
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_UpperCAmelCase      :				Tuple          = logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_UpperCAmelCase      :				Dict          = {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_UpperCAmelCase      :				Optional[Any]          = {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_UpperCAmelCase      :				List[Any]          = sorted(arg_to_scheduler.keys())
_UpperCAmelCase      :				List[Any]          = "{" + ", ".join(arg_to_scheduler_choices) + "}"


class        lowercase      (	pl.LightningModule     ):



		def __init__(				self       ,   A_       ,   A_=None       ,   A_="base"       ,   A_=None       ,   A_=None       ,   A_=None       ,   **A_       ,   )						->      str:
				"""simple docstring"""
				super().__init__()
				# TODO: move to self.save_hyperparameters()
				# self.save_hyperparameters()
				# can also expand arguments into trainer signature for easier reading

				self.save_hyperparameters(A_ )
				UpperCamelCase						=  0
				UpperCamelCase						=  Path(self.hparams.output_dir )
				UpperCamelCase						=  self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
						UpperCamelCase						=  AutoConfig.from_pretrained(
						    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path       ,   **({'num_labels': num_labels} if num_labels is not None else {})       ,   cache_dir=A_       ,   **A_       ,   )
				else:
						UpperCamelCase						=  config

				UpperCamelCase						=  ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
				for p in extra_model_params:
						if getattr(self.hparams       ,   A_       ,   A_ ):
								assert hasattr(self.config       ,   A_ ), F'''model config doesn\'t have a `{p}` attribute'''
								setattr(self.config       ,   A_       ,   getattr(self.hparams       ,   A_ ) )

				if tokenizer is None:
						UpperCamelCase						=  AutoTokenizer.from_pretrained(
						    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,   cache_dir=A_       ,   )
				else:
						UpperCamelCase						=  tokenizer
				UpperCamelCase						=  MODEL_MODES[mode]
				if model is None:
						UpperCamelCase						=  self.model_type.from_pretrained(
						    self.hparams.model_name_or_path       ,   from_tf=bool('.ckpt' in self.hparams.model_name_or_path )       ,   config=self.config       ,   cache_dir=A_       ,   )
				else:
						UpperCamelCase						=  model



		def __UpperCamelCase				(				self       ,   *A_       ,   **A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_type.from_pretrained(*A_       ,   **A_ )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  arg_to_scheduler[self.hparams.lr_scheduler]
				UpperCamelCase						=  get_schedule_func(
				    self.opt       ,   num_warmup_steps=self.hparams.warmup_steps       ,   num_training_steps=self.total_steps() )
				UpperCamelCase						=  {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
				return scheduler



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model
				UpperCamelCase						=  ['bias', 'LayerNorm.weight']
				UpperCamelCase						=  [
				    {
				        'params': [
				            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
				        ],  # check this named paramters
				        'weight_decay': self.hparams.weight_decay,
				    },
				    {
				        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
				        'weight_decay': 0.0,
				    },
				]
				if self.hparams.adafactor:
						UpperCamelCase						=  Adafactor(
						    A_       ,   lr=self.hparams.learning_rate       ,   scale_parameter=A_       ,   relative_step=A_ )

				else:
						UpperCamelCase						=  AdamW(
						    A_       ,   lr=self.hparams.learning_rate       ,   eps=self.hparams.adam_epsilon )
				UpperCamelCase						=  optimizer

				UpperCamelCase						=  self.get_lr_scheduler()

				return [optimizer], [scheduler]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				return self.validation_step(A_       ,   A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				return self.validation_end(A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  max(1       ,   self.hparams.gpus )  # TODO: consider num_tpu_cores
				UpperCamelCase						=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
				return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



		def __UpperCamelCase				(				self       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				if stage == "test":
						UpperCamelCase						=  len(self.test_dataloader().dataset )
				else:
						UpperCamelCase						=  self.get_dataloader('train'       ,   self.hparams.train_batch_size       ,   shuffle=A_ )
						UpperCamelCase						=  len(self.train_dataloader().dataset )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ = False )						->      List[str]:
				"""simple docstring"""
				raise NotImplementedError('You must implement this for your task' )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				return self.train_loader



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				return self.get_dataloader('dev'       ,   self.hparams.eval_batch_size       ,   shuffle=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				return self.get_dataloader('test'       ,   self.hparams.eval_batch_size       ,   shuffle=A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				return os.path.join(
				    self.hparams.data_dir       ,   'cached_{}_{}_{}'.format(
				        A_       ,   list(filter(A_       ,   self.hparams.model_name_or_path.split('/' ) ) ).pop()       ,   str(self.hparams.max_seq_length )       ,   )       ,   )



		@pl.utilities.rank_zero_only
		def __UpperCamelCase				(				self       ,   A_ )						->      None:
				"""simple docstring"""
				UpperCamelCase						=  self.output_dir.joinpath('best_tfmr' )
				UpperCamelCase						=  self.step_count
				self.model.save_pretrained(A_ )
				self.tokenizer.save_pretrained(A_ )



		@staticmethod
		def __UpperCamelCase				(				A_       ,   A_ )						->      Any:
				"""simple docstring"""
				parser.add_argument(
				    '--model_name_or_path'       ,   default=A_       ,   type=A_       ,   required=A_       ,   help='Path to pretrained model or model identifier from huggingface.co/models'       ,   )
				parser.add_argument(
				    '--config_name'       ,   default=''       ,   type=A_       ,   help='Pretrained config name or path if not the same as model_name' )
				parser.add_argument(
				    '--tokenizer_name'       ,   default=A_       ,   type=A_       ,   help='Pretrained tokenizer name or path if not the same as model_name'       ,   )
				parser.add_argument(
				    '--cache_dir'       ,   default=str(Path(A_ ).parent / 'test_run' / 'cache' )       ,   type=A_       ,   help='Where do you want to store the pre-trained models downloaded from huggingface.co'       ,   )
				parser.add_argument(
				    '--encoder_layerdrop'       ,   type=A_       ,   help='Encoder layer dropout probability (Optional). Goes into model.config'       ,   )
				parser.add_argument(
				    '--decoder_layerdrop'       ,   type=A_       ,   help='Decoder layer dropout probability (Optional). Goes into model.config'       ,   )
				parser.add_argument(
				    '--dropout'       ,   type=A_       ,   help='Dropout probability (Optional). Goes into model.config'       ,   )
				parser.add_argument(
				    '--attention_dropout'       ,   type=A_       ,   help='Attention dropout probability (Optional). Goes into model.config'       ,   )
				parser.add_argument('--learning_rate'       ,   default=5e-5       ,   type=A_       ,   help='The initial learning rate for Adam.' )
				parser.add_argument(
				    '--lr_scheduler'       ,   default='linear'       ,   choices=A_       ,   metavar=A_       ,   type=A_       ,   help='Learning rate scheduler'       ,   )
				parser.add_argument('--weight_decay'       ,   default=0.0       ,   type=A_       ,   help='Weight decay if we apply some.' )
				parser.add_argument('--adam_epsilon'       ,   default=1e-8       ,   type=A_       ,   help='Epsilon for Adam optimizer.' )
				parser.add_argument('--warmup_steps'       ,   default=0       ,   type=A_       ,   help='Linear warmup over warmup_steps.' )
				parser.add_argument('--num_workers'       ,   default=4       ,   type=A_       ,   help='kwarg passed to DataLoader' )
				parser.add_argument('--num_train_epochs'       ,   dest='max_epochs'       ,   default=3       ,   type=A_ )
				parser.add_argument('--train_batch_size'       ,   default=32       ,   type=A_ )
				parser.add_argument('--eval_batch_size'       ,   default=32       ,   type=A_ )
				parser.add_argument('--adafactor'       ,   action='store_true' )






class        lowercase      (	pl.Callback     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				if (
				    trainer.is_global_zero and trainer.global_rank == 0
				):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
						pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class        lowercase      (	pl.Callback     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				# print(pl_module.model.rag)
				for name, param in pl_module.model.rag.named_parameters():
						if param.grad is None:
								print(A_ )






class        lowercase      (	pl.Callback     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  trainer.lr_schedulers[0]['scheduler']
				UpperCamelCase						=  {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
				pl_module.logger.log_metrics(A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Dict:
				"""simple docstring"""
				rank_zero_info('***** Validation results *****' )
				UpperCamelCase						=  trainer.callback_metrics
				# Log results
				for key in sorted(A_ ):
						if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(A_       ,   str(metrics[key] ) ) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				rank_zero_info('***** Test results *****' )
				UpperCamelCase						=  trainer.callback_metrics
				# Log and save results to file
				UpperCamelCase						=  os.path.join(pl_module.hparams.output_dir       ,   'test_results.txt' )
				with open(A_       ,   'w' ) as writer:
						for key in sorted(A_ ):
								if key not in ["log", "progress_bar"]:
										rank_zero_info('{} = {}\n'.format(A_       ,   str(metrics[key] ) ) )
										writer.write('{} = {}\n'.format(A_       ,   str(metrics[key] ) ) )






def    A (      lowercase					, lowercase				)     ->						None:
		'''simple docstring'''

		parser.add_argument(
		    '--output_dir'					, default=str(Path(lowercase				).parent / 'test_run' / 'model_checkpoints'				)					, type=lowercase					, help='The output directory where the model predictions and checkpoints will be written.'					, )
		parser.add_argument(
		    '--fp16'					, action='store_true'					, help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'					, )

		parser.add_argument(
		    '--fp16_opt_level'					, type=lowercase					, default='O2'					, help=(
		        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
		        'See details at https://nvidia.github.io/apex/amp.html'
		    )					, )
		parser.add_argument('--n_tpu_cores'					, dest='tpu_cores'					, type=lowercase				)
		parser.add_argument('--max_grad_norm'					, dest='gradient_clip_val'					, default=1.0					, type=lowercase					, help='Max gradient norm'				)
		parser.add_argument('--do_train'					, action='store_true'					, help='Whether to run training.'				)
		parser.add_argument('--do_predict'					, action='store_true'					, help='Whether to run predictions on the test set.'				)
		parser.add_argument(
		    '--gradient_accumulation_steps'					, dest='accumulate_grad_batches'					, type=lowercase					, default=1					, help='Number of updates steps to accumulate before performing a backward/update pass.'					, )
		parser.add_argument('--seed'					, type=lowercase					, default=42					, help='random seed for initialization'				)
		parser.add_argument(
		    '--data_dir'					, default=str(Path(lowercase				).parent / 'test_run' / 'dummy-train-data'				)					, type=lowercase					, help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					, )


def    A (      lowercase					, lowercase					, lowercase=None					, lowercase=True					, lowercase=[]					, lowercase=None					, lowercase=None					, **lowercase					, )     ->						Dict:
		'''simple docstring'''

		pl.seed_everything(args.seed				)

		# init model
		UpperCamelCase						=  Path(model.hparams.output_dir				)
		odir.mkdir(exist_ok=lowercase				)

		# add custom checkpoints
		if checkpoint_callback is None:
				UpperCamelCase						=  pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir					, prefix='checkpoint'					, monitor='val_loss'					, mode='min'					, save_top_k=1				)
		if early_stopping_callback:
				extra_callbacks.append(lowercase				)
		if logging_callback is None:
				UpperCamelCase						=  LoggingCallback()

		UpperCamelCase						=  {}

		if args.fpaa:
				UpperCamelCase						=  16

		if args.gpus > 1:
				UpperCamelCase						=  'auto'
				UpperCamelCase						=  'ddp'

		UpperCamelCase						=  args.accumulate_grad_batches
		UpperCamelCase						=  None
		UpperCamelCase						=  'auto'

		UpperCamelCase						=  pl.Trainer.from_argparse_args(
		    lowercase					, weights_summary=lowercase					, callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					, logger=lowercase					, val_check_interval=1					, num_sanity_val_steps=2					, **lowercase					, )

		if args.do_train:
				trainer.fit(lowercase				)

		else:
				print('RAG modeling tests with new set functions successfuly executed!'				)
		return trainer


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
