





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 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Optional[Any]:
       if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
              UpperCamelCase_ 							=	unittest.skip("test requires Datasets, Faiss, psutil"	)(UpperCamelCase_	)
       return test_case







@require_distributed_retrieval
class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def lowercase			(		self:			Tuple		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	tempfile.mkdtemp()
              UpperCamelCase_ 							=	8

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

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

       def lowercase			(		self:			List[str]		)							->			DPRQuestionEncoderTokenizer:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[Any]		)							->			BartTokenizer:




              """simple docstring"""


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

       def lowercase			(		self:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              shutil.rmtree(self.tmpdirname		)

       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	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:			List[str]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			str=12345		)							->			RagPyTorchDistributedRetriever:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	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:
                     UpperCamelCase_ 							=	dataset
                     UpperCamelCase_ 							=	RagPyTorchDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
                     if init_retrieval:
                            retriever.init_retrieval(_SCREAMING_SNAKE_CASE		)
              return retriever

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			bool		)							->			RagRayDistributedRetriever:




              """simple docstring"""


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

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			int=12345		)							->			Dict:




              """simple docstring"""


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

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool		)							->			List[str]:




              """simple docstring"""


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

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			RagRetriever       , _SCREAMING_SNAKE_CASE:			np.array       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	retriever.retrieve(_SCREAMING_SNAKE_CASE       , n_docs=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(retrieved_doc_embeds.shape       , (2, n_docs, self.retrieval_vector_size)		)
              self.assertEqual(len(_SCREAMING_SNAKE_CASE		)       , 2		)
              self.assertEqual(sorted(doc_dicts[0]		)       , ["embeddings", "id", "text", "title"]		)
              self.assertEqual(len(doc_dicts[0]["id"]		)       , _SCREAMING_SNAKE_CASE		)
              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]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , )

       def lowercase			(		self:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , )

       @require_ray
       def lowercase			(		self:			List[str]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              ray.shutdown()

       @require_ray
       def lowercase			(		self:			Optional[Any]		)							->			Tuple:




              """simple docstring"""


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



       @require_ray
       def lowercase			(		self:			Optional[Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE       , from_disk=_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              ray.shutdown()









from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
__lowerCamelCase						= 9.8_06_65






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		None:







							"""simple docstring"""





							if not isinstance(UpperCAmelCase    ,	(int, float)     ):
														raise TypeError('''Invalid velocity. Should be a positive number.'''     )

							if not isinstance(UpperCAmelCase    ,	(int, float)     ):
														raise TypeError('''Invalid angle. Range is 1-90 degrees.'''     )

							# Ensure valid angle
							if angle > 90 or angle < 1:
														raise ValueError('''Invalid angle. Range is 1-90 degrees.'''     )

							# Ensure valid velocity
							if init_velocity < 0:
														raise ValueError('''Invalid velocity. Should be a positive number.'''     )






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		float:







							"""simple docstring"""





							check_args(UpperCAmelCase    ,	UpperCAmelCase     )
							_a  :    Dict			=						angle_to_radians(2 * angle     )
							return round(init_velocity**2 * sin(UpperCAmelCase     ) / g    ,	2     )






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		float:







							"""simple docstring"""





							check_args(UpperCAmelCase    ,	UpperCAmelCase     )
							_a  :    Union[str, Any]			=						angle_to_radians(UpperCAmelCase     )
							return round(init_velocity**2 * sin(UpperCAmelCase     ) ** 2 / (2 * g)    ,	2     )






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		float:







							"""simple docstring"""





							check_args(UpperCAmelCase    ,	UpperCAmelCase     )
							_a  :    str			=						angle_to_radians(UpperCAmelCase     )
							return round(2 * init_velocity * sin(UpperCAmelCase     ) / g    ,	2     )






def    UpperCamelCase__  (   )     ->		None:







							"""simple docstring"""





							_a		,					_a  :    Any			=						25, 20
							assert horizontal_distance(UpperCAmelCase    ,	UpperCAmelCase     ) == 4_0.9_7
							assert max_height(UpperCAmelCase    ,	UpperCAmelCase     ) == 3.7_3
							assert total_time(UpperCAmelCase    ,	UpperCAmelCase     ) == 1.7_4


if __name__ == "__main__":
						from doctest import testmod

						testmod()

						# Get input from user
						__lowerCamelCase						= float(input('Initial Velocity: ').strip())

						# Get input from user
						__lowerCamelCase						= float(input('angle: ').strip())

						# Print results
						print()
						print('Results: ')
						print(F'''Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]''')
						print(F'''Maximum Height: {max_height(init_vel, angle)!s} [m]''')
						print(F'''Total Time: {total_time(init_vel, angle)!s} [s]''')