
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				CommonSchedulerState

     # setable values
     UpperCAmelCase__							:				jnp.ndarray
     UpperCAmelCase__							:				jnp.ndarray
     UpperCAmelCase__							:				jnp.ndarray
     UpperCAmelCase__							:				Optional[int] 	=						None

     # running values
     UpperCAmelCase__							:				Optional[jnp.ndarray] 	=						None





     @classmethod
     def 						UpperCAmelCase(cls :       Union[str, Any]							,       _A :       CommonSchedulerState							,       _A :       jnp.ndarray							,       _A :       jnp.ndarray							,       _A :       jnp.ndarray  )		->		List[str]:
         return cls(common=_A							,       init_noise_sigma=_A							,       timesteps=_A							,       sigmas=_A  )







@dataclass
class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				LMSDiscreteSchedulerState







class 	lowerCamelCase						(    A_				,					A_    ):
     UpperCAmelCase__							:				str 	=						[e.name for e in FlaxKarrasDiffusionSchedulers]

     UpperCAmelCase__							:				jnp.dtype





     @property
     def 						UpperCAmelCase(self :       Dict  )		->		List[str]:
         return True





     @register_to_config
     def __init__(self :       Dict							,       _A :       int = 1_0_0_0							,       _A :       float = 0.00_01							,       _A :       float = 0.02							,       _A :       str = "linear"							,       _A :       Optional[jnp.ndarray] = None							,       _A :       str = "epsilon"							,       _A :       jnp.dtype = jnp.floataa							,       )		->		List[Any]:
         snake_case						=   dtype





     def 						UpperCAmelCase(self :       str							,       _A :       Optional[CommonSchedulerState] = None  )		->		LMSDiscreteSchedulerState:
         if common is None:
             snake_case						=   CommonSchedulerState.create(self  )

         snake_case						=   jnp.arange(0							,       self.config.num_train_timesteps  ).round()[::-1]
         snake_case						=   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

         # standard deviation of the initial noise distribution
         snake_case						=   sigmas.max()

         return LMSDiscreteSchedulerState.create(
             common=_A							,       init_noise_sigma=_A							,       timesteps=_A							,       sigmas=_A							,       )





     def 						UpperCAmelCase(self :       Dict							,       _A :       LMSDiscreteSchedulerState							,       _A :       jnp.ndarray							,       _A :       int  )		->		jnp.ndarray:
         ((snake_case)	,				)						=   jnp.where(state.timesteps == timestep							,       size=1  )
         snake_case						=   step_index[0]

         snake_case						=   state.sigmas[step_index]
         snake_case						=   sample / ((sigma**2 + 1) ** 0.5)
         return sample





     def 						UpperCAmelCase(self :       Tuple							,       _A :       LMSDiscreteSchedulerState							,       _A :       Optional[Any]							,       _A :       Any							,       _A :       Any  )		->		int:

         def lms_derivative(_A :       List[Any]  ):
             snake_case						=   1.0
             for k in range(_A  ):
                 if current_order == k:
                     continue
                 prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
             return prod

         snake_case						=   integrate.quad(_A							,       state.sigmas[t]							,       state.sigmas[t + 1]							,       epsrel=1E-4  )[0]

         return integrated_coeff





     def 						UpperCAmelCase(self :       Tuple							,       _A :       LMSDiscreteSchedulerState							,       _A :       int							,       _A :       Tuple = ()  )		->		LMSDiscreteSchedulerState:
         snake_case						=   jnp.linspace(self.config.num_train_timesteps - 1							,       0							,       _A							,       dtype=self.dtype  )

         snake_case						=   jnp.floor(_A  ).astype(jnp.intaa  )
         snake_case						=   jnp.ceil(_A  ).astype(jnp.intaa  )

         snake_case						=   jnp.mod(_A							,       1.0  )

         snake_case						=   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
         snake_case						=   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
         snake_case						=   jnp.concatenate([sigmas, jnp.array([0.0]							,       dtype=self.dtype  )]  )

         snake_case						=   timesteps.astype(jnp.intaa  )

         # initial running values
         snake_case						=   jnp.zeros((0,) + shape							,       dtype=self.dtype  )

         return state.replace(
             timesteps=_A							,       sigmas=_A							,       num_inference_steps=_A							,       derivatives=_A							,       )





     def 						UpperCAmelCase(self :       int							,       _A :       LMSDiscreteSchedulerState							,       _A :       jnp.ndarray							,       _A :       int							,       _A :       jnp.ndarray							,       _A :       int = 4							,       _A :       bool = True							,       )		->		Union[FlaxLMSSchedulerOutput, Tuple]:
         if state.num_inference_steps is None:
             raise ValueError(
                 "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"  )

         snake_case						=   state.sigmas[timestep]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             snake_case						=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             snake_case						=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

         # 2. Convert to an ODE derivative
         snake_case						=   (sample - pred_original_sample) / sigma
         snake_case						=   state.replace(derivatives=jnp.append(state.derivatives							,       _A  )  )
         if len(state.derivatives  ) > order:
             snake_case						=   state.replace(derivatives=jnp.delete(state.derivatives							,       0  )  )

         # 3. Compute linear multistep coefficients
         snake_case						=   min(timestep + 1							,       _A  )
         snake_case						=   [self.get_lms_coefficient(_A							,       _A							,       _A							,       _A  ) for curr_order in range(_A  )]

         # 4. Compute previous sample based on the derivatives path
         snake_case						=   sample + sum(
             coeff * derivative for coeff, derivative in zip(_A							,       reversed(state.derivatives  )  )  )

         if not return_dict:
             return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=_A							,       state=_A  )





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       LMSDiscreteSchedulerState							,       _A :       jnp.ndarray							,       _A :       jnp.ndarray							,       _A :       jnp.ndarray							,       )		->		jnp.ndarray:
         snake_case						=   state.sigmas[timesteps].flatten()
         snake_case						=   broadcast_to_shape_from_left(_A							,       noise.shape  )

         snake_case						=   original_samples + noise * sigma

         return noisy_samples





     def __len__(self :       List[Any]  )		->		str:
         return self.config.num_train_timesteps



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir						)
        and os.listdir(training_args.output_dir						)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."						)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,						datefmt="%m/%d/%Y %H:%M:%S"					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1						)					,						training_args.fpaa					,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank						):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"					,						A__						)

    # Prepare Question-Answering task
    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case						=   AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
    snake_case						=   AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

    # Get datasets
    snake_case						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # Data collator
    snake_case						=   DataCollatorWithPadding(A__					,						pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

