


'''simple docstring'''


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=False							,   SCREAMING_SNAKE_CASE__=False							,   SCREAMING_SNAKE_CASE__="data2vec."  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   """backbone.""" if is_semantic else """"""

				_SCREAMING_SNAKE_CASE				: Any  	=   []
				for i in range(config.num_hidden_layers  ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")  )
								rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")  )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")  )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")  )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")  )
								rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")  )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")  )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")  )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")  )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")  )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
				        (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
				        (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
				    ]  )

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
								        (
								            """rel_pos_bias.relative_position_bias_table""",
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
								        ),
								        (
								            """rel_pos_bias.relative_position_index""",
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
								        ),
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								    ]  )
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
								        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
								        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
								        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
								    ]  )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
								        ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]  )

				return rename_keys







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=False							,   SCREAMING_SNAKE_CASE__=False							,   SCREAMING_SNAKE_CASE__="data2vec_vision."  ):







				"""simple docstring"""





				for i in range(config.num_hidden_layers  ):
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   """backbone.""" if is_semantic else """"""
								# queries, keys and values
								_SCREAMING_SNAKE_CASE				: int  	=   state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""  )
								_SCREAMING_SNAKE_CASE				: Tuple  	=   state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
								_SCREAMING_SNAKE_CASE				: Dict  	=   state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

								_SCREAMING_SNAKE_CASE				: Any  	=   in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE				: str  	=   q_bias
								_SCREAMING_SNAKE_CASE				: str  	=   in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE				: str  	=   in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""  )
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""  )

								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   gamma_a
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												_SCREAMING_SNAKE_CASE				: Any  	=   state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""  )

												_SCREAMING_SNAKE_CASE				: Tuple  	=   table
												_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   index







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Any  	=   argparse.ArgumentParser(
				    """Convert Data2VecVision to HF for image classification and pretraining"""							,   add_help=SCREAMING_SNAKE_CASE__  )
				parser.add_argument("""--hf_checkpoint_name"""							,   type=SCREAMING_SNAKE_CASE__  )
				parser.add_argument("""--input_size"""							,   default=224							,   type=SCREAMING_SNAKE_CASE__							,   help="""images input size"""  )
				parser.add_argument("""--beit_checkpoint"""							,   default=""""""							,   help="""beit checkpoint"""  )

				return parser.parse_args()







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				def load_state_dict(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=""							,   SCREAMING_SNAKE_CASE__="relative_position_index"  ):
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   []
								_SCREAMING_SNAKE_CASE				: int  	=   []
								_SCREAMING_SNAKE_CASE				: str  	=   []
								# copy state_dict so _load_from_state_dict can modify it
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   getattr(SCREAMING_SNAKE_CASE__							,   """_metadata"""							,   SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   state_dict.copy()
								if metadata is not None:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   metadata

								def load(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=""  ):
												_SCREAMING_SNAKE_CASE				: str  	=   {} if metadata is None else metadata.get(prefix[:-1]							,   {}  )
												module._load_from_state_dict(
												    SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												for name, child in module._modules.items():
																if child is not None:
																				load(SCREAMING_SNAKE_CASE__							,   prefix + name + """."""  )

								load(SCREAMING_SNAKE_CASE__							,   prefix=SCREAMING_SNAKE_CASE__  )

								_SCREAMING_SNAKE_CASE				: Dict  	=   []
								_SCREAMING_SNAKE_CASE				: Tuple  	=   []
								for key in missing_keys:
												_SCREAMING_SNAKE_CASE				: int  	=   True
												for ignore_key in ignore_missing.split("""|"""  ):
																if ignore_key in key:
																				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   False
																				break
												if keep_flag:
																warn_missing_keys.append(SCREAMING_SNAKE_CASE__  )
												else:
																ignore_missing_keys.append(SCREAMING_SNAKE_CASE__  )

								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   warn_missing_keys

								if len(SCREAMING_SNAKE_CASE__  ) > 0:
												print(
												    """Weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__							,   SCREAMING_SNAKE_CASE__  )  )
								if len(SCREAMING_SNAKE_CASE__  ) > 0:
												print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__							,   SCREAMING_SNAKE_CASE__  )  )
								if len(SCREAMING_SNAKE_CASE__  ) > 0:
												print(
												    """Ignored weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__							,   SCREAMING_SNAKE_CASE__  )  )
								if len(SCREAMING_SNAKE_CASE__  ) > 0:
												print("""\n""".join(SCREAMING_SNAKE_CASE__  )  )

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
				    """pretrained""": False,
				    """use_shared_rel_pos_bias""": True,
				    """use_abs_pos_emb""": False,
				    """init_values""": 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        """num_classes""": 1000,
								        """use_mean_pooling""": True,
								        """init_scale""": 0.0_0_1,
								        """use_rel_pos_bias""": True,
								    }  )

				_SCREAMING_SNAKE_CASE				: List[str]  	=   create_model(
				    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""							,   **SCREAMING_SNAKE_CASE__							,   )
				_SCREAMING_SNAKE_CASE				: str  	=   model.patch_embed.patch_size
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (args.input_size // patch_size[0], args.input_size // patch_size[1])
				_SCREAMING_SNAKE_CASE				: Dict  	=   torch.load(args.beit_checkpoint							,   map_location="""cpu"""  )

				print(f"""Load ckpt from {args.beit_checkpoint}"""  )
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   checkpoint[model_key]
												print(f"""Load state_dict by model_key = {model_key}"""  )
												break

				_SCREAMING_SNAKE_CASE				: Any  	=   list(checkpoint_model.keys()  )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(SCREAMING_SNAKE_CASE__  )

								if "relative_position_bias_table" in key:
												_SCREAMING_SNAKE_CASE				: int  	=   checkpoint_model[key]
												_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   rel_pos_bias.size()
												_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model.state_dict()[key].size()
												_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   prefix=""""""  )

				return model







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: int  	=   get_args()

				_SCREAMING_SNAKE_CASE				: Tuple  	=   """ft1k""" in args.hf_checkpoint_name
				_SCREAMING_SNAKE_CASE				: Tuple  	=   """large""" in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				_SCREAMING_SNAKE_CASE				: int  	=   DataaVecVisionConfig()
				if is_finetuned:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   True
								_SCREAMING_SNAKE_CASE				: Tuple  	=   False
								_SCREAMING_SNAKE_CASE				: Dict  	=   True
								_SCREAMING_SNAKE_CASE				: List[str]  	=   1000

								_SCREAMING_SNAKE_CASE				: Dict  	=   """huggingface/label-files"""
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   """imagenet-1k-id2label.json"""
								_SCREAMING_SNAKE_CASE				: List[str]  	=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   repo_type="""dataset"""  )							,   """r"""  )  )
								_SCREAMING_SNAKE_CASE				: Any  	=   {int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   idalabel
								_SCREAMING_SNAKE_CASE				: List[str]  	=   {v: k for k, v in idalabel.items()}
				else:
								_SCREAMING_SNAKE_CASE				: int  	=   False
								_SCREAMING_SNAKE_CASE				: int  	=   True
								_SCREAMING_SNAKE_CASE				: str  	=   False

				if is_large:
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   1024
								_SCREAMING_SNAKE_CASE				: Tuple  	=   4096
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   24
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   16

				# 2. Load Beit model
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   load_beit_model(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				orig_model.eval()

				# 3. Forward Beit model
				_SCREAMING_SNAKE_CASE				: int  	=   BeitImageProcessor(size=config.image_size							,   do_center_crop=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Any  	=   Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   image_processor(images=SCREAMING_SNAKE_CASE__							,   return_tensors="""pt"""  )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   encoding["""pixel_values"""]

				_SCREAMING_SNAKE_CASE				: Any  	=   (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE				: Tuple  	=   orig_model(*SCREAMING_SNAKE_CASE__  )

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE__  )
								hf_model.eval()
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   False
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   """data2vec_vision."""
				else:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   DataaVecVisionModel(SCREAMING_SNAKE_CASE__  )
								hf_model.eval()
								_SCREAMING_SNAKE_CASE				: int  	=   True
								_SCREAMING_SNAKE_CASE				: Tuple  	=   """"""

				_SCREAMING_SNAKE_CASE				: List[str]  	=   create_rename_keys(SCREAMING_SNAKE_CASE__							,   hf_prefix=SCREAMING_SNAKE_CASE__							,   has_lm_head=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   orig_model.state_dict()
				for src, dest in rename_keys:
								_SCREAMING_SNAKE_CASE				: int  	=   state_dict.pop(SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   val

				read_in_q_k_v(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   hf_prefix=SCREAMING_SNAKE_CASE__							,   has_lm_head=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   hf_model.load_state_dict(SCREAMING_SNAKE_CASE__							,   strict=SCREAMING_SNAKE_CASE__  )
				print("""HF missing"""							,   SCREAMING_SNAKE_CASE__  )
				print("""HF unexpected_keys"""							,   SCREAMING_SNAKE_CASE__  )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_model(SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: int  	=   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				_SCREAMING_SNAKE_CASE				: Dict  	=   torch.max(torch.abs(hf_output - orig_model_output  )  ).item()

				print(f"""max_absolute_diff = {max_absolute_diff}"""  )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   torch.allclose(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   atol=1E-3  )
				print("""Do both models output the same tensors?"""							,   """🔥""" if success else """💩"""  )
				if not success:
								raise Exception("""Something went wRoNg"""  )

				# 7. Save
				print(f"""Saving to {args.hf_checkpoint_name}"""  )
				hf_model.save_pretrained(args.hf_checkpoint_name  )
				image_processor.save_pretrained(args.hf_checkpoint_name  )


if __name__ == "__main__":
				main()
				# Run the following to convert checkpoints
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








'''simple docstring'''


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


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

transformers_logging.set_verbosity_info()







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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

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

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   0
				for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
								total += 1
								em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

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

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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

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

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				def strip_title(SCREAMING_SNAKE_CASE__  ):
								if title.startswith("""\""""  ):
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   title[1:]
								if title.endswith("""\""""  ):
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   title[:-1]
								return title

				_SCREAMING_SNAKE_CASE				: Dict  	=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    SCREAMING_SNAKE_CASE__							,   return_tensors="""pt"""							,   padding=SCREAMING_SNAKE_CASE__							,   truncation=SCREAMING_SNAKE_CASE__							,   )["""input_ids"""].to(args.device  )

				_SCREAMING_SNAKE_CASE				: List[str]  	=   rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   question_enc_outputs[0]

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   rag_model.retriever(
				    SCREAMING_SNAKE_CASE__							,   question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()							,   prefix=rag_model.rag.generator.config.prefix							,   n_docs=rag_model.config.n_docs							,   return_tensors="""pt"""							,   )
				_SCREAMING_SNAKE_CASE				: int  	=   rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				_SCREAMING_SNAKE_CASE				: int  	=   []
				for docs in all_docs:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   [strip_title(SCREAMING_SNAKE_CASE__  ) for title in docs["""title"""]]
								provenance_strings.append("""\t""".join(SCREAMING_SNAKE_CASE__  )  )
				return provenance_strings







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				with torch.no_grad():
								_SCREAMING_SNAKE_CASE				: Tuple  	=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    SCREAMING_SNAKE_CASE__							,   return_tensors="""pt"""							,   padding=SCREAMING_SNAKE_CASE__							,   truncation=SCREAMING_SNAKE_CASE__  )

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

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

								return answers







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   argparse.ArgumentParser()
				parser.add_argument(
				    """--model_type"""							,   choices=["""rag_sequence""", """rag_token""", """bart"""]							,   type=SCREAMING_SNAKE_CASE__							,   help=(
				        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
				        """ model_name_or_path"""
				    )							,   )
				parser.add_argument(
				    """--index_name"""							,   default=SCREAMING_SNAKE_CASE__							,   choices=["""exact""", """compressed""", """legacy"""]							,   type=SCREAMING_SNAKE_CASE__							,   help="""RAG model retriever type"""							,   )
				parser.add_argument(
				    """--index_path"""							,   default=SCREAMING_SNAKE_CASE__							,   type=SCREAMING_SNAKE_CASE__							,   help="""Path to the retrieval index"""							,   )
				parser.add_argument("""--n_docs"""							,   default=5							,   type=SCREAMING_SNAKE_CASE__							,   help="""Number of retrieved docs"""  )
				parser.add_argument(
				    """--model_name_or_path"""							,   default=SCREAMING_SNAKE_CASE__							,   type=SCREAMING_SNAKE_CASE__							,   required=SCREAMING_SNAKE_CASE__							,   help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""							,   )
				parser.add_argument(
				    """--eval_mode"""							,   choices=["""e2e""", """retrieval"""]							,   default="""e2e"""							,   type=SCREAMING_SNAKE_CASE__							,   help=(
				        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
				        """ precision@k."""
				    )							,   )
				parser.add_argument("""--k"""							,   default=1							,   type=SCREAMING_SNAKE_CASE__							,   help="""k for the precision@k calculation"""  )
				parser.add_argument(
				    """--evaluation_set"""							,   default=SCREAMING_SNAKE_CASE__							,   type=SCREAMING_SNAKE_CASE__							,   required=SCREAMING_SNAKE_CASE__							,   help="""Path to a file containing evaluation samples"""							,   )
				parser.add_argument(
				    """--gold_data_path"""							,   default=SCREAMING_SNAKE_CASE__							,   type=SCREAMING_SNAKE_CASE__							,   required=SCREAMING_SNAKE_CASE__							,   help="""Path to a tab-separated file with gold samples"""							,   )
				parser.add_argument(
				    """--gold_data_mode"""							,   default="""qa"""							,   type=SCREAMING_SNAKE_CASE__							,   choices=["""qa""", """ans"""]							,   help=(
				        """Format of the gold data file"""
				        """qa - a single line in the following format: question [tab] answer_list"""
				        """ans - a single line of the gold file contains the expected answer string"""
				    )							,   )
				parser.add_argument(
				    """--predictions_path"""							,   type=SCREAMING_SNAKE_CASE__							,   default="""predictions.txt"""							,   help="""Name of the predictions file, to be stored in the checkpoints directory"""							,   )
				parser.add_argument(
				    """--eval_all_checkpoints"""							,   action="""store_true"""							,   help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""							,   )
				parser.add_argument(
				    """--eval_batch_size"""							,   default=8							,   type=SCREAMING_SNAKE_CASE__							,   help="""Batch size per GPU/CPU for evaluation."""							,   )
				parser.add_argument(
				    """--recalculate"""							,   help="""Recalculate predictions even if the prediction file exists"""							,   action="""store_true"""							,   )
				parser.add_argument(
				    """--num_beams"""							,   default=4							,   type=SCREAMING_SNAKE_CASE__							,   help="""Number of beams to be used when generating answers"""							,   )
				parser.add_argument("""--min_length"""							,   default=1							,   type=SCREAMING_SNAKE_CASE__							,   help="""Min length of the generated answers"""  )
				parser.add_argument("""--max_length"""							,   default=50							,   type=SCREAMING_SNAKE_CASE__							,   help="""Max length of the generated answers"""  )

				parser.add_argument(
				    """--print_predictions"""							,   action="""store_true"""							,   help="""If True, prints predictions while evaluating."""							,   )
				parser.add_argument(
				    """--print_docs"""							,   action="""store_true"""							,   help="""If True, prints docs retried while generating."""							,   )
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   parser.parse_args()
				_SCREAMING_SNAKE_CASE				: int  	=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
				return args







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				if args.model_type is None:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   infer_model_type(args.model_name_or_path  )
								assert args.model_type is not None
				if args.model_type.startswith("""rag"""  ):
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
								_SCREAMING_SNAKE_CASE				: str  	=   args.n_docs
								if args.index_name is not None:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args.index_name
								if args.index_path is not None:
												_SCREAMING_SNAKE_CASE				: str  	=   args.index_path
				else:
								_SCREAMING_SNAKE_CASE				: Any  	=   BartForConditionalGeneration

				_SCREAMING_SNAKE_CASE				: int  	=   (
				    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info("""Evaluate the following checkpoints: %s"""							,   SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Dict  	=   get_scores if args.eval_mode == """e2e""" else get_precision_at_k
				_SCREAMING_SNAKE_CASE				: str  	=   evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path  ) and (not args.recalculate):
												logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path  )  )
												score_fn(SCREAMING_SNAKE_CASE__							,   args.predictions_path							,   args.gold_data_path  )
												continue

								logger.info("""***** Running evaluation for {} *****""".format(SCREAMING_SNAKE_CASE__  )  )
								logger.info("""  Batch size = %d"""							,   args.eval_batch_size  )
								logger.info("""  Predictions will be stored under {}""".format(args.predictions_path  )  )

								if args.model_type.startswith("""rag"""  ):
												_SCREAMING_SNAKE_CASE				: Any  	=   RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: int  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__							,   retriever=SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
												model.retriever.init_retrieval()
								else:
												_SCREAMING_SNAKE_CASE				: Dict  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__							,   **SCREAMING_SNAKE_CASE__  )
								model.to(args.device  )

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

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


if __name__ == "__main__":
				UpperCAmelCase_	:     int							     =	get_args()
				main(args)




