
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE_         =  logging.getLogger(__name__)




def       lowercase__  (   )	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   argparse.ArgumentParser(description='PyTorch Transformer Language Model'	)
    parser.add_argument('--model_name'	,			type=lowerCAmelCase	,			default='transfo-xl-wt103'	,			help='pretrained model name'	)
    parser.add_argument(
        '--split'	,			type=lowerCAmelCase	,			default='test'	,			choices=['all', 'valid', 'test']	,			help='which split to evaluate'	)
    parser.add_argument('--batch_size'	,			type=lowerCAmelCase	,			default=10	,			help='batch size'	)
    parser.add_argument('--tgt_len'	,			type=lowerCAmelCase	,			default=128	,			help='number of tokens to predict'	)
    parser.add_argument('--ext_len'	,			type=lowerCAmelCase	,			default=0	,			help='length of the extended context'	)
    parser.add_argument('--mem_len'	,			type=lowerCAmelCase	,			default=1_600	,			help='length of the retained previous heads'	)
    parser.add_argument('--clamp_len'	,			type=lowerCAmelCase	,			default=1_000	,			help='max positional embedding index'	)
    parser.add_argument('--no_cuda'	,			action='store_true'	,			help='Do not use CUDA even though CUA is available'	)
    parser.add_argument('--work_dir'	,			type=lowerCAmelCase	,			required=lowerCAmelCase	,			help='path to the work_dir'	)
    parser.add_argument('--no_log'	,			action='store_true'	,			help='do not log the eval result'	)
    parser.add_argument('--same_length'	,			action='store_true'	,			help='set same length attention with masking'	)
    parser.add_argument('--server_ip'	,			type=lowerCAmelCase	,			default=''	,			help='Can be used for distant debugging.'	)
    parser.add_argument('--server_port'	,			type=lowerCAmelCase	,			default=''	,			help='Can be used for distant debugging.'	)
    UpperCAmelCase       =   parser.parse_args()
    assert args.ext_len >= 0, "extended context length must be non-negative"

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print('Waiting for debugger attach'	)
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)	,			redirect_output=lowerCAmelCase	)
        ptvsd.wait_for_attach()

    UpperCAmelCase       =   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'	)
    logger.info('device: {}'.format(lowerCAmelCase	)	)

    # Load a pre-processed dataset
    # You can also build the corpus yourself using TransfoXLCorpus methods
    # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
    # and tokenizing the dataset
    # The pre-processed corpus is a convertion (using the conversion script )
    UpperCAmelCase       =   TransfoXLCorpus.from_pretrained(args.model_name	)

    UpperCAmelCase       =   corpus.get_iterator('valid'	,			args.batch_size	,			args.tgt_len	,			device=lowerCAmelCase	,			ext_len=args.ext_len	)
    UpperCAmelCase       =   corpus.get_iterator('test'	,			args.batch_size	,			args.tgt_len	,			device=lowerCAmelCase	,			ext_len=args.ext_len	)

    # Load a pre-trained model
    UpperCAmelCase       =   TransfoXLLMHeadModel.from_pretrained(args.model_name	)
    model.to(lowerCAmelCase	)

    logger.info(
        'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
            args.batch_size	,			args.tgt_len	,			args.ext_len	,			args.mem_len	,			args.clamp_len	)	)

    model.reset_memory_length(args.mem_len	)
    if args.clamp_len > 0:
        UpperCAmelCase       =   args.clamp_len
    if args.same_length:
        UpperCAmelCase       =   True

    ###############################################################################
    # Evaluation code
    ###############################################################################
    def evaluate(lowerCAmelCase			:   List[str]	):
        # Turn on evaluation mode which disables dropout.
        model.eval()
        UpperCAmelCase     ,							UpperCAmelCase       =   0, 0.0
        UpperCAmelCase       =   time.time()
        with torch.no_grad():
            UpperCAmelCase       =   None
            for idx, (data, target, seq_len) in enumerate(lowerCAmelCase	):
                UpperCAmelCase       =   model(lowerCAmelCase	,			lm_labels=lowerCAmelCase	,			mems=lowerCAmelCase	)
                UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   ret
                UpperCAmelCase       =   loss.mean()
                total_loss += seq_len * loss.item()
                total_len += seq_len
            UpperCAmelCase       =   time.time() - start_time
        logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(lowerCAmelCase	,			1_000 * total_time / (idx + 1)	)	)
        return total_loss / total_len

    # Run on test data.
    if args.split == "all":
        UpperCAmelCase       =   evaluate(lowerCAmelCase	)
        UpperCAmelCase       =   evaluate(lowerCAmelCase	)
    elif args.split == "valid":
        UpperCAmelCase       =   evaluate(lowerCAmelCase	)
        UpperCAmelCase       =   None
    elif args.split == "test":
        UpperCAmelCase       =   evaluate(lowerCAmelCase	)
        UpperCAmelCase       =   None

    def format_log(lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Any	):
        UpperCAmelCase       =   '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowerCAmelCase	,			lowerCAmelCase	,			math.exp(lowerCAmelCase	)	)
        return log_str

    UpperCAmelCase       =   ''
    if valid_loss is not None:
        log_str += format_log(lowerCAmelCase	,			'valid'	)
    if test_loss is not None:
        log_str += format_log(lowerCAmelCase	,			'test'	)

    logger.info('=' * 100	)
    logger.info(lowerCAmelCase	)
    logger.info('=' * 100	)


if __name__ == "__main__":
   main()





"""simple docstring"""

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class        _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE				:							CommonSchedulerState
      __SCREAMING_SNAKE_CASE				:							jnp.ndarray

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




      @classmethod
      def       a_ (							cls				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   Dict:
          return cls(
              common=lowercase_				,    final_alpha_cumprod=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )

@dataclass
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							DDIMSchedulerState

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		[e.name for e in FlaxKarrasDiffusionSchedulers]

      __SCREAMING_SNAKE_CASE				:							jnp.dtype




      @property
      def       a_ (							self							)  ->   Union[str, Any]:
          return True




      @register_to_config
      def __init__(							self				,    lowercase_ = 1_0_0_0				,    lowercase_ = 0.0_0_0_1				,    lowercase_ = 0.0_2				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = True				,    lowercase_ = 0				,    lowercase_ = "epsilon"				,    lowercase_ = jnp.floataa				,    )  ->   Optional[Any]:
          UpperCAmelCase       =   dtype




      def       a_ (							self				,    lowercase_ = None							)  ->   DDIMSchedulerState:
          if common is None:
              UpperCAmelCase       =   CommonSchedulerState.create(self							)

          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          UpperCAmelCase       =   (
              jnp.array(1.0				,    dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )

          # standard deviation of the initial noise distribution
          UpperCAmelCase       =   jnp.array(1.0				,    dtype=self.dtype							)

          UpperCAmelCase       =   jnp.arange(0				,    self.config.num_train_timesteps							).round()[::-1]

          return DDIMSchedulerState.create(
              common=lowercase_				,    final_alpha_cumprod=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = None							)  ->   jnp.ndarray:
          return sample




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = ()							)  ->   DDIMSchedulerState:
          UpperCAmelCase       =   self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          UpperCAmelCase       =   (jnp.arange(0				,    lowercase_							) * step_ratio).round()[::-1] + self.config.steps_offset

          return state.replace(
              num_inference_steps=lowercase_				,    timesteps=lowercase_				,    )




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   state.common.alphas_cumprod[timestep]
          UpperCAmelCase       =   jnp.where(
              prev_timestep >= 0				,    state.common.alphas_cumprod[prev_timestep]				,    state.final_alpha_cumprod							)
          UpperCAmelCase       =   1 - alpha_prod_t
          UpperCAmelCase       =   1 - alpha_prod_t_prev

          UpperCAmelCase       =   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 0.0				,    lowercase_ = True				,    )  ->   Union[FlaxDDIMSchedulerOutput, 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'							)

          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding

          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"

          # 1. get previous step value (=t-1)
          UpperCAmelCase       =   timestep - self.config.num_train_timesteps // state.num_inference_steps

          UpperCAmelCase       =   state.common.alphas_cumprod
          UpperCAmelCase       =   state.final_alpha_cumprod

          # 2. compute alphas, betas
          UpperCAmelCase       =   alphas_cumprod[timestep]
          UpperCAmelCase       =   jnp.where(prev_timestep >= 0				,    alphas_cumprod[prev_timestep]				,    lowercase_							)

          UpperCAmelCase       =   1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              UpperCAmelCase       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              UpperCAmelCase       =   model_output
          elif self.config.prediction_type == "sample":
              UpperCAmelCase       =   model_output
              UpperCAmelCase       =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase       =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              UpperCAmelCase       =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                  ' `v_prediction`'							)

          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          UpperCAmelCase       =   self._get_variance(lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   eta * variance ** 0.5

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
              return (prev_sample, state)

          return FlaxDDIMSchedulerOutput(prev_sample=lowercase_				,    state=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   jnp.ndarray:
          return add_noise_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   jnp.ndarray:
          return get_velocity_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




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



