

"""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 					__UpperCamelCase (     a__:			List[Any]			,						a__:			str=False			,						a__:			Union[str, Any]=False			,						a__:			Optional[int]="data2vec."      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''backbone.''' if is_semantic else ''''''

					__lowercase        =      []
					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 					__UpperCamelCase (     a__:			int			,						a__:			str			,						a__:			Optional[int]=False			,						a__:			Any=False			,						a__:			Union[str, Any]="data2vec_vision."      )					->      Optional[Any]:




					"""simple docstring"""



					for i in range(config.num_hidden_layers      ):
										__lowercase        =      '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										__lowercase        =      state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
										__lowercase        =      state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
										__lowercase        =      state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )

										__lowercase        =      in_proj_weight[
										    : config.hidden_size, :
										]
										__lowercase        =      q_bias
										__lowercase        =      in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__lowercase        =      in_proj_weight[
										    -config.hidden_size :, :
										]
										__lowercase        =      v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__lowercase        =      state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
										__lowercase        =      state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )

										__lowercase        =      gamma_a
										__lowercase        =      gamma_a

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

															__lowercase        =      table
															__lowercase        =      index





def 					__UpperCamelCase (     )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      argparse.ArgumentParser(
					    '''Convert Data2VecVision to HF for image classification and pretraining'''			,						add_help=UpperCamelCase__      )
					parser.add_argument('''--hf_checkpoint_name'''			,						type=UpperCamelCase__      )
					parser.add_argument('''--input_size'''			,						default=2_24			,						type=UpperCamelCase__			,						help='''images input size'''      )
					parser.add_argument('''--beit_checkpoint'''			,						default=''''''			,						help='''beit checkpoint'''      )

					return parser.parse_args()





def 					__UpperCamelCase (     a__:			Any			,						a__:			Tuple			,						a__:			Union[str, Any]      )					->      Dict:




					"""simple docstring"""



					def load_state_dict(a__:			int			,						a__:			Optional[int]			,						a__:			str=""			,						a__:			List[str]="relative_position_index"      ):
										__lowercase        =      []
										__lowercase        =      []
										__lowercase        =      []
										# copy state_dict so _load_from_state_dict can modify it
										__lowercase        =      getattr(UpperCamelCase__			,						'''_metadata'''			,						UpperCamelCase__      )
										__lowercase        =      state_dict.copy()
										if metadata is not None:
															__lowercase        =      metadata

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

										load(UpperCamelCase__			,						prefix=UpperCamelCase__      )

										__lowercase        =      []
										__lowercase        =      []
										for key in missing_keys:
															__lowercase        =      True
															for ignore_key in ignore_missing.split('''|'''      ):
																				if ignore_key in key:
																									__lowercase        =      False
																									break
															if keep_flag:
																				warn_missing_keys.append(UpperCamelCase__      )
															else:
																				ignore_missing_keys.append(UpperCamelCase__      )

										__lowercase        =      warn_missing_keys

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

					__lowercase        =      {
					    '''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''': 10_00,
										        '''use_mean_pooling''': True,
										        '''init_scale''': 0.0_01,
										        '''use_rel_pos_bias''': True,
										    }      )

					__lowercase        =      create_model(
					    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,						**UpperCamelCase__			,						)
					__lowercase        =      model.patch_embed.patch_size
					__lowercase        =      (args.input_size // patch_size[0], args.input_size // patch_size[1])
					__lowercase        =      torch.load(args.beit_checkpoint			,						map_location='''cpu'''      )

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

					__lowercase        =      list(checkpoint_model.keys()      )
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(UpperCamelCase__      )

										if "relative_position_bias_table" in key:
															__lowercase        =      checkpoint_model[key]
															__lowercase    ,			__lowercase        =      rel_pos_bias.size()
															__lowercase    ,			__lowercase        =      model.state_dict()[key].size()
															__lowercase        =      model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(UpperCamelCase__			,						UpperCamelCase__			,						prefix=''''''      )

					return model





def 					__UpperCamelCase (     )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      get_args()

					__lowercase        =      '''ft1k''' in args.hf_checkpoint_name
					__lowercase        =      '''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
					__lowercase        =      DataaVecVisionConfig()
					if is_finetuned:
										__lowercase        =      True
										__lowercase        =      False
										__lowercase        =      True
										__lowercase        =      10_00

										__lowercase        =      '''huggingface/label-files'''
										__lowercase        =      '''imagenet-1k-id2label.json'''
										__lowercase        =      json.load(open(hf_hub_download(UpperCamelCase__			,						UpperCamelCase__			,						repo_type='''dataset'''      )			,						'''r'''      )      )
										__lowercase        =      {int(UpperCamelCase__      ): v for k, v in idalabel.items()}
										__lowercase        =      idalabel
										__lowercase        =      {v: k for k, v in idalabel.items()}
					else:
										__lowercase        =      False
										__lowercase        =      True
										__lowercase        =      False

					if is_large:
										__lowercase        =      10_24
										__lowercase        =      40_96
										__lowercase        =      24
										__lowercase        =      16

					# 2. Load Beit model
					__lowercase        =      load_beit_model(UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__      )
					orig_model.eval()

					# 3. Forward Beit model
					__lowercase        =      BeitImageProcessor(size=config.image_size			,						do_center_crop=UpperCamelCase__      )
					__lowercase        =      Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					__lowercase        =      image_processor(images=UpperCamelCase__			,						return_tensors='''pt'''      )
					__lowercase        =      encoding['''pixel_values''']

					__lowercase        =      (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__lowercase        =      orig_model(*UpperCamelCase__      )

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__lowercase        =      DataaVecVisionForImageClassification(UpperCamelCase__      )
										hf_model.eval()
										__lowercase        =      False
										__lowercase        =      '''data2vec_vision.'''
					else:
										__lowercase        =      DataaVecVisionModel(UpperCamelCase__      )
										hf_model.eval()
										__lowercase        =      True
										__lowercase        =      ''''''

					__lowercase        =      create_rename_keys(UpperCamelCase__			,						hf_prefix=UpperCamelCase__			,						has_lm_head=UpperCamelCase__      )
					__lowercase        =      orig_model.state_dict()
					for src, dest in rename_keys:
										__lowercase        =      state_dict.pop(UpperCamelCase__      )
										__lowercase        =      val

					read_in_q_k_v(UpperCamelCase__			,						UpperCamelCase__			,						hf_prefix=UpperCamelCase__			,						has_lm_head=UpperCamelCase__      )
					__lowercase    ,			__lowercase        =      hf_model.load_state_dict(UpperCamelCase__			,						strict=UpperCamelCase__      )
					print('''HF missing'''			,						UpperCamelCase__      )
					print('''HF unexpected_keys'''			,						UpperCamelCase__      )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__lowercase        =      hf_model(UpperCamelCase__      )

					__lowercase        =      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__lowercase        =      torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

					print(f'''max_absolute_diff = {max_absolute_diff}'''      )
					__lowercase        =      torch.allclose(UpperCamelCase__			,						UpperCamelCase__			,						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"








import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	__A						:		str          =						None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	__A						:		int          =						None
	__A						:		Tuple          =						None
	__A						:		Union[str, Any]          =						None





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								__lowerCAmelCase					=  unittest.skip("""test requires Datasets, Faiss, psutil"""   )(UpperCamelCase__   )
				return test_case

@require_distributed_retrieval
class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  tempfile.mkdtemp()
									__lowerCAmelCase					=  8

									# DPR tok
									__lowerCAmelCase					=  [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dpr_tokenizer""")
									os.makedirs(_lowercase,     exist_ok=_lowercase)
									__lowerCAmelCase					=  os.path.join(_lowercase,     DPR_VOCAB_FILES_NAMES["""vocab_file"""])
									with open(self.vocab_file,     """w""",     encoding="""utf-8""") as vocab_writer:
													vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

									# BART tok
									__lowerCAmelCase					=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									__lowerCAmelCase					=  dict(zip(_lowercase,     range(len(_lowercase))))
									__lowerCAmelCase					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									__lowerCAmelCase					=  {"""unk_token""": """<unk>"""}

									__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """bart_tokenizer""")
									os.makedirs(_lowercase,     exist_ok=_lowercase)
									__lowerCAmelCase					=  os.path.join(_lowercase,     BART_VOCAB_FILES_NAMES["""vocab_file"""])
									__lowerCAmelCase					=  os.path.join(_lowercase,     BART_VOCAB_FILES_NAMES["""merges_file"""])
									with open(self.vocab_file,     """w""",     encoding="""utf-8""") as fp:
													fp.write(json.dumps(_lowercase) + """\n""")
									with open(self.merges_file,     """w""",     encoding="""utf-8""") as fp:
													fp.write("""\n""".join(_lowercase))




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """dpr_tokenizer"""))




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """bart_tokenizer"""))




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									shutil.rmtree(self.tmpdirname)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)],
									    })
									dataset.add_faiss_index("""embeddings""",     string_factory="""Flat""",     metric_type=faiss.METRIC_INNER_PRODUCT)
									return dataset




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool,     _lowercase:		Any=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
													if init_retrieval:
																	retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  self.get_dummy_dataset()
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     )
													if init_retrieval:
																	retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		int=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
									else:
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		List[Any],     _lowercase:		bool,     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex.load_from_disk(
													        vector_size=config.retrieval_vector_size,     dataset_path=config.passages_path,     index_path=config.index_path,     ),     )
									else:
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		int,     _lowercase:		RagRetriever,     _lowercase:		np.array,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  retriever.retrieve(_lowercase,     n_docs=_lowercase)
									self.assertEqual(retrieved_doc_embeds.shape,     (2, n_docs, self.retrieval_vector_size))
									self.assertEqual(len(_lowercase),     2)
									self.assertEqual(sorted(doc_dicts[0]),     ["""embeddings""", """id""", """text""", """title"""])
									self.assertEqual(len(doc_dicts[0]["""id"""]),     _lowercase)
									self.assertEqual(doc_dicts[0]["""id"""][0],     """1""")  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0],     """0""")  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist(),     [[1], [0]])




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					@require_ray
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_ray_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()




					@require_ray
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)
									with self.assertRaises(_lowercase):
													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )
									ray.shutdown()







					@require_ray
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()





