






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__UpperCamelCase   :   Dict      =    logging.get_logger(__name__)

# General docstring
__UpperCamelCase   :   Any      =    """ResNetConfig"""

# Base docstring
__UpperCamelCase   :   Union[str, Any]      =    """microsoft/resnet-50"""
__UpperCamelCase   :   List[Any]      =    [1, 2048, 7, 7]

# Image classification docstring
__UpperCamelCase   :   str      =    """microsoft/resnet-50"""
__UpperCamelCase   :   List[str]      =    """tiger cat"""

__UpperCamelCase   :   Union[str, Any]      =    [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int = 3							,					_lowerCAmelCase   :				int = 1							,					_lowerCAmelCase   :				str = "relu"      )		->			str:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	nn.Convad(
		    _lowerCAmelCase							,					_lowerCAmelCase							,					kernel_size=_lowerCAmelCase							,					stride=_lowerCAmelCase							,					padding=kernel_size // 2							,					bias=_lowerCAmelCase      )
		__lowercase   							=	nn.BatchNormad(_lowerCAmelCase      )
		__lowercase   							=	ACTaFN[activation] if activation is not None else nn.Identity()







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Tensor      )		->			Tensor:
		"""simple docstring"""







		__lowercase   							=	self.convolution(_lowerCAmelCase      )
		__lowercase   							=	self.normalization(_lowerCAmelCase      )
		__lowercase   							=	self.activation(_lowerCAmelCase      )
		return hidden_state


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				ResNetConfig      )		->			str:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	ResNetConvLayer(
		    config.num_channels							,					config.embedding_size							,					kernel_size=7							,					stride=2							,					activation=config.hidden_act      )
		__lowercase   							=	nn.MaxPoolad(kernel_size=3							,					stride=2							,					padding=1      )
		__lowercase   							=	config.num_channels







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				Tensor      )		->			Tensor:
		"""simple docstring"""







		__lowercase   							=	pixel_values.shape[1]
		if num_channels != self.num_channels:
			raise ValueError(
			    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""      )
		__lowercase   							=	self.embedder(_lowerCAmelCase      )
		__lowercase   							=	self.pooler(_lowerCAmelCase      )
		return embedding


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				Tuple							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int = 2      )		->			Tuple:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	nn.Convad(_lowerCAmelCase							,					_lowerCAmelCase							,					kernel_size=1							,					stride=_lowerCAmelCase							,					bias=_lowerCAmelCase      )
		__lowercase   							=	nn.BatchNormad(_lowerCAmelCase      )







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Tensor      )		->			Tensor:
		"""simple docstring"""







		__lowercase   							=	self.convolution(_lowerCAmelCase      )
		__lowercase   							=	self.normalization(_lowerCAmelCase      )
		return hidden_state


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				str							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int = 1							,					_lowerCAmelCase   :				str = "relu"      )		->			Optional[int]:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	in_channels != out_channels or stride != 1
		__lowercase   							=	(
		    ResNetShortCut(_lowerCAmelCase							,					_lowerCAmelCase							,					stride=_lowerCAmelCase      ) if should_apply_shortcut else nn.Identity()
		)
		__lowercase   							=	nn.Sequential(
		    ResNetConvLayer(_lowerCAmelCase							,					_lowerCAmelCase							,					stride=_lowerCAmelCase      )							,					ResNetConvLayer(_lowerCAmelCase							,					_lowerCAmelCase							,					activation=_lowerCAmelCase      )							,					)
		__lowercase   							=	ACTaFN[activation]







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Dict      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	hidden_state
		__lowercase   							=	self.layer(_lowerCAmelCase      )
		__lowercase   							=	self.shortcut(_lowerCAmelCase      )
		hidden_state += residual
		__lowercase   							=	self.activation(_lowerCAmelCase      )
		return hidden_state


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int = 1							,					_lowerCAmelCase   :				str = "relu"							,					_lowerCAmelCase   :				int = 4      )		->			Any:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	in_channels != out_channels or stride != 1
		__lowercase   							=	out_channels // reduction
		__lowercase   							=	(
		    ResNetShortCut(_lowerCAmelCase							,					_lowerCAmelCase							,					stride=_lowerCAmelCase      ) if should_apply_shortcut else nn.Identity()
		)
		__lowercase   							=	nn.Sequential(
		    ResNetConvLayer(_lowerCAmelCase							,					_lowerCAmelCase							,					kernel_size=1      )							,					ResNetConvLayer(_lowerCAmelCase							,					_lowerCAmelCase							,					stride=_lowerCAmelCase      )							,					ResNetConvLayer(_lowerCAmelCase							,					_lowerCAmelCase							,					kernel_size=1							,					activation=_lowerCAmelCase      )							,					)
		__lowercase   							=	ACTaFN[activation]







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				List[str]      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	hidden_state
		__lowercase   							=	self.layer(_lowerCAmelCase      )
		__lowercase   							=	self.shortcut(_lowerCAmelCase      )
		hidden_state += residual
		__lowercase   							=	self.activation(_lowerCAmelCase      )
		return hidden_state


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				Optional[int]							,					_lowerCAmelCase   :				ResNetConfig							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int = 2							,					_lowerCAmelCase   :				int = 2							,					)		->			List[str]:
		"""simple docstring"""







		super().__init__()

		__lowercase   							=	ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

		__lowercase   							=	nn.Sequential(
		    # downsampling is done in the first layer with stride of 2
		    layer(_lowerCAmelCase							,					_lowerCAmelCase							,					stride=_lowerCAmelCase							,					activation=config.hidden_act      )							,					*[layer(_lowerCAmelCase							,					_lowerCAmelCase							,					activation=config.hidden_act      ) for _ in range(depth - 1      )]							,					)







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				Tensor      )		->			Tensor:
		"""simple docstring"""







		__lowercase   							=	input
		for layer in self.layers:
			__lowercase   							=	layer(_lowerCAmelCase      )
		return hidden_state


class 							__UpperCamelCase     (  nn.Module ):







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				ResNetConfig      )		->			List[str]:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	nn.ModuleList([]      )
		# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
		self.stages.append(
		    ResNetStage(
		        _lowerCAmelCase							,					config.embedding_size							,					config.hidden_sizes[0]							,					stride=2 if config.downsample_in_first_stage else 1							,					depth=config.depths[0]							,					)      )
		__lowercase   							=	zip(config.hidden_sizes							,					config.hidden_sizes[1:]      )
		for (in_channels, out_channels), depth in zip(_lowerCAmelCase							,					config.depths[1:]      ):
			self.stages.append(ResNetStage(_lowerCAmelCase							,					_lowerCAmelCase							,					_lowerCAmelCase							,					depth=_lowerCAmelCase      )      )







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				Tensor							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = True      )		->			BaseModelOutputWithNoAttention:
		"""simple docstring"""







		__lowercase   							=	() if output_hidden_states else None

		for stage_module in self.stages:
			if output_hidden_states:
				__lowercase   							=	hidden_states + (hidden_state,)

			__lowercase   							=	stage_module(_lowerCAmelCase      )

		if output_hidden_states:
			__lowercase   							=	hidden_states + (hidden_state,)

		if not return_dict:
			return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

		return BaseModelOutputWithNoAttention(
		    last_hidden_state=_lowerCAmelCase							,					hidden_states=_lowerCAmelCase							,					)


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Optional[Any] =							ResNetConfig
	__snake_case						:str =							'resnet'
	__snake_case						:Dict =							'pixel_values'
	__snake_case						:Any =							True







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Any      )		->			Optional[int]:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase							,					nn.Convad      ):
			nn.init.kaiming_normal_(module.weight							,					mode="""fan_out"""							,					nonlinearity="""relu"""      )
		elif isinstance(_lowerCAmelCase							,					(nn.BatchNormad, nn.GroupNorm)      ):
			nn.init.constant_(module.weight							,					1      )
			nn.init.constant_(module.bias							,					0      )







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Optional[int]=False      )		->			int:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ):
			__lowercase   							=	value

__UpperCamelCase   :   str      =    r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__UpperCamelCase   :   List[str]      =    r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.' ,      _lowerCAmelCase ,      )
class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			str:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase      )
		__lowercase   							=	config
		__lowercase   							=	ResNetEmbeddings(_lowerCAmelCase      )
		__lowercase   							=	ResNetEncoder(_lowerCAmelCase      )
		__lowercase   							=	nn.AdaptiveAvgPoolad((1, 1)      )
		# Initialize weights and apply final processing
		self.post_init()







	@add_start_docstrings_to_model_forward(_lowerCAmelCase      )
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC							,					output_type=_lowerCAmelCase							,					config_class=_CONFIG_FOR_DOC							,					modality="""vision"""							,					expected_output=_EXPECTED_OUTPUT_SHAPE							,					)
	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Tensor							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				Optional[bool] = None      )		->			BaseModelOutputWithPoolingAndNoAttention:
		"""simple docstring"""







		__lowercase   							=	(
		    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)
		__lowercase   							=	return_dict if return_dict is not None else self.config.use_return_dict

		__lowercase   							=	self.embedder(_lowerCAmelCase      )

		__lowercase   							=	self.encoder(
		    _lowerCAmelCase							,					output_hidden_states=_lowerCAmelCase							,					return_dict=_lowerCAmelCase      )

		__lowercase   							=	encoder_outputs[0]

		__lowercase   							=	self.pooler(_lowerCAmelCase      )

		if not return_dict:
			return (last_hidden_state, pooled_output) + encoder_outputs[1:]

		return BaseModelOutputWithPoolingAndNoAttention(
		    last_hidden_state=_lowerCAmelCase							,					pooler_output=_lowerCAmelCase							,					hidden_states=encoder_outputs.hidden_states							,					)


@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ' ,      _lowerCAmelCase ,      )
class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				Dict							,					_lowerCAmelCase   :				int      )		->			Optional[int]:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase      )
		__lowercase   							=	config.num_labels
		__lowercase   							=	ResNetModel(_lowerCAmelCase      )
		# classification head
		__lowercase   							=	nn.Sequential(
		    nn.Flatten()							,					nn.Linear(config.hidden_sizes[-1]							,					config.num_labels      ) if config.num_labels > 0 else nn.Identity()							,					)
		# initialize weights and apply final processing
		self.post_init()







	@add_start_docstrings_to_model_forward(_lowerCAmelCase      )
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT							,					output_type=_lowerCAmelCase							,					config_class=_CONFIG_FOR_DOC							,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,					)
	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Optional[torch.FloatTensor] = None							,					_lowerCAmelCase   :				Optional[torch.LongTensor] = None							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				Optional[bool] = None							,					)		->			ImageClassifierOutputWithNoAttention:
		"""simple docstring"""







		__lowercase   							=	return_dict if return_dict is not None else self.config.use_return_dict

		__lowercase   							=	self.resnet(_lowerCAmelCase							,					output_hidden_states=_lowerCAmelCase							,					return_dict=_lowerCAmelCase      )

		__lowercase   							=	outputs.pooler_output if return_dict else outputs[1]

		__lowercase   							=	self.classifier(_lowerCAmelCase      )

		__lowercase   							=	None

		if labels is not None:
			if self.config.problem_type is None:
				if self.num_labels == 1:
					__lowercase   							=	"""regression"""
				elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
					__lowercase   							=	"""single_label_classification"""
				else:
					__lowercase   							=	"""multi_label_classification"""
			if self.config.problem_type == "regression":
				__lowercase   							=	MSELoss()
				if self.num_labels == 1:
					__lowercase   							=	loss_fct(logits.squeeze()							,					labels.squeeze()      )
				else:
					__lowercase   							=	loss_fct(_lowerCAmelCase							,					_lowerCAmelCase      )
			elif self.config.problem_type == "single_label_classification":
				__lowercase   							=	CrossEntropyLoss()
				__lowercase   							=	loss_fct(logits.view(-1							,					self.num_labels      )							,					labels.view(-1      )      )
			elif self.config.problem_type == "multi_label_classification":
				__lowercase   							=	BCEWithLogitsLoss()
				__lowercase   							=	loss_fct(_lowerCAmelCase							,					_lowerCAmelCase      )

		if not return_dict:
			__lowercase   							=	(logits,) + outputs[2:]
			return (loss,) + output if loss is not None else output

		return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase							,					logits=_lowerCAmelCase							,					hidden_states=outputs.hidden_states      )


@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    ' ,      _lowerCAmelCase ,      )
class 							__UpperCamelCase     (  _lowerCAmelCase ,      _lowerCAmelCase ):







	def __init__(	self   :				Tuple							,					_lowerCAmelCase   :				Tuple      )		->			str:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase      )
		super()._init_backbone(_lowerCAmelCase      )

		__lowercase   							=	[config.embedding_size] + config.hidden_sizes
		__lowercase   							=	ResNetEmbeddings(_lowerCAmelCase      )
		__lowercase   							=	ResNetEncoder(_lowerCAmelCase      )

		# initialize weights and apply final processing
		self.post_init()







	@add_start_docstrings_to_model_forward(_lowerCAmelCase      )
	@replace_return_docstrings(output_type=_lowerCAmelCase							,					config_class=_CONFIG_FOR_DOC      )
	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Tensor							,					_lowerCAmelCase   :				Optional[bool] = None							,					_lowerCAmelCase   :				Optional[bool] = None      )		->			BackboneOutput:
		"""simple docstring"""







		__lowercase   							=	return_dict if return_dict is not None else self.config.use_return_dict
		__lowercase   							=	(
		    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)

		__lowercase   							=	self.embedder(_lowerCAmelCase      )

		__lowercase   							=	self.encoder(_lowerCAmelCase							,					output_hidden_states=_lowerCAmelCase							,					return_dict=_lowerCAmelCase      )

		__lowercase   							=	outputs.hidden_states

		__lowercase   							=	()
		for idx, stage in enumerate(self.stage_names      ):
			if stage in self.out_features:
				feature_maps += (hidden_states[idx],)

		if not return_dict:
			__lowercase   							=	(feature_maps,)
			if output_hidden_states:
				output += (outputs.hidden_states,)
			return output

		return BackboneOutput(
		    feature_maps=_lowerCAmelCase							,					hidden_states=outputs.hidden_states if output_hidden_states else None							,					attentions=_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


A__							:  Union[str, Any]    =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      return max(metric_fn(lowerCAmelCase				,      lowerCAmelCase    ) for gt in ground_truths    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[line.strip() for line in open(lowerCAmelCase				,      """r"""    ).readlines()]
      _lowerCAmelCase					=				[]

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

      _lowerCAmelCase					=				_lowerCAmelCase					=				_lowerCAmelCase					=				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    )

      _lowerCAmelCase					=				100.0 * em / total
      _lowerCAmelCase					=				100.0 * fa / total

      logger.info(f"F1: {fa:.2f}"    )
      logger.info(f"EM: {em:.2f}"    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				args.k
      _lowerCAmelCase					=				[line.strip() for line in open(lowerCAmelCase				,      """r"""    ).readlines()]
      _lowerCAmelCase					=				[line.strip() for line in open(lowerCAmelCase				,      """r"""    ).readlines()]

      _lowerCAmelCase					=				_lowerCAmelCase					=				0
      for hypo, reference in zip(lowerCAmelCase				,      lowerCAmelCase    ):
            _lowerCAmelCase					=				set(hypo.split("""\t"""    )[:k]    )
            _lowerCAmelCase					=				set(reference.split("""\t"""    )    )
            total += 1
            em += len(hypo_provenance & ref_provenance    ) / k

      _lowerCAmelCase					=				100.0 * em / total
      logger.info(f"Precision@{k}: {em: .2f}"    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      def strip_title(lowerCAmelCase    ):
            if title.startswith("""\""""    ):
                  _lowerCAmelCase					=				title[1:]
            if title.endswith("""\""""    ):
                  _lowerCAmelCase					=				title[:-1]
            return title

      _lowerCAmelCase					=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          lowerCAmelCase				,      return_tensors="""pt"""				,      padding=lowerCAmelCase				,      truncation=lowerCAmelCase				,      )["""input_ids"""].to(args.device    )

      _lowerCAmelCase					=				rag_model.rag.question_encoder(lowerCAmelCase    )
      _lowerCAmelCase					=				question_enc_outputs[0]

      _lowerCAmelCase					=				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"""				,      )
      _lowerCAmelCase					=				rag_model.retriever.index.get_doc_dicts(result.doc_ids    )
      _lowerCAmelCase					=				[]
      for docs in all_docs:
            _lowerCAmelCase					=				[strip_title(lowerCAmelCase    ) for title in docs["""title"""]]
            provenance_strings.append("""\t""".join(lowerCAmelCase    )    )
      return provenance_strings
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      with torch.no_grad():
            _lowerCAmelCase					=				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                lowerCAmelCase				,      return_tensors="""pt"""				,      padding=lowerCAmelCase				,      truncation=lowerCAmelCase    )

            _lowerCAmelCase					=				inputs_dict.input_ids.to(args.device    )
            _lowerCAmelCase					=				inputs_dict.attention_mask.to(args.device    )
            _lowerCAmelCase					=				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]]				,      )
            _lowerCAmelCase					=				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   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				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."""				,      )
      _lowerCAmelCase					=				parser.parse_args()
      _lowerCAmelCase					=				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
      return args
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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

      _lowerCAmelCase					=				(
          [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    )

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






