



import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
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.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
						from transformers.models.rag.configuration_rag import RagConfig
						from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class      UpperCamelCase    (						_UpperCAmelCase     ):



	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(UpperCAmelCase__			,	exist_ok=UpperCAmelCase__		)
							A__							=       os.path.join(UpperCAmelCase__			,	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(UpperCAmelCase__			,	range(len(UpperCAmelCase__		)		)		)		)
							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(UpperCAmelCase__			,	exist_ok=UpperCAmelCase__		)
							A__							=       os.path.join(UpperCAmelCase__			,	BART_VOCAB_FILES_NAMES["vocab_file"]		)
							A__							=       os.path.join(UpperCAmelCase__			,	BART_VOCAB_FILES_NAMES["merges_file"]		)
							with open(self.vocab_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write(json.dumps(UpperCAmelCase__		) + "\n"		)
							with open(self.merges_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write("\n".join(UpperCAmelCase__		)		)



	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		)



	@require_tokenizers
	def 			__A							(       self		):
							A__							=       os.path.join(self.tmpdirname			,	"rag_tokenizer"		)
							A__							=       RagConfig(question_encoder=DPRConfig().to_dict()			,	generator=BartConfig().to_dict()		)
							A__							=       RagTokenizer(question_encoder=self.get_dpr_tokenizer()			,	generator=self.get_bart_tokenizer()		)
							rag_config.save_pretrained(UpperCAmelCase__		)
							rag_tokenizer.save_pretrained(UpperCAmelCase__		)
							A__							=       RagTokenizer.from_pretrained(UpperCAmelCase__			,	config=UpperCAmelCase__		)
							self.assertIsInstance(new_rag_tokenizer.question_encoder			,	UpperCAmelCase__		)
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()			,	rag_tokenizer.question_encoder.get_vocab()		)
							self.assertIsInstance(new_rag_tokenizer.generator			,	UpperCAmelCase__		)
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()			,	rag_tokenizer.generator.get_vocab()		)



	@slow
	def 			__A							(       self		):
							A__							=       RagTokenizer.from_pretrained("facebook/rag-token-nq"		)
							A__							=       [
							    "who got the first nobel prize in physics",
							    "when is the next deadpool movie being released",
							    "which mode is used for short wave broadcast service",
							    "who is the owner of reading football club",
							    "when is the next scandal episode coming out",
							    "when is the last time the philadelphia won the superbowl",
							    "what is the most current adobe flash player version",
							    "how many episodes are there in dragon ball z",
							    "what is the first step in the evolution of the eye",
							    "where is gall bladder situated in human body",
							    "what is the main mineral in lithium batteries",
							    "who is the president of usa right now",
							    "where do the greasers live in the outsiders",
							    "panda is a national animal of which country",
							    "what is the name of manchester united stadium",
							]
							A__							=       tokenizer(UpperCAmelCase__		)
							self.assertIsNotNone(UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							A__							=       RagTokenizer.from_pretrained("facebook/rag-sequence-nq"		)
							A__							=       [
							    "who got the first nobel prize in physics",
							    "when is the next deadpool movie being released",
							    "which mode is used for short wave broadcast service",
							    "who is the owner of reading football club",
							    "when is the next scandal episode coming out",
							    "when is the last time the philadelphia won the superbowl",
							    "what is the most current adobe flash player version",
							    "how many episodes are there in dragon ball z",
							    "what is the first step in the evolution of the eye",
							    "where is gall bladder situated in human body",
							    "what is the main mineral in lithium batteries",
							    "who is the president of usa right now",
							    "where do the greasers live in the outsiders",
							    "panda is a national animal of which country",
							    "what is the name of manchester united stadium",
							]
							A__							=       tokenizer(UpperCAmelCase__		)
							self.assertIsNotNone(UpperCAmelCase__		)










import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""linear"""
	lowerCAmelCase	:   int				=	"""cosine"""
	lowerCAmelCase	:   Dict				=	"""cosine_with_restarts"""
	lowerCAmelCase	:   Optional[Any]				=	"""polynomial"""
	lowerCAmelCase	:   Dict				=	"""constant"""
	lowerCAmelCase	:   Any				=	"""constant_with_warmup"""
	lowerCAmelCase	:   Union[str, Any]				=	"""piecewise_constant"""





def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						return LambdaLR(_A			,							lambda _A    : 1			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int = -1    )->       Optional[Any]:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1.0			,							_A    )    )
												return 1.0

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				str			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						A__							=       {}
						A__							=       step_rules.split(","    )
						for rule_str in rule_list[:-1]:
												A__						,       A__							=       rule_str.split(":"    )
												A__							=       int(_A    )
												A__							=       float(_A    )
												A__							=       value
						A__							=       float(rule_list[-1]    )

						def create_rules_function(_A			:				Any			,							_A			:				Optional[int]    ):
												def rule_func(_A			:				int    ) -> float:
																		A__							=       sorted(rules_dict.keys()    )
																		for i, sorted_step in enumerate(_A    ):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						A__							=       create_rules_function(_A			,							_A    )

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str			,							_A			:				str=-1    )->       Tuple:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												return max(
												    0.0			,							float(num_training_steps - current_step    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				float = 0.5			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * float(_A    ) * 2.0 * progress    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				int = 1			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												if progress >= 1.0:
																		return 0.0
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * ((float(_A    ) * progress) % 1.0)    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Tuple=1E-7			,							_A			:				Dict=1.0			,							_A			:				Union[str, Any]=-1    )->       Any:





						"""simple docstring"""





						A__							=       optimizer.defaults["lr"]
						if not (lr_init > lr_end):
												raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""    )

						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		A__							=       lr_init - lr_end
																		A__							=       num_training_steps - num_warmup_steps
																		A__							=       1 - (current_step - num_warmup_steps) / decay_steps
																		A__							=       lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A			,							_A			,							_A    )


UpperCAmelCase_							:      Any      =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def  UpperCamelCase						(       _A			:				Union[str, SchedulerType]			,							_A			:				Optimizer			,							_A			:				Optional[str] = None			,							_A			:				Optional[int] = None			,							_A			:				Optional[int] = None			,							_A			:				int = 1			,							_A			:				float = 1.0			,							_A			:				int = -1			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       SchedulerType(_A    )
						A__							=       TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_A			,							last_epoch=_A    )

						if name == SchedulerType.PIECEWISE_CONSTANT:
												return schedule_func(_A			,							step_rules=_A			,							last_epoch=_A    )

						# All other schedulers require `num_warmup_steps`
						if num_warmup_steps is None:
												raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""    )

						if name == SchedulerType.CONSTANT_WITH_WARMUP:
												return schedule_func(_A			,							num_warmup_steps=_A			,							last_epoch=_A    )

						# All other schedulers require `num_training_steps`
						if num_training_steps is None:
												raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""    )

						if name == SchedulerType.COSINE_WITH_RESTARTS:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							num_cycles=_A			,							last_epoch=_A			,							)

						if name == SchedulerType.POLYNOMIAL:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							power=_A			,							last_epoch=_A			,							)

						return schedule_func(
						    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							last_epoch=_A    )





