
"""simple docstring"""






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase__ :       Optional[int]						     =			"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def __A						(   a_ :							Any		,		a_ :							Optional[Any]		,		a_ :							List[str]=8     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		SCREAMING_SNAKE_CASE       :     Any				= width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :UNetaDConditionModel		, lowerCamelCase_    :DDPMScheduler		, lowerCamelCase_    :VQModel		, )							->			str:







			'''simple docstring'''




			super().__init__()

			self.register_modules(
			    unet=lowerCamelCase_		, scheduler=lowerCamelCase_		, movq=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Dict				= 2 ** (len(self.movq.config.block_out_channels					) - 1)





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







			'''simple docstring'''




			if latents is None:
					SCREAMING_SNAKE_CASE       :     List[Any]				= randn_tensor(lowerCamelCase_		, generator=lowerCamelCase_		, device=lowerCamelCase_		, dtype=lowerCamelCase_					)
			else:
					if latents.shape != shape:
							raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"					)
					SCREAMING_SNAKE_CASE       :     int				= latents.to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= latents * scheduler.init_noise_sigma
			return latents





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Any=0					)							->			List[Any]:







			'''simple docstring'''




			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

			SCREAMING_SNAKE_CASE       :     List[str]				= torch.device(f"cuda:{gpu_id}"					)

			SCREAMING_SNAKE_CASE       :     int				= [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(lowerCamelCase_		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Union[str, Any]=0					)							->			Optional[int]:







			'''simple docstring'''




			if is_accelerate_available() and is_accelerate_version('''>='''		, '''0.17.0.dev0'''					):
					from accelerate import cpu_offload_with_hook
			else:
					raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.device(f"cuda:{gpu_id}"					)

			if self.device.type != "cpu":
					self.to('''cpu'''		, silence_dtype_warnings=lowerCamelCase_					)
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			SCREAMING_SNAKE_CASE       :     str				= None
			for cpu_offloaded_model in [self.unet, self.movq]:
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= cpu_offload_with_hook(lowerCamelCase_		, lowerCamelCase_		, prev_module_hook=lowerCamelCase_					)

			# We'll offload the last model manually.
			SCREAMING_SNAKE_CASE       :     int				= hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			if not hasattr(self.unet		, '''_hf_hook'''					):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(lowerCamelCase_		, '''_hf_hook'''					)
					    and hasattr(module._hf_hook		, '''execution_device'''					)
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device					)
			return self.device







	@torch.no_grad()
	@replace_example_docstring(lowerCamelCase_					)
	def __call__(						self    :str		, lowerCamelCase_    :Union[torch.FloatTensor, List[torch.FloatTensor]]		, lowerCamelCase_    :Union[torch.FloatTensor, List[torch.FloatTensor]]		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :int = 5_12		, lowerCamelCase_    :int = 5_12		, lowerCamelCase_    :int = 1_00		, lowerCamelCase_    :float = 4.0		, lowerCamelCase_    :int = 1		, lowerCamelCase_    :Optional[Union[torch.Generator, List[torch.Generator]]] = None		, lowerCamelCase_    :Optional[torch.FloatTensor] = None		, lowerCamelCase_    :Optional[str] = "pil"		, lowerCamelCase_    :bool = True		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self._execution_device

			SCREAMING_SNAKE_CASE       :     List[Any]				= guidance_scale > 1.0

			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     str				= torch.cat(lowerCamelCase_		, dim=0					)
			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.cat(lowerCamelCase_		, dim=0					)
			if isinstance(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.cat(lowerCamelCase_		, dim=0					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= image_embeds.shape[0] * num_images_per_prompt

			if do_classifier_free_guidance:
					SCREAMING_SNAKE_CASE       :     Tuple				= image_embeds.repeat_interleave(lowerCamelCase_		, dim=0					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= negative_image_embeds.repeat_interleave(lowerCamelCase_		, dim=0					)
					SCREAMING_SNAKE_CASE       :     Tuple				= hint.repeat_interleave(lowerCamelCase_		, dim=0					)

			SCREAMING_SNAKE_CASE       :     Any				= torch.cat([negative_image_embeds, image_embeds]		, dim=0					).to(dtype=self.unet.dtype		, device=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat([hint, hint]		, dim=0					).to(dtype=self.unet.dtype		, device=lowerCamelCase_					)
			self.scheduler.set_timesteps(lowerCamelCase_		, device=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= self.scheduler.timesteps

			SCREAMING_SNAKE_CASE       :     List[str]				= self.movq.config.latent_channels

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= downscale_height_and_width(lowerCamelCase_		, lowerCamelCase_		, self.movq_scale_factor					)

			# create initial latent
			SCREAMING_SNAKE_CASE       :     int				= self.prepare_latents(
			    (batch_size, num_channels_latents, height, width)		, image_embeds.dtype		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, self.scheduler		, )

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

					SCREAMING_SNAKE_CASE       :     Any				= {'''image_embeds''': image_embeds, '''hint''': hint}
					SCREAMING_SNAKE_CASE       :     Dict				= self.unet(
					    sample=lowerCamelCase_		, timestep=lowerCamelCase_		, encoder_hidden_states=lowerCamelCase_		, added_cond_kwargs=lowerCamelCase_		, return_dict=lowerCamelCase_		, )[0]

					if do_classifier_free_guidance:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= noise_pred.split(latents.shape[1]		, dim=1					)
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= noise_pred.chunk(2					)
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= variance_pred.chunk(2					)
							SCREAMING_SNAKE_CASE       :     Dict				= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							SCREAMING_SNAKE_CASE       :     str				= torch.cat([noise_pred, variance_pred_text]		, dim=1					)

					if not (
					    hasattr(self.scheduler.config		, '''variance_type'''					)
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= noise_pred.split(latents.shape[1]		, dim=1					)

					# compute the previous noisy sample x_t -> x_t-1
					SCREAMING_SNAKE_CASE       :     str				= self.scheduler.step(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_		, )[0]
			# post-processing
			SCREAMING_SNAKE_CASE       :     int				= self.movq.decode(lowerCamelCase_		, force_not_quantize=lowerCamelCase_					)['''sample''']

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"					)

			if output_type in ["np", "pil"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= image * 0.5 + 0.5
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= image.clamp(0		, 1					)
					SCREAMING_SNAKE_CASE       :     str				= image.cpu().permute(0		, 2		, 3		, 1					).float().numpy()

			if output_type == "pil":
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.numpy_to_pil(lowerCamelCase_					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=lowerCamelCase_					)







"""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__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''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						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






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





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

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

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

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





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

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

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





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

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

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

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





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

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

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

				return answers





def __A						(   )->							int:




		'''simple docstring'''






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





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= 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[str]				= 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       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     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'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= 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(a_		,		args.predictions_path		,		args.gold_data_path     )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				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       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

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

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


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





