
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  _lowerCAmelCase    (    __A    ):
							lowercase_  :  int      =				42
							lowercase_  :  int      =				None





def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__=0.999					,  UpperCamelCase__="cosine"					,  ):


	"""simple docstring"""






	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase__   ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase__   ):
			return math.exp(t * -12.0   )

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

	_UpperCAmelCase			=    []
	for i in range(lowerCAmelCase__   ):
		_UpperCAmelCase			=    i / num_diffusion_timesteps
		_UpperCAmelCase			=    (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__   ) / alpha_bar_fn(lowerCAmelCase__   )					,  lowerCAmelCase__   )   )
	return torch.tensor(lowerCAmelCase__					,  dtype=torch.floataa   )



class  _lowerCAmelCase    (    __A     ,							__A    ):
							lowercase_  :  Tuple      =				1



							@register_to_config
							def __init__(   self			,    a_ = 1000			,    a_ = 0.0001			,    a_ = 0.02			,    a_ = "linear"			,    a_ = None			,    a_ = True			,    a_ = True			,    a_ = 0			,    a_ = "epsilon"			,    a_ = 1.0			,    **a_			,    )				->				List[str]:
								if kwargs.get("set_alpha_to_one"			,    __A ) is not None:
									_UpperCAmelCase			=    (
									    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
									)
									deprecate("set_alpha_to_one"			,    "1.0.0"			,    __A			,    standard_warn=__A )
									_UpperCAmelCase			=    kwargs["set_alpha_to_one"]
								if trained_betas is not None:
									_UpperCAmelCase			=    torch.tensor(__A			,    dtype=torch.floataa )
								elif beta_schedule == "linear":
									_UpperCAmelCase			=    torch.linspace(__A			,    __A			,    __A			,    dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_UpperCAmelCase			=    (
									    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    __A			,    dtype=torch.floataa ) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_UpperCAmelCase			=    betas_for_alpha_bar(__A )
								else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )

								_UpperCAmelCase			=    1.0 - self.betas
								_UpperCAmelCase			=    torch.cumprod(self.alphas			,    dim=0 )

								# At every step in inverted ddim, we are looking into the next alphas_cumprod
								# For the final step, there is no next alphas_cumprod, and the index is out of bounds
								# `set_alpha_to_zero` decides whether we set this parameter simply to zero
								# in this case, self.step() just output the predicted noise
								# or whether we use the final alpha of the "non-previous" one.
								_UpperCAmelCase			=    torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								_UpperCAmelCase			=    1.0

								# setable values
								_UpperCAmelCase			=    None
								_UpperCAmelCase			=    torch.from_numpy(np.arange(0			,    __A ).copy().astype(np.intaa ) )



							def    _a (   self			,    a_			,    a_ = None )				->				Any:
								return sample



							def    _a (   self			,    a_			,    a_ = None )				->				Any:

								if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
									    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
									    f" maximal {self.config.num_train_timesteps} timesteps." )

								_UpperCAmelCase			=    num_inference_steps
								_UpperCAmelCase			=    self.config.num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								_UpperCAmelCase			=    (np.arange(0			,    __A ) * step_ratio).round().copy().astype(np.intaa )
								_UpperCAmelCase			=    torch.from_numpy(__A ).to(__A )
								self.timesteps += self.config.steps_offset



							def    _a (   self			,    a_			,    a_			,    a_			,    a_ = 0.0			,    a_ = False			,    a_ = None			,    a_ = True			,    )				->				Tuple:
								# 1. get previous step value (=t+1)
								_UpperCAmelCase			=    timestep + self.config.num_train_timesteps // self.num_inference_steps

								# 2. compute alphas, betas
								# change original implementation to exactly match noise levels for analogous forward process
								_UpperCAmelCase			=    self.alphas_cumprod[timestep]
								_UpperCAmelCase			=    (
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								_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. Clip or threshold "predicted x_0"
								if self.config.clip_sample:
									_UpperCAmelCase			=    pred_original_sample.clamp(
									    -self.config.clip_sample_range			,    self.config.clip_sample_range )

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								_UpperCAmelCase			=    (1 - alpha_prod_t_prev) ** 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, pred_original_sample)
								return DDIMSchedulerOutput(prev_sample=__A			,    pred_original_sample=__A )



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








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



