


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			__lowerCAmelCase   (       snake_case_       ,     snake_case_       ,     snake_case_       ,     unittest.TestCase    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				StableDiffusionInstructPixaPixPipeline
  _SCREAMING_SNAKE_CASE									=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  _SCREAMING_SNAKE_CASE									=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE									=				IMAGE_TO_IMAGE_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE									=				IMAGE_TO_IMAGE_IMAGE_PARAMS



  def     lowerCAmelCase__     (   self				:				Tuple     )      ->       List[str]:




     """simple docstring"""
     torch.manual_seed(0     )
     snake_case_    			=    UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)			, layers_per_block=2			, sample_size=3_2			, in_channels=8			, out_channels=4			, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			, cross_attention_dim=3_2			, )
     snake_case_    			=    PNDMScheduler(skip_prk_steps=_lowerCAmelCase     )
     torch.manual_seed(0     )
     snake_case_    			=    AutoencoderKL(
         block_out_channels=[3_2, 6_4]			, in_channels=3			, out_channels=3			, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			, latent_channels=4			, )
     torch.manual_seed(0     )
     snake_case_    			=    CLIPTextConfig(
         bos_token_id=0			, eos_token_id=2			, hidden_size=3_2			, intermediate_size=3_7			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1_0_0_0			, )
     snake_case_    			=    CLIPTextModel(_lowerCAmelCase     )
     snake_case_    			=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

     snake_case_    			=    {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components



  def     lowerCAmelCase__     (   self				:				Any			, _lowerCAmelCase				:				List[Any]			, _lowerCAmelCase				:				List[str]=0     )      ->       List[str]:




     """simple docstring"""
     snake_case_    			=    floats_tensor((1, 3, 3_2, 3_2)			, rng=random.Random(_lowerCAmelCase     )     ).to(_lowerCAmelCase     )
     snake_case_    			=    image.cpu().permute(0			, 2			, 3			, 1     )[0]
     snake_case_    			=    Image.fromarray(np.uinta(_lowerCAmelCase     )     ).convert("RGB"     )
     if str(_lowerCAmelCase     ).startswith("mps"     ):
        snake_case_    			=    torch.manual_seed(_lowerCAmelCase     )
     else:
        snake_case_    			=    torch.Generator(device=_lowerCAmelCase     ).manual_seed(_lowerCAmelCase     )
     snake_case_    			=    {
         'prompt': 'A painting of a squirrel eating a burger',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
         'image_guidance_scale': 1,
         'output_type': 'numpy',
     }
     return inputs



  def     lowerCAmelCase__     (   self				:				Dict     )      ->       str:




     """simple docstring"""
     snake_case_    			=    'cpu'  # ensure determinism for the device-dependent torch.Generator
     snake_case_    			=    self.get_dummy_components()
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase     )
     snake_case_    			=    sd_pipe.to(_lowerCAmelCase     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

     snake_case_    			=    self.get_dummy_inputs(_lowerCAmelCase     )
     snake_case_    			=    sd_pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[0, -3:, -3:, -1]
     assert image.shape == (1, 3_2, 3_2, 3)
     snake_case_    			=    np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



  def     lowerCAmelCase__     (   self				:				str     )      ->       int:




     """simple docstring"""
     snake_case_    			=    'cpu'  # ensure determinism for the device-dependent torch.Generator
     snake_case_    			=    self.get_dummy_components()
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase     )
     snake_case_    			=    sd_pipe.to(_lowerCAmelCase     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

     snake_case_    			=    self.get_dummy_inputs(_lowerCAmelCase     )
     snake_case_    			=    'french fries'
     snake_case_    			=    sd_pipe(**_lowerCAmelCase			, negative_prompt=_lowerCAmelCase     )
     snake_case_    			=    output.images
     snake_case_    			=    image[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     snake_case_    			=    np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



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




     """simple docstring"""
     snake_case_    			=    'cpu'  # ensure determinism for the device-dependent torch.Generator
     snake_case_    			=    self.get_dummy_components()
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase     )
     snake_case_    			=    sd_pipe.to(_lowerCAmelCase     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

     snake_case_    			=    self.get_dummy_inputs(_lowerCAmelCase     )
     snake_case_    			=    [inputs['prompt']] * 2

     snake_case_    			=    np.array(inputs["image"]     ).astype(np.floataa     ) / 255.0
     snake_case_    			=    torch.from_numpy(_lowerCAmelCase     ).unsqueeze(0     ).to(_lowerCAmelCase     )
     snake_case_    			=    image / 2 + 0.5
     snake_case_    			=    image.permute(0			, 3			, 1			, 2     )
     snake_case_    			=    image.repeat(2			, 1			, 1			, 1     )

     snake_case_    			=    sd_pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[-1, -3:, -3:, -1]

     assert image.shape == (2, 3_2, 3_2, 3)
     snake_case_    			=    np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



  def     lowerCAmelCase__     (   self				:				str     )      ->       int:




     """simple docstring"""
     snake_case_    			=    'cpu'  # ensure determinism for the device-dependent torch.Generator
     snake_case_    			=    self.get_dummy_components()
     snake_case_    			=    EulerAncestralDiscreteScheduler(
         beta_start=0.00_085			, beta_end=0.012			, beta_schedule="scaled_linear"     )
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase     )
     snake_case_    			=    sd_pipe.to(_lowerCAmelCase     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

     snake_case_    			=    self.get_dummy_inputs(_lowerCAmelCase     )
     snake_case_    			=    sd_pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[0, -3:, -3:, -1]

     snake_case_    			=    [round(_lowerCAmelCase			, 4     ) for x in image_slice.flatten().tolist()]
     print(",".join([str(_lowerCAmelCase     ) for x in slice]     )     )

     assert image.shape == (1, 3_2, 3_2, 3)
     snake_case_    			=    np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



  def     lowerCAmelCase__     (   self				:				Tuple     )      ->       List[Any]:




     """simple docstring"""
     super().test_inference_batch_single_identical(expected_max_diff=3e-3     )



  def     lowerCAmelCase__     (   self				:				Any     )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    self.get_dummy_components()
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase     )
     snake_case_    			=    VaeImageProcessor(do_resize=_lowerCAmelCase			, do_normalize=_lowerCAmelCase     )
     snake_case_    			=    pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )

     snake_case_    			=    pipe(**self.get_dummy_inputs_by_type(_lowerCAmelCase			, input_image_type="pt"     )     )[0]

     snake_case_    			=    components['vae']
     snake_case_    			=    self.get_dummy_inputs_by_type(_lowerCAmelCase			, input_image_type="pt"     )

     for image_param in self.image_latents_params:
        if image_param in inputs.keys():
           snake_case_    			=    vae.encode(inputs[image_param]     ).latent_dist.mode()

     snake_case_    			=    pipe(**_lowerCAmelCase     )[0]

     snake_case_    			=    np.abs(out - out_latents_inputs     ).max()
     self.assertLess(_lowerCAmelCase			, 1e-4			, "passing latents as image input generate different result from passing image"     )






@slow
@require_torch_gpu
class 			__lowerCAmelCase   (       unittest.TestCase    ):






  """simple docstring"""





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




     """simple docstring"""
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  def     lowerCAmelCase__     (   self				:				Optional[int]			, _lowerCAmelCase				:				Tuple=0     )      ->       List[Any]:




     """simple docstring"""
     snake_case_    			=    torch.manual_seed(_lowerCAmelCase     )
     snake_case_    			=    load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"     )
     snake_case_    			=    {
         'prompt': 'turn him into a cyborg',
         'image': image,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 7.5,
         'image_guidance_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs



  def     lowerCAmelCase__     (   self				:				Dict     )      ->       int:




     """simple docstring"""
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"			, safety_checker=_lowerCAmelCase     )
     pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing()

     snake_case_    			=    self.get_inputs()
     snake_case_    			=    pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     snake_case_    			=    np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3



  def     lowerCAmelCase__     (   self				:				List[Any]     )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"			, safety_checker=_lowerCAmelCase     )
     snake_case_    			=    LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing()

     snake_case_    			=    self.get_inputs()
     snake_case_    			=    pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     snake_case_    			=    np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3



  def     lowerCAmelCase__     (   self				:				int     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"			, safety_checker=_lowerCAmelCase     )
     snake_case_    			=    DDIMScheduler.from_config(pipe.scheduler.config     )
     pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing()

     snake_case_    			=    self.get_inputs()
     snake_case_    			=    pipe(**_lowerCAmelCase     ).images
     snake_case_    			=    image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     snake_case_    			=    np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3



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




     """simple docstring"""
     snake_case_    			=    0

     def callback_fn(_lowerCAmelCase				:				int			, _lowerCAmelCase				:				int			, _lowerCAmelCase				:				torch.FloatTensor     ) -> None:
        snake_case_    			=    True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           snake_case_    			=    latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           snake_case_    			=    latents[0, -3:, -3:, -1]
           snake_case_    			=    np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
        elif step == 2:
           snake_case_    			=    latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           snake_case_    			=    latents[0, -3:, -3:, -1]
           snake_case_    			=    np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2

     snake_case_    			=    False

     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"			, safety_checker=_lowerCAmelCase			, torch_dtype=torch.floataa     )
     snake_case_    			=    pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing()

     snake_case_    			=    self.get_inputs()
     pipe(**_lowerCAmelCase			, callback=_lowerCAmelCase			, callback_steps=1     )
     assert callback_fn.has_been_called
     assert number_of_steps == 3



  def     lowerCAmelCase__     (   self				:				Tuple     )      ->       str:




     """simple docstring"""
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"			, safety_checker=_lowerCAmelCase			, torch_dtype=torch.floataa     )
     snake_case_    			=    pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     snake_case_    			=    self.get_inputs()
     snake_case_    			=    pipe(**_lowerCAmelCase     )

     snake_case_    			=    torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 1_0**9



  def     lowerCAmelCase__     (   self				:				Tuple     )      ->       int:




     """simple docstring"""
     snake_case_    			=    self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     snake_case_    			=    inputs['image'].resize((5_0_4, 5_0_4)     )

     snake_case_    			=    'timbrooks/instruct-pix2pix'
     snake_case_    			=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
         _lowerCAmelCase			, safety_checker=_lowerCAmelCase			, )
     pipe.to(_lowerCAmelCase     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
     pipe.enable_attention_slicing()

     snake_case_    			=    pipe(**_lowerCAmelCase     )
     snake_case_    			=    output.images[0]

     snake_case_    			=    image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert image.shape == (5_0_4, 5_0_4, 3)
     snake_case_    			=    np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3





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


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

transformers_logging.set_verbosity_info()





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 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    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     str	):
 return max(metric_fn(lowerCamelCase  ,  lowerCamelCase	) for gt in ground_truths	)





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						Tuple =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]
 UpperCamelCase_					:						List[Any] =					[]

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

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

 UpperCamelCase_					:						Union[str, Any] =					1_0_0.0 * em / total
 UpperCamelCase_					:						List[Any] =					1_0_0.0 * fa / total

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





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						Optional[int] =					args.k
 UpperCamelCase_					:						List[Any] =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]
 UpperCamelCase_					:						List[str] =					[line.strip() for line in open(lowerCamelCase  ,  'r'	).readlines()]

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

 UpperCamelCase_					:						Union[str, Any] =					1_0_0.0 * em / total
 logger.info(F"Precision@{k}: {em: .2f}"	)





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     Any	):
 def strip_title(lowerCamelCase					:     List[str]	):
  if title.startswith('"'	):
   UpperCamelCase_					:						List[str] =					title[1:]
  if title.endswith('"'	):
   UpperCamelCase_					:						int =					title[:-1]
  return title

 UpperCamelCase_					:						Optional[int] =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
     lowerCamelCase  ,  return_tensors='pt'  ,  padding=lowerCamelCase  ,  truncation=lowerCamelCase  ,  )['input_ids'].to(args.device	)

 UpperCamelCase_					:						int =					rag_model.rag.question_encoder(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					question_enc_outputs[0]

 UpperCamelCase_					:						Tuple =					rag_model.retriever(
     lowerCamelCase  ,  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_					:						str =					rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
 UpperCamelCase_					:						int =					[]
 for docs in all_docs:
  UpperCamelCase_					:						Union[str, Any] =					[strip_title(lowerCamelCase	) for title in docs['title']]
  provenance_strings.append('\t'.join(lowerCamelCase	)	)
 return provenance_strings





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     List[Any]	):
 with torch.no_grad():
  UpperCamelCase_					:						List[Any] =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      lowerCamelCase  ,  return_tensors='pt'  ,  padding=lowerCamelCase  ,  truncation=lowerCamelCase	)

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

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

  return answers





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





def    __lowercase     (						lowerCamelCase					:     int	):
 UpperCamelCase_					:						Any =					{}
 if args.model_type is None:
  UpperCamelCase_					:						List[Any] =					infer_model_type(args.model_name_or_path	)
  assert args.model_type is not None
 if args.model_type.startswith('rag'	):
  UpperCamelCase_					:						Optional[int] =					RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
  UpperCamelCase_					:						Dict =					args.n_docs
  if args.index_name is not None:
   UpperCamelCase_					:						Union[str, Any] =					args.index_name
  if args.index_path is not None:
   UpperCamelCase_					:						str =					args.index_path
 else:
  UpperCamelCase_					:						Tuple =					BartForConditionalGeneration

 UpperCamelCase_					:						Optional[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'  ,  lowerCamelCase	)

 UpperCamelCase_					:						Optional[int] =					get_scores if args.eval_mode == 'e2e' else get_precision_at_k
 UpperCamelCase_					:						Optional[int] =					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(lowerCamelCase  ,  args.predictions_path  ,  args.gold_data_path	)
   continue

  logger.info('***** Running evaluation for {} *****'.format(lowerCamelCase	)	)
  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_					:						List[str] =					RagRetriever.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
   UpperCamelCase_					:						List[Any] =					model_class.from_pretrained(lowerCamelCase  ,  retriever=lowerCamelCase  ,  **lowerCamelCase	)
   model.retriever.init_retrieval()
  else:
   UpperCamelCase_					:						Optional[Any] =					model_class.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
  model.to(args.device	)

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

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


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



