



"""simple docstring"""




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:
	UpperCAmelCase								=   None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	UpperCAmelCase								=   None
	UpperCAmelCase								=   None
	UpperCAmelCase								=   None

def      A_ (		__a       :			Tuple						):

				"""simple docstring"""
				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								a__			 =						unittest.skip("""test requires Datasets, Faiss, psutil"""						)(__a						)
				return test_case




@require_distributed_retrieval
class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()
											a__			 =						8

											# DPR tok
											a__			 =						[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											a__			 =						os.path.join(self.tmpdirname					,					"""dpr_tokenizer"""						)
											os.makedirs(a_					,					exist_ok=a_						)
											a__			 =						os.path.join(a_					,					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
											a__			 =						[
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											a__			 =						dict(zip(a_					,					range(len(a_						)						)						)						)
											a__			 =						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											a__			 =						{"""unk_token""": """<unk>"""}

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


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


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


							def _a (							self						):
											shutil.rmtree(self.tmpdirname						)


							def _a (							self						):
											a__			 =						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 _a (							self					,					a_					,					a_=12_345						):
											a__			 =						self.get_dummy_dataset()
											a__			 =						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:
															a__			 =						dataset
															a__			 =						RagPyTorchDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					)
															if init_retrieval:
																			retriever.init_retrieval(a_						)
											return retriever


							def _a (							self					,					a_						):
											# Have to run in local mode because sys.path modifications at top of
											# file are not propogated to remote workers.
											# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
											ray.init(local_mode=a_						)
											a__			 =						RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size					,					question_encoder=DPRConfig().to_dict()					,					generator=BartConfig().to_dict()					,					)
											a__			 =						ray.remote(a_						)
											a__			 =						[remote_cls.remote() for _ in range(1						)]
											with patch("""transformers.models.rag.retrieval_rag.load_dataset"""						) as mock_load_dataset:
															a__			 =						self.get_dummy_dataset()
															a__			 =						RagRayDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					retrieval_workers=a_					,					)
															if init_retrieval:
																			retriever.init_retrieval()
											return retriever


							def _a (							self					,					a_					,					a_					,					a_=12_345						):
											a__			 =						self.get_dummy_dataset()
											a__			 =						RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size					,					question_encoder=DPRConfig().to_dict()					,					generator=BartConfig().to_dict()					,					index_name="""custom"""					,					)
											if from_disk:
															a__			 =						os.path.join(self.tmpdirname					,					"""dataset"""						)
															a__			 =						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
															a__			 =						RagPyTorchDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					)
											else:
															a__			 =						RagPyTorchDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					index=CustomHFIndex(config.retrieval_vector_size					,					a_						)					,					)
											if init_retrieval:
															retriever.init_retrieval(a_						)
											return retriever


							def _a (							self					,					a_					,					a_						):
											# Have to run in local mode because sys.path modifications at top of
											# file are not propogated to remote workers.
											# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
											ray.init(local_mode=a_						)
											a__			 =						self.get_dummy_dataset()
											a__			 =						RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size					,					question_encoder=DPRConfig().to_dict()					,					generator=BartConfig().to_dict()					,					index_name="""custom"""					,					)
											a__			 =						ray.remote(a_						)
											a__			 =						[remote_cls.remote() for _ in range(1						)]
											if from_disk:
															a__			 =						os.path.join(self.tmpdirname					,					"""dataset"""						)
															a__			 =						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
															a__			 =						RagRayDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					retrieval_workers=a_					,					index=CustomHFIndex.load_from_disk(
															        vector_size=config.retrieval_vector_size					,					dataset_path=config.passages_path					,					index_path=config.index_path					,					)					,					)
											else:
															a__			 =						RagRayDistributedRetriever(
															    a_					,					question_encoder_tokenizer=self.get_dpr_tokenizer()					,					generator_tokenizer=self.get_bart_tokenizer()					,					retrieval_workers=a_					,					index=CustomHFIndex(config.retrieval_vector_size					,					a_						)					,					)
											if init_retrieval:
															retriever.init_retrieval()
											return retriever


							def _a (							self					,					a_					,					a_					,					a_						):
											a__ ,     a__ ,     a__			 =						retriever.retrieve(a_					,					n_docs=a_						)
											self.assertEqual(retrieved_doc_embeds.shape					,					(2, n_docs, self.retrieval_vector_size)						)
											self.assertEqual(len(a_						)					,					2						)
											self.assertEqual(sorted(doc_dicts[0]						)					,					["""embeddings""", """id""", """text""", """title"""]						)
											self.assertEqual(len(doc_dicts[0]["""id"""]						)					,					a_						)
											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 _a (							self						):
											a__			 =						1
											a__			 =						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=a_						)					,					a_					,					a_						)


							def _a (							self						):
											a__			 =						1
											a__			 =						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=a_					,					from_disk=a_						)					,					a_					,					a_					,					)


							def _a (							self						):
											a__			 =						1
											a__			 =						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=a_					,					from_disk=a_						)					,					a_					,					a_					,					)


							@require_ray
							def _a (							self						):
											a__			 =						1
											a__			 =						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=a_						)					,					a_					,					a_						)
											ray.shutdown()


							@require_ray
							def _a (							self						):
											a__			 =						1
											a__			 =						np.array(
											    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,					dtype=np.floataa						)
											with self.assertRaises(a_						):
															self.distributed_retriever_check(
															    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=a_					,					from_disk=a_						)					,					a_					,					a_					,					)
											ray.shutdown()







							@require_ray
							def _a (							self						):
											a__			 =						1
											a__			 =						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=a_					,					from_disk=a_						)					,					a_					,					a_						)
											ray.shutdown()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

