



import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput







class      __lowerCAmelCase       (     nn.Module		):



			def __init__(		self  :    Optional[int]					,						A  :    Any					,						A  :    Union[str, Any]=1.0)  ->       Dict:



						"""simple docstring"""






						super().__init__()

						_UpperCAmelCase									=  cut_size
						_UpperCAmelCase									=  cut_power



			def        _lowerCamelCase			(		self  :    Union[str, Any]					,						A  :    Dict					,						A  :    Any)  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  pixel_values.shape[2:4]
						_UpperCAmelCase									=  min(A					,						A)
						_UpperCAmelCase									=  min(A					,						A					,						self.cut_size)
						_UpperCAmelCase									=  []
						for _ in range(A):
									_UpperCAmelCase									=  int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
									_UpperCAmelCase									=  torch.randint(0					,						sideX - size + 1					,						())
									_UpperCAmelCase									=  torch.randint(0					,						sideY - size + 1					,						())
									_UpperCAmelCase									=  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(A					,						self.cut_size))
						return torch.cat(A)





def 				A      (	_UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Dict       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  F.normalize(_UpperCAmelCase				,  dim=-1       )
			_UpperCAmelCase									=  F.normalize(_UpperCAmelCase				,  dim=-1       )
			return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )





def 				A      (	_UpperCAmelCase							:  Optional[Any]				,  _UpperCAmelCase							:  Optional[Any]       )							->					List[Any]:







			'''simple docstring'''
			for param in model.parameters():
						_UpperCAmelCase									=  value







class      __lowerCAmelCase       (     A		):



			def __init__(		self  :    Tuple					,						A  :    AutoencoderKL					,						A  :    CLIPTextModel					,						A  :    CLIPModel					,						A  :    CLIPTokenizer					,						A  :    UNetaDConditionModel					,						A  :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]					,						A  :    CLIPImageProcessor					,						)  ->       Dict:



						"""simple docstring"""






						super().__init__()
						self.register_modules(
						    vae=A					,						text_encoder=A					,						clip_model=A					,						tokenizer=A					,						unet=A					,						scheduler=A					,						feature_extractor=A					,						)

						_UpperCAmelCase									=  transforms.Normalize(mean=feature_extractor.image_mean					,						std=feature_extractor.image_std)
						_UpperCAmelCase									=  (
						    feature_extractor.size
						    if isinstance(feature_extractor.size					,						A)
						    else feature_extractor.size['shortest_edge']
						)
						_UpperCAmelCase									=  MakeCutouts(self.cut_out_size)

						set_requires_grad(self.text_encoder					,						A)
						set_requires_grad(self.clip_model					,						A)



			def        _lowerCamelCase			(		self  :    int					,						A  :    Optional[Union[str, int]] = "auto")  ->       int:



						"""simple docstring"""






						if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									_UpperCAmelCase									=  self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(A)



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



						"""simple docstring"""






						self.enable_attention_slicing(A)



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



						"""simple docstring"""






						set_requires_grad(self.vae					,						A)



			def        _lowerCamelCase			(		self  :    Optional[int])  ->       str:



						"""simple docstring"""






						set_requires_grad(self.vae					,						A)



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



						"""simple docstring"""






						set_requires_grad(self.unet					,						A)



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



						"""simple docstring"""






						set_requires_grad(self.unet					,						A)



			@torch.enable_grad()
			def        _lowerCamelCase			(		self  :    Dict					,						A  :    Tuple					,						A  :    str					,						A  :    Tuple					,						A  :    str					,						A  :    Dict					,						A  :    int					,						A  :    Union[str, Any]					,						A  :    Optional[Any]					,						A  :    Optional[Any]=True					,						)  ->       int:



						"""simple docstring"""






						_UpperCAmelCase									=  latents.detach().requires_grad_()

						_UpperCAmelCase									=  self.scheduler.scale_model_input(A					,						A)

						# predict the noise residual
						_UpperCAmelCase									=  self.unet(A					,						A					,						encoder_hidden_states=A).sample

						if isinstance(self.scheduler					,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
									_UpperCAmelCase									=  self.scheduler.alphas_cumprod[timestep]
									_UpperCAmelCase									=  1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									_UpperCAmelCase									=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									_UpperCAmelCase									=  torch.sqrt(A)
									_UpperCAmelCase									=  pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler					,						A):
									_UpperCAmelCase									=  self.scheduler.sigmas[index]
									_UpperCAmelCase									=  latents - sigma * noise_pred
						else:
									raise ValueError(F"scheduler type {type(self.scheduler)} not supported")

						_UpperCAmelCase									=  1 / self.vae.config.scaling_factor * sample
						_UpperCAmelCase									=  self.vae.decode(A).sample
						_UpperCAmelCase									=  (image / 2 + 0.5).clamp(0					,						1)

						if use_cutouts:
									_UpperCAmelCase									=  self.make_cutouts(A					,						A)
						else:
									_UpperCAmelCase									=  transforms.Resize(self.cut_out_size)(A)
						_UpperCAmelCase									=  self.normalize(A).to(latents.dtype)

						_UpperCAmelCase									=  self.clip_model.get_image_features(A)
						_UpperCAmelCase									=  image_embeddings_clip / image_embeddings_clip.norm(p=2					,						dim=-1					,						keepdim=A)

						if use_cutouts:
									_UpperCAmelCase									=  spherical_dist_loss(A					,						A)
									_UpperCAmelCase									=  dists.view([num_cutouts, sample.shape[0], -1])
									_UpperCAmelCase									=  dists.sum(2).mean(0).sum() * clip_guidance_scale
						else:
									_UpperCAmelCase									=  spherical_dist_loss(A					,						A).mean() * clip_guidance_scale

						_UpperCAmelCase									=  -torch.autograd.grad(A					,						A)[0]

						if isinstance(self.scheduler					,						A):
									_UpperCAmelCase									=  latents.detach() + grads * (sigma**2)
									_UpperCAmelCase									=  noise_pred_original
						else:
									_UpperCAmelCase									=  noise_pred_original - torch.sqrt(A) * grads
						return noise_pred, latents



			@torch.no_grad()
			def __call__(		self  :    str					,						A  :    Union[str, List[str]]					,						A  :    Optional[int] = 5_12					,						A  :    Optional[int] = 5_12					,						A  :    Optional[int] = 50					,						A  :    Optional[float] = 7.5					,						A  :    Optional[int] = 1					,						A  :    float = 0.0					,						A  :    Optional[float] = 1_00					,						A  :    Optional[Union[str, List[str]]] = None					,						A  :    Optional[int] = 4					,						A  :    Optional[bool] = True					,						A  :    Optional[torch.Generator] = None					,						A  :    Optional[torch.FloatTensor] = None					,						A  :    Optional[str] = "pil"					,						A  :    bool = True					,						)  ->       Optional[Any]:



						"""simple docstring"""






						if isinstance(A					,						A):
									_UpperCAmelCase									=  1
						elif isinstance(A					,						A):
									_UpperCAmelCase									=  len(A)
						else:
									raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A)}")

						if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

						# get prompt text embeddings
						_UpperCAmelCase									=  self.tokenizer(
						    A					,						padding='max_length'					,						max_length=self.tokenizer.model_max_length					,						truncation=A					,						return_tensors='pt'					,						)
						_UpperCAmelCase									=  self.text_encoder(text_input.input_ids.to(self.device))[0]
						# duplicate text embeddings for each generation per prompt
						_UpperCAmelCase									=  text_embeddings.repeat_interleave(A					,						dim=0)

						if clip_guidance_scale > 0:
									if clip_prompt is not None:
												_UpperCAmelCase									=  self.tokenizer(
												    A					,						padding='max_length'					,						max_length=self.tokenizer.model_max_length					,						truncation=A					,						return_tensors='pt'					,						).input_ids.to(self.device)
									else:
												_UpperCAmelCase									=  text_input.input_ids.to(self.device)
									_UpperCAmelCase									=  self.clip_model.get_text_features(A)
									_UpperCAmelCase									=  text_embeddings_clip / text_embeddings_clip.norm(p=2					,						dim=-1					,						keepdim=A)
									# duplicate text embeddings clip for each generation per prompt
									_UpperCAmelCase									=  text_embeddings_clip.repeat_interleave(A					,						dim=0)

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						_UpperCAmelCase									=  guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
									_UpperCAmelCase									=  text_input.input_ids.shape[-1]
									_UpperCAmelCase									=  self.tokenizer(['']					,						padding='max_length'					,						max_length=A					,						return_tensors='pt')
									_UpperCAmelCase									=  self.text_encoder(uncond_input.input_ids.to(self.device))[0]
									# duplicate unconditional embeddings for each generation per prompt
									_UpperCAmelCase									=  uncond_embeddings.repeat_interleave(A					,						dim=0)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									_UpperCAmelCase									=  torch.cat([uncond_embeddings, text_embeddings])

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						_UpperCAmelCase									=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						_UpperCAmelCase									=  text_embeddings.dtype
						if latents is None:
									if self.device.type == "mps":
												# randn does not work reproducibly on mps
												_UpperCAmelCase									=  torch.randn(A					,						generator=A					,						device='cpu'					,						dtype=A).to(
												    self.device)
									else:
												_UpperCAmelCase									=  torch.randn(A					,						generator=A					,						device=self.device					,						dtype=A)
						else:
									if latents.shape != latents_shape:
												raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
									_UpperCAmelCase									=  latents.to(self.device)

						# set timesteps
						_UpperCAmelCase									=  'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
						_UpperCAmelCase									=  {}
						if accepts_offset:
									_UpperCAmelCase									=  1

						self.scheduler.set_timesteps(A					,						**A)

						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						_UpperCAmelCase									=  self.scheduler.timesteps.to(self.device)

						# scale the initial noise by the standard deviation required by the scheduler
						_UpperCAmelCase									=  latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						_UpperCAmelCase									=  'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
						_UpperCAmelCase									=  {}
						if accepts_eta:
									_UpperCAmelCase									=  eta

						# check if the scheduler accepts generator
						_UpperCAmelCase									=  'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
						if accepts_generator:
									_UpperCAmelCase									=  generator

						for i, t in enumerate(self.progress_bar(A)):
									# expand the latents if we are doing classifier free guidance
									_UpperCAmelCase									=  torch.cat([latents] * 2) if do_classifier_free_guidance else latents
									_UpperCAmelCase									=  self.scheduler.scale_model_input(A					,						A)

									# predict the noise residual
									_UpperCAmelCase									=  self.unet(A					,						A					,						encoder_hidden_states=A).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
												_UpperCAmelCase			,	_UpperCAmelCase									=  noise_pred.chunk(2)
												_UpperCAmelCase									=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
												_UpperCAmelCase									=  (
												    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
												)
												_UpperCAmelCase			,	_UpperCAmelCase									=  self.cond_fn(
												    A					,						A					,						A					,						A					,						A					,						A					,						A					,						A					,						A					,						)

									# compute the previous noisy sample x_t -> x_t-1
									_UpperCAmelCase									=  self.scheduler.step(A					,						A					,						A					,						**A).prev_sample

						# scale and decode the image latents with vae
						_UpperCAmelCase									=  1 / self.vae.config.scaling_factor * latents
						_UpperCAmelCase									=  self.vae.decode(A).sample

						_UpperCAmelCase									=  (image / 2 + 0.5).clamp(0					,						1)
						_UpperCAmelCase									=  image.cpu().permute(0					,						2					,						3					,						1).numpy()

						if output_type == "pil":
									_UpperCAmelCase									=  self.numpy_to_pil(A)

						if not return_dict:
									return (image, None)

						return StableDiffusionPipelineOutput(images=A					,						nsfw_content_detected=A)





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

transformers_logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''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 				A      (	_UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Optional[Any]       )							->					Dict:







			'''simple docstring'''
			return max(metric_fn(_UpperCAmelCase				,  _UpperCAmelCase       ) for gt in ground_truths       )





def 				A      (	_UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Optional[Any]				,  _UpperCAmelCase							:  str       )							->					Any:







			'''simple docstring'''
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  []

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

			_UpperCAmelCase									=  _UpperCAmelCase									=  _UpperCAmelCase									=  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       )

			_UpperCAmelCase									=  100.0 * em / total
			_UpperCAmelCase									=  100.0 * fa / total

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





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







			'''simple docstring'''
			_UpperCAmelCase									=  args.k
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]
			_UpperCAmelCase									=  [line.strip() for line in open(_UpperCAmelCase				,  'r'       ).readlines()]

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

			_UpperCAmelCase									=  100.0 * em / total
			logger.info(F"Precision@{k}: {em: .2f}"       )





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Union[str, Any]       )							->					List[Any]:







			'''simple docstring'''
			def strip_title(_UpperCAmelCase							:  List[Any]       ):
						if title.startswith('"'       ):
									_UpperCAmelCase									=  title[1:]
						if title.endswith('"'       ):
									_UpperCAmelCase									=  title[:-1]
						return title

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

			_UpperCAmelCase									=  rag_model.rag.question_encoder(_UpperCAmelCase       )
			_UpperCAmelCase									=  question_enc_outputs[0]

			_UpperCAmelCase									=  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'				,  )
			_UpperCAmelCase									=  rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
			_UpperCAmelCase									=  []
			for docs in all_docs:
						_UpperCAmelCase									=  [strip_title(_UpperCAmelCase       ) for title in docs['title']]
						provenance_strings.append('\t'.join(_UpperCAmelCase       )       )
			return provenance_strings





def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  List[str]				,  _UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''simple docstring'''
			with torch.no_grad():
						_UpperCAmelCase									=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    _UpperCAmelCase				,  return_tensors='pt'				,  padding=_UpperCAmelCase				,  truncation=_UpperCAmelCase       )

						_UpperCAmelCase									=  inputs_dict.input_ids.to(args.device       )
						_UpperCAmelCase									=  inputs_dict.attention_mask.to(args.device       )
						_UpperCAmelCase									=  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]]				,  )
						_UpperCAmelCase									=  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 				A      (	)							->					Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  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.'				,  )
			_UpperCAmelCase									=  parser.parse_args()
			_UpperCAmelCase									=  torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
			return args





def 				A      (	_UpperCAmelCase							:  Any       )							->					int:







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

			_UpperCAmelCase									=  (
			    [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       )

			_UpperCAmelCase									=  get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			_UpperCAmelCase									=  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'       ):
									_UpperCAmelCase									=  RagRetriever.from_pretrained(_UpperCAmelCase				,  **_UpperCAmelCase       )
									_UpperCAmelCase									=  model_class.from_pretrained(_UpperCAmelCase				,  retriever=_UpperCAmelCase				,  **_UpperCAmelCase       )
									model.retriever.init_retrieval()
						else:
									_UpperCAmelCase									=  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:
									_UpperCAmelCase									=  []
									for line in tqdm(_UpperCAmelCase       ):
												questions.append(line.strip()       )
												if len(_UpperCAmelCase       ) == args.eval_batch_size:
															_UpperCAmelCase									=  evaluate_batch_fn(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
															preds_file.write('\n'.join(_UpperCAmelCase       ) + '\n'       )
															preds_file.flush()
															_UpperCAmelCase									=  []
									if len(_UpperCAmelCase       ) > 0:
												_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			get_args()
							main(args)
