


'''simple docstring'''


import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							A_							:   Dict   			=						"""config.json"""






							@register_to_config
							def __init__(					self					,      __snake_case=2					,      __snake_case=5					,      __snake_case=(2, 5)					,      __snake_case="for diffusion"					,      __snake_case=[1, 3]					,      ):
											pass





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							A_							:   int   			=						"""config.json"""






							@register_to_config
							def __init__(					self					,      __snake_case=2					,      __snake_case=5					,      __snake_case=(2, 5)					,      __snake_case="for diffusion"					,      __snake_case=[1, 3]					,      ):
											pass





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							A_							:   List[str]   			=						"""config.json"""






							@register_to_config
							def __init__(					self					,      __snake_case=2					,      __snake_case=5					,      __snake_case=(2, 5)					,      __snake_case="for diffusion"					,      __snake_case=[1, 3]					,      __snake_case=[1, 3]					,      ):
											pass





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							A_							:   Union[str, Any]   			=						"""config.json"""






							@register_to_config
							def __init__(					self					,      __snake_case=2					,      __snake_case=5					,      __snake_case=(2, 5)					,      __snake_case="for diffusion"					,      __snake_case=[1, 5]					,      __snake_case=[5, 4]					,      ):
											pass





class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							def UpperCAmelCase_			(					self						):
											with self.assertRaises(__snake_case						):
															ConfigMixin.load_config("""dummy_path"""						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   SampleObject()
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# init ignore private arguments
											_SCREAMING_SNAKE_CASE				: Dict  	=   SampleObject(_name_or_path="""lalala"""						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# can override default
											_SCREAMING_SNAKE_CASE				: Dict  	=   SampleObject(c=6						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == 6
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# can use positional arguments.
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   SampleObject(1					,      c=6						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   obj.config
											assert config["a"] == 1
											assert config["b"] == 5
											assert config["c"] == 6
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   SampleObject()
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   obj.config

											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											with tempfile.TemporaryDirectory() as tmpdirname:
															obj.save_config(__snake_case						)
															_SCREAMING_SNAKE_CASE				: int  	=   SampleObject.from_config(SampleObject.load_config(__snake_case						)						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   new_obj.config

											# unfreeze configs
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   dict(__snake_case						)
											_SCREAMING_SNAKE_CASE				: int  	=   dict(__snake_case						)

											assert config.pop("""c"""						) == (2, 5)  # instantiated as tuple
											assert new_config.pop("""c"""						) == [2, 5]  # saved & loaded as list because of json
											config.pop("""_use_default_values"""						)
											assert config == new_config






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   DDIMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""						)

											assert ddim.__class__ == DDIMScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   EulerDiscreteScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""						)

											assert euler.__class__ == EulerDiscreteScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: str  	=   EulerAncestralDiscreteScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""						)

											assert euler.__class__ == EulerAncestralDiscreteScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   PNDMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""						)

											assert pndm.__class__ == PNDMScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[str]  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: str  	=   DDPMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""					,      prediction_type="""sample"""					,      beta_end=8					,      )

											with CaptureLogger(__snake_case						) as cap_logger_a:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""					,      beta_start=88						)

											assert ddpm.__class__ == DDPMScheduler
											assert ddpm.config.prediction_type == "sample"
											assert ddpm.config.beta_end == 8
											assert ddpm_a.config.beta_start == 88

											# no warning should be thrown
											assert cap_logger.out == ""
											assert cap_logger_a.out == ""






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   logging.get_logger("""diffusers.configuration_utils"""						)
											# 30 for warning
											logger.setLevel(30						)

											with CaptureLogger(__snake_case						) as cap_logger:
															_SCREAMING_SNAKE_CASE				: int  	=   DPMSolverMultistepScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""					,      subfolder="""scheduler"""						)

											assert dpm.__class__ == DPMSolverMultistepScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_			(					self						):
											# let's first save a config that should be in the form
											#    a=2,
											#    b=5,
											#    c=(2, 5),
											#    d="for diffusion",
											#    e=[1, 3],

											_SCREAMING_SNAKE_CASE				: List[str]  	=   SampleObject()

											_SCREAMING_SNAKE_CASE				: List[str]  	=   {k: v for k, v in config.config.items() if not k.startswith("""_"""						)}

											# make sure that default config has all keys in `_use_default_values`
											assert set(config_dict.keys()						) == set(config.config._use_default_values						)

											with tempfile.TemporaryDirectory() as tmpdirname:
															config.save_config(__snake_case						)

															# now loading it with SampleObject2 should put f into `_use_default_values`
															_SCREAMING_SNAKE_CASE				: Tuple  	=   SampleObjecta.from_config(__snake_case						)

															assert "f" in config._use_default_values
															assert config.f == [1, 3]

											# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
											# **BECAUSE** it is part of `config._use_default_values`
											_SCREAMING_SNAKE_CASE				: str  	=   SampleObjecta.from_config(config.config						)
											assert new_config.f == [5, 4]

											config.config._use_default_values.pop()
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   SampleObjecta.from_config(config.config						)
											assert new_config_a.f == [1, 3]

											# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
											assert new_config_a.e == [1, 3]








'''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


UpperCAmelCase_	:     str							     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				return max(metric_fn(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for gt in ground_truths  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]
				_SCREAMING_SNAKE_CASE				: str  	=   []

				if args.gold_data_mode == "qa":
								_SCREAMING_SNAKE_CASE				: Any  	=   pd.read_csv(SCREAMING_SNAKE_CASE__							,   sep="""\t"""							,   header=SCREAMING_SNAKE_CASE__  )
								for answer_list in data[1]:
												_SCREAMING_SNAKE_CASE				: str  	=   ast.literal_eval(SCREAMING_SNAKE_CASE__  )
												answers.append(SCREAMING_SNAKE_CASE__  )
				else:
								_SCREAMING_SNAKE_CASE				: 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				: List[Any]  	=   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				: str  	=   1_0_0.0 * em / total
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   1_0_0.0 * fa / total

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args.k
				_SCREAMING_SNAKE_CASE				: str  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]
				_SCREAMING_SNAKE_CASE				: str  	=   [line.strip() for line in open(SCREAMING_SNAKE_CASE__							,   """r"""  ).readlines()]

				_SCREAMING_SNAKE_CASE				: Tuple  	=   0
				for hypo, reference in zip(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								_SCREAMING_SNAKE_CASE				: List[str]  	=   set(hypo.split("""\t"""  )[:k]  )
								_SCREAMING_SNAKE_CASE				: Dict  	=   set(reference.split("""\t"""  )  )
								total += 1
								em += len(hypo_provenance & ref_provenance  ) / k

				_SCREAMING_SNAKE_CASE				: str  	=   1_0_0.0 * em / total
				logger.info(f"""Precision@{k}: {em: .2f}"""  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				def strip_title(SCREAMING_SNAKE_CASE__  ):
								if title.startswith("""\""""  ):
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   title[1:]
								if title.endswith("""\""""  ):
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   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				: str  	=   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				: int  	=   rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				_SCREAMING_SNAKE_CASE				: int  	=   []
				for docs in all_docs:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   [strip_title(SCREAMING_SNAKE_CASE__  ) for title in docs["""title"""]]
								provenance_strings.append("""\t""".join(SCREAMING_SNAKE_CASE__  )  )
				return provenance_strings







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				with torch.no_grad():
								_SCREAMING_SNAKE_CASE				: Tuple  	=   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				: Optional[Any]  	=   inputs_dict.input_ids.to(args.device  )
								_SCREAMING_SNAKE_CASE				: Dict  	=   inputs_dict.attention_mask.to(args.device  )
								_SCREAMING_SNAKE_CASE				: Union[str, 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				: Optional[Any]  	=   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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   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				: List[Any]  	=   parser.parse_args()
				_SCREAMING_SNAKE_CASE				: int  	=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
				return args







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				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				: Optional[int]  	=   RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
								_SCREAMING_SNAKE_CASE				: str  	=   args.n_docs
								if args.index_name is not None:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args.index_name
								if args.index_path is not None:
												_SCREAMING_SNAKE_CASE				: str  	=   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				: Dict  	=   get_scores if args.eval_mode == """e2e""" else get_precision_at_k
				_SCREAMING_SNAKE_CASE				: str  	=   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				: Any  	=   RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: int  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__							,   retriever=SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												model.retriever.init_retrieval()
								else:
												_SCREAMING_SNAKE_CASE				: Dict  	=   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				: List[Any]  	=   []
												for line in tqdm(SCREAMING_SNAKE_CASE__  ):
																questions.append(line.strip()  )
																if len(SCREAMING_SNAKE_CASE__  ) == args.eval_batch_size:
																				_SCREAMING_SNAKE_CASE				: Any  	=   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				: Union[str, Any]  	=   []
												if len(SCREAMING_SNAKE_CASE__  ) > 0:
																_SCREAMING_SNAKE_CASE				: 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__":
				UpperCAmelCase_	:     int							     =	get_args()
				main(args)




