



"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
					import faiss




@require_faiss
class 			snake_case_						(						a_				):






							def snake_case_  (   self      ):
									a_     :      Any 						=     tempfile.mkdtemp()
									a_     :      Any 						=     8

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

									a_     :      Optional[Any] 						=     os.path.join(self.tmpdirname			,     "bart_tokenizer"      )
									os.makedirs(a_			,     exist_ok=a_      )
									a_     :      int 						=     os.path.join(a_			,     BART_VOCAB_FILES_NAMES["vocab_file"]      )
									a_     :      int 						=     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 snake_case_  (   self      ):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "dpr_tokenizer"      )      )






							def snake_case_  (   self      ):
									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "dpr_tokenizer"      )      )






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






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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     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 snake_case_  (   self      ):
									a_     :      Optional[int] 						=     self.get_dummy_dataset()
									a_     :      Union[str, Any] 						=     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_     :      List[str] 						=     dataset
											a_     :      List[str] 						=     RagRetriever(
											    a_			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     )
									return retriever






							def snake_case_  (   self			,     a_      ):
									a_     :      Dict 						=     self.get_dummy_dataset()
									a_     :      Any 						=     RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size			,     question_encoder=DPRConfig().to_dict()			,     generator=BartConfig().to_dict()			,     index_name="custom"			,     )
									if from_disk:
											a_     :      Dict 						=     os.path.join(self.tmpdirname			,     "dataset"      )
											a_     :      Optional[Any] 						=     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_     :      Optional[Any] 						=     RagRetriever(
											    a_			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     )
									else:
											a_     :      Dict 						=     RagRetriever(
											    a_			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     index=CustomHFIndex(config.retrieval_vector_size			,     a_      )			,     )
									return retriever






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

									a_     :      List[Any] 						=     os.path.join(self.tmpdirname			,     "hf_bert_base.hnswSQ8_correct_phi_128.c_index"      )
									dataset.save_faiss_index("embeddings"			,     index_file_name + ".index.dpr"      )
									pickle.dump(dataset["id"]			,     open(index_file_name + ".index_meta.dpr"			,     "wb"      )      )

									a_     :      Optional[Any] 						=     os.path.join(self.tmpdirname			,     "psgs_w100.tsv.pkl"      )
									a_     :      Optional[int] 						=     {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
									pickle.dump(a_			,     open(a_			,     "wb"      )      )

									a_     :      str 						=     RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size			,     question_encoder=DPRConfig().to_dict()			,     generator=BartConfig().to_dict()			,     index_name="legacy"			,     index_path=self.tmpdirname			,     )
									a_     :      Any 						=     RagRetriever(
									    a_			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()      )
									return retriever






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     1
									a_     :      int 						=     self.get_dummy_canonical_hf_index_retriever()
									a_     :      Dict 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_						,				a_						,				a_     :      Tuple 						=     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 snake_case_  (   self      ):
									a_     :      int 						=     self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											with patch("transformers.models.rag.retrieval_rag.load_dataset"      ) as mock_load_dataset:
													a_     :      Union[str, Any] 						=     self.get_dummy_dataset()
													retriever.save_pretrained(a_      )
													a_     :      Dict 						=     RagRetriever.from_pretrained(a_      )
													self.assertIsInstance(a_			,     a_      )
													a_     :      Any 						=     np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
													a_     :      Dict 						=     retriever.retrieve(a_			,     n_docs=1      )
													self.assertTrue(out is not None      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     1
									a_     :      Union[str, Any] 						=     self.get_dummy_custom_hf_index_retriever(from_disk=a_      )
									a_     :      Optional[Any] 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_						,				a_						,				a_     :      Any 						=     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 snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.get_dummy_custom_hf_index_retriever(from_disk=a_      )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(a_      )
											a_     :      str 						=     RagRetriever.from_pretrained(a_      )
											self.assertIsInstance(a_			,     a_      )
											a_     :      Tuple 						=     np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
											a_     :      Union[str, Any] 						=     retriever.retrieve(a_			,     n_docs=1      )
											self.assertTrue(out is not None      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     1
									a_     :      Union[str, Any] 						=     self.get_dummy_custom_hf_index_retriever(from_disk=a_      )
									a_     :      Dict 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_						,				a_						,				a_     :      str 						=     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 snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_dummy_custom_hf_index_retriever(from_disk=a_      )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(a_      )
											a_     :      Tuple 						=     RagRetriever.from_pretrained(a_      )
											self.assertIsInstance(a_			,     a_      )
											a_     :      str 						=     np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
											a_     :      Union[str, Any] 						=     retriever.retrieve(a_			,     n_docs=1      )
											self.assertTrue(out is not None      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     1
									a_     :      Any 						=     self.get_dummy_legacy_index_retriever()
									a_     :      Any 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_						,				a_						,				a_     :      str 						=     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]      )			,     ["text", "title"]      )
									self.assertEqual(len(doc_dicts[0]["text"]      )			,     a_      )
									self.assertEqual(doc_dicts[0]["text"][0]			,     "bar"      )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["text"][0]			,     "foo"      )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()			,     [[1], [0]]      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(a_      )
											a_     :      List[Any] 						=     RagRetriever.from_pretrained(a_      )
											self.assertIsInstance(a_			,     a_      )
											a_     :      Any 						=     np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
											a_     :      Dict 						=     retriever.retrieve(a_			,     n_docs=1      )
											self.assertTrue(out is not None      )






							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def snake_case_  (   self      ):
									import torch

									a_     :      Dict 						=     1
									a_     :      Optional[int] 						=     self.get_dummy_canonical_hf_index_retriever()
									a_     :      Dict 						=     [[5, 7], [1_0, 1_1]]
									a_     :      int 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_     :      List[Any] 						=     retriever(a_			,     a_			,     prefix=retriever.config.generator.prefix			,     n_docs=a_      )
									a_						,				a_						,				a_     :      Optional[Any] 						=     (
									    out["context_input_ids"],
									    out["context_attention_mask"],
									    out["retrieved_doc_embeds"],
									)
									self.assertEqual(retrieved_doc_embeds.shape			,     (2, n_docs, self.retrieval_vector_size)      )
									self.assertIsInstance(a_			,     a_      )
									self.assertIsInstance(a_			,     a_      )
									self.assertIsInstance(a_			,     np.ndarray      )

									a_     :      Optional[Any] 						=     retriever(
									    a_			,     a_			,     prefix=retriever.config.generator.prefix			,     n_docs=a_			,     return_tensors="pt"			,     )
									a_						,				a_						,				a_						,				a_     :      Any 						=     (  # noqa: F841
									    out["context_input_ids"],
									    out["context_attention_mask"],
									    out["retrieved_doc_embeds"],
									    out["doc_ids"],
									)
									self.assertEqual(retrieved_doc_embeds.shape			,     (2, n_docs, self.retrieval_vector_size)      )
									self.assertIsInstance(a_			,     torch.Tensor      )
									self.assertIsInstance(a_			,     torch.Tensor      )
									self.assertIsInstance(a_			,     torch.Tensor      )






							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.get_dpr_ctx_encoder_tokenizer()
									a_     :      Optional[Any] 						=     1
									a_     :      str 						=     self.get_dummy_custom_hf_index_retriever(from_disk=a_      )
									retriever.set_ctx_encoder_tokenizer(a_      )

									a_     :      Tuple 						=     [[5, 7], [1_0, 1_1]]
									a_     :      Union[str, Any] 						=     np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,     dtype=np.floataa      )
									a_     :      int 						=     retriever(a_			,     a_			,     prefix=retriever.config.generator.prefix			,     n_docs=a_      )

									self.assertEqual(
									    len(a_      )			,     6      )  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")      )			,     a_      )  # check for doc token related keys in dictionary.




"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)