






"""simple docstring"""



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
A__	:    Tuple			 = logging.get_logger()







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:List[nn.Module]             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:list             =			field(default_factory=snake_case__       )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str       ,	snake_case__					:  Tensor       ,	snake_case__					:  Tensor ):
										lowerCamelCase_	:							Union[str, Any]									=len(list(m.modules() ) ) == 1 or isinstance(snake_case__       ,	nn.Convad ) or isinstance(snake_case__       ,	nn.BatchNormad )
										if has_not_submodules:
																	self.traced.append(snake_case__ )


			def __call__(				self					:  List[str]       ,	snake_case__					:  Tensor ):
										for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook ) )
										self.module(snake_case__ )
										[x.remove() for x in self.handles]
										return self


			@property
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda snake_case__ : len(list(x.state_dict().keys() ) ) > 0       ,	self.traced ) )







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:int             =			1
			_UpperCAmelCase		:List             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:List             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:bool             =			True


			def __call__(				self					:  Union[str, Any]       ,	snake_case__					:  Tensor ):
										lowerCamelCase_	:							int									=Tracker(self.dest )(snake_case__ ).parametrized
										lowerCamelCase_	:							Union[str, Any]									=Tracker(self.src )(snake_case__ ).parametrized

										lowerCamelCase_	:							Tuple									=list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip       ,	snake_case__ ) )
										lowerCamelCase_	:							List[str]									=list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip       ,	snake_case__ ) )

										if len(snake_case__ ) != len(snake_case__ ) and self.raise_if_mismatch:
																	raise Exception(
																	    F"""Numbers of operations are different. Source module has {len(snake_case__ )} operations while"""
																	    F""" destination module has {len(snake_case__ )}.""" )

										for dest_m, src_m in zip(snake_case__       ,	snake_case__ ):
																	dest_m.load_state_dict(src_m.state_dict() )
																	if self.verbose == 1:
																								print(F"""Transfered from={src_m} to={dest_m}""" )







class        lowercase__      (							nn.Module       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  nn.Module ):
										super().__init__()

										lowerCamelCase_	:							List[Tuple[str, nn.Module]]									=[]
										# - get the stem
										feature_blocks.append(("conv1", model.stem) )
										# - get all the feature blocks
										for k, v in model.trunk_output.named_children():
																	assert k.startswith("block" ), F"""Unexpected layer name {k}"""
																	lowerCamelCase_	:							Any									=len(snake_case__ ) + 1
																	feature_blocks.append((F"""res{block_index}""", v) )

										lowerCamelCase_	:							Optional[Any]									=nn.ModuleDict(snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Tensor ):
										return get_trunk_forward_outputs(
										    snake_case__       ,	out_feat_keys=snake_case__       ,	feature_blocks=self._feature_blocks       ,	)







class        lowercase__      (							snake_case__       ):


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str ):
										lowerCamelCase_	:							List[Any]									=x.split("-" )
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )


			def __getitem__(				self					:  Union[str, Any]       ,	snake_case__					:  str ):
										# default to timm!
										if x not in self:
																	lowerCamelCase_	:							List[Any]									=self.convert_name_to_timm(snake_case__ )
																	lowerCamelCase_	:							List[str]									=partial(lambda: (timm.create_model(snake_case__       ,	pretrained=snake_case__ ).eval(), None) )

										else:
																	lowerCamelCase_	:							Any									=super().__getitem__(snake_case__ )

										return val







class        lowercase__      (							snake_case__       ):


			def __getitem__(				self					:  List[Any]       ,	snake_case__					:  str ):
										if "seer" in x and "in1k" not in x:
																	lowerCamelCase_	:							Dict									=RegNetModel
										else:
																	lowerCamelCase_	:							Dict									=RegNetForImageClassification
										return val


def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   List[Tuple[str, str]]  )    ->     int:
							for from_key, to_key in keys:
														lowerCamelCase_	:							Tuple									=from_state_dict[from_key].clone()
														print(F"""Copied key={from_key} to={to_key}"""  )
							return to_state_dict

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Callable[[], nn.Module]    ,						lowerCamelCase__     :   Callable[[], nn.Module]    ,						lowerCamelCase__     :   RegNetConfig    ,						lowerCamelCase__     :   Path    ,						lowerCamelCase__     :   bool = True    ,						)    ->     str:
							print(F"""Converting {name}..."""  )
							with torch.no_grad():
														lowerCamelCase_							,						lowerCamelCase_	:							Dict									=from_model_func()
														lowerCamelCase_	:							str									=our_model_func(lowerCamelCase__  ).eval()
														lowerCamelCase_	:							List[str]									=ModuleTransfer(src=lowerCamelCase__    ,						dest=lowerCamelCase__    ,						raise_if_mismatch=lowerCamelCase__  )
														lowerCamelCase_	:							Optional[int]									=torch.randn((1, 3, 224, 224)  )
														module_transfer(lowerCamelCase__  )

							if from_state_dict is not None:
														lowerCamelCase_	:							List[str]									=[]
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					lowerCamelCase_	:							Union[str, Any]									=[("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
														lowerCamelCase_	:							Tuple									=manually_copy_vissl_head(lowerCamelCase__    ,						our_model.state_dict()    ,						lowerCamelCase__  )
														our_model.load_state_dict(lowerCamelCase__  )

							lowerCamelCase_	:							Optional[Any]									=our_model(lowerCamelCase__    ,						output_hidden_states=lowerCamelCase__  )
							lowerCamelCase_	:							Any									=(
							    our_outputs.logits if isinstance(lowerCamelCase__    ,						lowerCamelCase__  ) else our_outputs.last_hidden_state
							)

							lowerCamelCase_	:							Dict									=from_model(lowerCamelCase__  )
							lowerCamelCase_	:							Union[str, Any]									=from_output[-1] if type(lowerCamelCase__  ) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														lowerCamelCase_	:							int									=our_outputs.hidden_states[-1]

							assert torch.allclose(lowerCamelCase__    ,						lowerCamelCase__  ), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name    ,						commit_message="Add model"    ,						use_temp_dir=lowerCamelCase__    ,						)

														lowerCamelCase_	:							Tuple									=224 if "seer" not in name else 384
														# we can use the convnext one
														lowerCamelCase_	:							Any									=AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"    ,						size=lowerCamelCase__  )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name    ,						commit_message="Add image processor"    ,						use_temp_dir=lowerCamelCase__    ,						)

														print(F"""Pushed {name}"""  )

def 			_snake_case    (					lowerCamelCase__     :   Path    ,						lowerCamelCase__     :   str = None    ,						lowerCamelCase__     :   bool = True  )    ->     Optional[Any]:
							lowerCamelCase_	:							Union[str, Any]									="imagenet-1k-id2label.json"
							lowerCamelCase_	:							Any									=1_000
							lowerCamelCase_	:							Tuple									=(1, num_labels)

							lowerCamelCase_	:							List[str]									="huggingface/label-files"
							lowerCamelCase_	:							Union[str, Any]									=num_labels
							lowerCamelCase_	:							List[Any]									=json.load(open(cached_download(hf_hub_url(lowerCamelCase__    ,						lowerCamelCase__    ,						repo_type="dataset"  )  )    ,						"r"  )  )
							lowerCamelCase_	:							List[Any]									={int(lowerCamelCase__  ): v for k, v in idalabel.items()}

							lowerCamelCase_	:							str									=idalabel
							lowerCamelCase_	:							str									={v: k for k, v in idalabel.items()}

							lowerCamelCase_	:							List[Any]									=partial(lowerCamelCase__    ,						num_labels=lowerCamelCase__    ,						idalabel=lowerCamelCase__    ,						labelaid=lowerCamelCase__  )

							lowerCamelCase_	:							Union[str, Any]									={
							    "regnet-x-002": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]    ,						hidden_sizes=[24, 56, 152, 368]    ,						groups_width=8    ,						layer_type="x"  ),
							    "regnet-x-004": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]    ,						hidden_sizes=[32, 64, 160, 384]    ,						groups_width=16    ,						layer_type="x"  ),
							    "regnet-x-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]    ,						hidden_sizes=[48, 96, 240, 528]    ,						groups_width=24    ,						layer_type="x"  ),
							    "regnet-x-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]    ,						hidden_sizes=[64, 128, 288, 672]    ,						groups_width=16    ,						layer_type="x"  ),
							    "regnet-x-016": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]    ,						hidden_sizes=[72, 168, 408, 912]    ,						groups_width=24    ,						layer_type="x"  ),
							    "regnet-x-032": ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]    ,						hidden_sizes=[96, 192, 432, 1_008]    ,						groups_width=48    ,						layer_type="x"  ),
							    "regnet-x-040": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]    ,						hidden_sizes=[80, 240, 560, 1_360]    ,						groups_width=40    ,						layer_type="x"  ),
							    "regnet-x-064": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]    ,						hidden_sizes=[168, 392, 784, 1_624]    ,						groups_width=56    ,						layer_type="x"  ),
							    "regnet-x-080": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]    ,						hidden_sizes=[80, 240, 720, 1_920]    ,						groups_width=120    ,						layer_type="x"  ),
							    "regnet-x-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]    ,						hidden_sizes=[224, 448, 896, 2_240]    ,						groups_width=112    ,						layer_type="x"  ),
							    "regnet-x-160": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]    ,						hidden_sizes=[256, 512, 896, 2_048]    ,						groups_width=128    ,						layer_type="x"  ),
							    "regnet-x-320": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]    ,						hidden_sizes=[336, 672, 1_344, 2_520]    ,						groups_width=168    ,						layer_type="x"  ),
							    # y variant
							    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,						hidden_sizes=[24, 56, 152, 368]    ,						groups_width=8  ),
							    "regnet-y-004": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]    ,						hidden_sizes=[48, 104, 208, 440]    ,						groups_width=8  ),
							    "regnet-y-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]    ,						hidden_sizes=[48, 112, 256, 608]    ,						groups_width=16  ),
							    "regnet-y-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]    ,						hidden_sizes=[64, 128, 320, 768]    ,						groups_width=16  ),
							    "regnet-y-016": ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]    ,						hidden_sizes=[48, 120, 336, 888]    ,						groups_width=24  ),
							    "regnet-y-032": ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]    ,						hidden_sizes=[72, 216, 576, 1_512]    ,						groups_width=24  ),
							    "regnet-y-040": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]    ,						hidden_sizes=[128, 192, 512, 1_088]    ,						groups_width=64  ),
							    "regnet-y-064": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]    ,						hidden_sizes=[144, 288, 576, 1_296]    ,						groups_width=72  ),
							    "regnet-y-080": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]    ,						hidden_sizes=[168, 448, 896, 2_016]    ,						groups_width=56  ),
							    "regnet-y-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]    ,						hidden_sizes=[224, 448, 896, 2_240]    ,						groups_width=112  ),
							    "regnet-y-160": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]    ,						hidden_sizes=[224, 448, 1_232, 3_024]    ,						groups_width=112  ),
							    "regnet-y-320": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[232, 696, 1_392, 3_712]    ,						groups_width=232  ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]    ,						hidden_sizes=[232, 696, 1_392, 3_712]    ,						groups_width=232  ),
							    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]    ,						hidden_sizes=[328, 984, 1_968, 4_920]    ,						groups_width=328  ),
							    "regnet-y-1280-seer": RegNetConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[528, 1_056, 2_904, 7_392]    ,						groups_width=264  ),
							    "regnet-y-2560-seer": RegNetConfig(
							        depths=[3, 7, 16, 1]    ,						hidden_sizes=[640, 1_696, 2_544, 5_088]    ,						groups_width=640  ),
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,						groups_width=1_010  ),
							    # finetuned on imagenet
							    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[232, 696, 1_392, 3_712]    ,						groups_width=232  ),
							    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[328, 984, 1_968, 4_920]    ,						groups_width=328  ),
							    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[528, 1_056, 2_904, 7_392]    ,						groups_width=264  ),
							    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]    ,						hidden_sizes=[640, 1_696, 2_544, 5_088]    ,						groups_width=640  ),
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,						groups_width=1_010  ),
							}

							lowerCamelCase_	:							Any									=NameToOurModelFuncMap()
							lowerCamelCase_	:							str									=NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Callable[[], nn.Module]  ) -> Tuple[nn.Module, Dict]:
														lowerCamelCase_	:							str									=torch.hub.load_state_dict_from_url(lowerCamelCase__    ,						model_dir=str(lowerCamelCase__  )    ,						map_location="cpu"  )
														lowerCamelCase_	:							List[Any]									=model_func()
														# check if we have a head, if yes add it
														lowerCamelCase_	:							int									=files["classy_state_dict"]["base_model"]["model"]
														lowerCamelCase_	:							List[Any]									=model_state_dict["trunk"]
														model.load_state_dict(lowerCamelCase__  )
														return model.eval(), model_state_dict["heads"]

							# pretrained
							lowerCamelCase_	:							str									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )    ,						)

							lowerCamelCase_	:							Tuple									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )    ,						)

							lowerCamelCase_	:							Optional[Any]									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )    ,						)

							lowerCamelCase_	:							List[str]									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"    ,						lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27    ,						group_width=1_010    ,						w_a=1_744    ,						w_a=620.83    ,						w_m=2.52  )  )  )    ,						)

							# IN1K finetuned
							lowerCamelCase_	:							str									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )    ,						)

							lowerCamelCase_	:							Any									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )    ,						)

							lowerCamelCase_	:							Dict									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )    ,						)

							lowerCamelCase_	:							List[Any]									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"    ,						lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27    ,						group_width=1_010    ,						w_a=1_744    ,						w_a=620.83    ,						w_m=2.52  )  )  )    ,						)

							if model_name:
														convert_weight_and_push(
														    lowerCamelCase__    ,						names_to_from_model_map[model_name]    ,						names_to_ours_model_map[model_name]    ,						names_to_config[model_name]    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    lowerCamelCase__    ,						names_to_from_model_map[model_name]    ,						names_to_ours_model_map[model_name]    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						)
							return config, expected_shape


if __name__ == "__main__":
						A__	:    Optional[int]			 = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default=None,
						    type=str,
						    help=(
						        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
						        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
						    ),
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=Path,
						    required=True,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    default=True,
						    type=bool,
						    required=False,
						    help='If True, push model and image processor to the hub.',
						)

						A__	:    Optional[Any]			 = parser.parse_args()

						A__	:    Path			 = args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)











"""simple docstring"""



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


A__	:    Tuple			 = logging.get_logger(__name__)

A__	:    Tuple			 = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A__	:    Optional[int]			 = {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
A__	:    int			 = {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

A__	:    Dict			 = {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A__	:    Dict			 = {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A__	:    Any			 = {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


A__	:    Union[str, Any]			 = {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A__	:    Tuple			 = {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A__	:    Any			 = {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Dict             =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:str             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


A__	:    Union[str, Any]			 = collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__	:    Union[str, Any]			 = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__	:    List[Any]			 = r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '







@add_start_docstrings(snake_case__       )
class        lowercase__      :


			def __call__(				self					:  List[str]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[str] = None       ,	snake_case__					:  Optional[str] = None       ,	snake_case__					:  Union[bool, str] = False       ,	snake_case__					:  Union[bool, str] = False       ,	snake_case__					:  Optional[int] = None       ,	snake_case__					:  Optional[Union[str, TensorType]] = None       ,	snake_case__					:  Optional[bool] = None       ,	**snake_case__					:  Tuple       ,	):
										if titles is None and texts is None:
																	return super().__call__(
																	    snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__       ,	return_attention_mask=snake_case__       ,	**snake_case__       ,	)
										elif titles is None or texts is None:
																	lowerCamelCase_	:							Dict									=titles if texts is None else texts
																	return super().__call__(
																	    snake_case__       ,	snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__       ,	return_attention_mask=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Tuple									=titles if not isinstance(snake_case__       ,	snake_case__ ) else [titles]
										lowerCamelCase_	:							List[str]									=texts if not isinstance(snake_case__       ,	snake_case__ ) else [texts]
										lowerCamelCase_	:							int									=len(snake_case__ )
										lowerCamelCase_	:							Any									=questions if not isinstance(snake_case__       ,	snake_case__ ) else [questions] * n_passages
										if len(snake_case__ ) != len(snake_case__ ):
																	raise ValueError(
																	    F"""There should be as many titles than texts but got {len(snake_case__ )} titles and {len(snake_case__ )} texts.""" )
										lowerCamelCase_	:							Any									=super().__call__(snake_case__       ,	snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__ )["input_ids"]
										lowerCamelCase_	:							Union[str, Any]									=super().__call__(snake_case__       ,	add_special_tokens=snake_case__       ,	padding=snake_case__       ,	truncation=snake_case__ )["input_ids"]
										lowerCamelCase_	:							str									={
										    "input_ids": [
										        (encoded_question_and_title + encoded_text)[:max_length]
										        if max_length is not None and truncation
										        else encoded_question_and_title + encoded_text
										        for encoded_question_and_title, encoded_text in zip(snake_case__       ,	snake_case__ )
										    ]
										}
										if return_attention_mask is not False:
																	lowerCamelCase_	:							List[Any]									=[]
																	for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
																	lowerCamelCase_	:							Optional[int]									=attention_mask
										return self.pad(snake_case__       ,	padding=snake_case__       ,	max_length=snake_case__       ,	return_tensors=snake_case__ )


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  BatchEncoding       ,	snake_case__					:  DPRReaderOutput       ,	snake_case__					:  int = 16       ,	snake_case__					:  int = 64       ,	snake_case__					:  int = 4       ,	):
										lowerCamelCase_	:							str									=reader_input["input_ids"]
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							str									=reader_output[:3]
										lowerCamelCase_	:							List[Any]									=len(snake_case__ )
										lowerCamelCase_	:							Any									=sorted(range(snake_case__ )       ,	reverse=snake_case__       ,	key=relevance_logits.__getitem__ )
										lowerCamelCase_	:							List[DPRReaderOutput]									=[]
										for doc_id in sorted_docs:
																	lowerCamelCase_	:							int									=list(input_ids[doc_id] )
																	# assuming question & title information is at the beginning of the sequence
																	lowerCamelCase_	:							List[Any]									=sequence_ids.index(self.sep_token_id       ,	2 ) + 1  # second sep id
																	if sequence_ids[-1] == self.pad_token_id:
																								lowerCamelCase_	:							Optional[int]									=sequence_ids.index(self.pad_token_id )
																	else:
																								lowerCamelCase_	:							int									=len(snake_case__ )

																	lowerCamelCase_	:							int									=self._get_best_spans(
																	    start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,	end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,	max_answer_length=snake_case__       ,	top_spans=snake_case__       ,	)
																	for start_index, end_index in best_spans:
																								start_index += passage_offset
																								end_index += passage_offset
																								nbest_spans_predictions.append(
																								    DPRSpanPrediction(
																								        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,	relevance_score=relevance_logits[doc_id]       ,	doc_id=snake_case__       ,	start_index=snake_case__       ,	end_index=snake_case__       ,	text=self.decode(sequence_ids[start_index : end_index + 1] )       ,	) )
																	if len(snake_case__ ) >= num_spans:
																								break
										return nbest_spans_predictions[:num_spans]


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  List[int]       ,	snake_case__					:  List[int]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	):
										lowerCamelCase_	:							Any									=[]
										for start_index, start_score in enumerate(snake_case__ ):
																	for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
																								scores.append(((start_index, start_index + answer_length), start_score + end_score) )
										lowerCamelCase_	:							List[str]									=sorted(snake_case__       ,	key=lambda snake_case__ : x[1]       ,	reverse=snake_case__ )
										lowerCamelCase_	:							List[Any]									=[]
										for (start_index, end_index), score in scores:
																	if start_index > end_index:
																								raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
																	lowerCamelCase_	:							Tuple									=end_index - start_index + 1
																	if length > max_answer_length:
																								raise ValueError(F"""Span is too long: {length} > {max_answer_length}""" )
																	if any(
																	    start_index <= prev_start_index <= prev_end_index <= end_index
																	    or prev_start_index <= start_index <= end_index <= prev_end_index
																	    for (prev_start_index, prev_end_index) in chosen_span_intervals ):
																								continue
																	chosen_span_intervals.append((start_index, end_index) )

																	if len(snake_case__ ) == top_spans:
																								break
										return chosen_span_intervals







@add_end_docstrings(snake_case__       )
class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:List[str]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Union[str, Any]             =			READER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:int             =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:int             =			READER_PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase		:Optional[Any]             =			["input_ids", "attention_mask"]



