
							return provenance_strings



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.input_ids.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.attention_mask.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.generate(  # rag_model overwrites generate
														    __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	num_beams=args.num_beams     ,	min_length=args.min_length     ,	max_length=args.max_length     ,	early_stopping=__SCREAMING_SNAKE_CASE     ,	num_return_sequences=1     ,	bad_words_ids=[[0, 0]]     ,	)
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    rag_model.retriever.generator_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,	skip_special_tokens=__SCREAMING_SNAKE_CASE  )

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

														return answers



def      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    argparse.ArgumentParser()
							parser.add_argument(
							    """--model_type"""     ,	choices=["""rag_sequence""", """rag_token""", """bart"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	choices=["""exact""", """compressed""", """legacy"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""RAG model retriever type"""     ,	)
							parser.add_argument(
							    """--index_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Path to the retrieval index"""     ,	)
							parser.add_argument("""--n_docs"""     ,	default=5     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Number of retrieved docs"""  )
							parser.add_argument(
							    """--model_name_or_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""     ,	)
							parser.add_argument(
							    """--eval_mode"""     ,	choices=["""e2e""", """retrieval"""]     ,	default="""e2e"""     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
							        """ precision@k."""
							    )     ,	)
							parser.add_argument("""--k"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""k for the precision@k calculation"""  )
							parser.add_argument(
							    """--evaluation_set"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a file containing evaluation samples"""     ,	)
							parser.add_argument(
							    """--gold_data_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a tab-separated file with gold samples"""     ,	)
							parser.add_argument(
							    """--gold_data_mode"""     ,	default="""qa"""     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	help="""Number of beams to be used when generating answers"""     ,	)
							parser.add_argument("""--min_length"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Min length of the generated answers"""  )
							parser.add_argument("""--max_length"""     ,	default=50     ,	type=__SCREAMING_SNAKE_CASE     ,	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."""     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
							return args



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							if args.model_type is None:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    infer_model_type(args.model_name_or_path  )
														assert args.model_type is not None
							if args.model_type.startswith("""rag"""  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.n_docs
														if args.index_name is not None:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.index_name
														if args.index_path is not None:
																					_SCREAMING_SNAKE_CASE		:						Any			  =    args.index_path
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    BartForConditionalGeneration

							_SCREAMING_SNAKE_CASE		:						int			  =    (
							    [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"""     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_scores if args.eval_mode == """e2e""" else get_precision_at_k
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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(__SCREAMING_SNAKE_CASE     ,	args.predictions_path     ,	args.gold_data_path  )
																					continue

														logger.info("""***** Running evaluation for {} *****""".format(__SCREAMING_SNAKE_CASE  )  )
														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"""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	retriever=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					model.retriever.init_retrieval()
														else:
																					_SCREAMING_SNAKE_CASE		:						str			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
														model.to(args.device  )

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

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


if __name__ == "__main__":
			lowerCAmelCase_     =	get_args()
			main(args)


