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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


UpperCamelCase    				=		logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     snake_case_			(						__A			):
   __A      :						List[str]   =							42
   __A      :						int   =							None



def      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			int=0.999						,      _lowerCamelCase				:			str="cosine"						,      ):



    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_lowerCamelCase				:			Optional[Any]):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

    elif alpha_transform_type == "exp":

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

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

    lowercase__					:  List[Any]          =							[]
    for i in range(a__):
        lowercase__					:  List[str]          =							i / num_diffusion_timesteps
        lowercase__					:  Tuple          =							(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(a__) / alpha_bar_fn(a__)						,      a__))
    return torch.tensor(a__						,      dtype=torch.floataa)



class     snake_case_			(						__A    ,__A			):
   __A      :						str   =							[e.name for e in KarrasDiffusionSchedulers]
   __A      :						Any   =							1






   @register_to_config
   def __init__( self							:	Union[str, Any]      ,		lowercase_							:	Dict = 10_00      ,		lowercase_							:	Tuple = 0.00_01      ,		lowercase_							:	Dict = 0.02      ,		lowercase_							:	int = "linear"      ,		lowercase_							:	Tuple = None      ,		lowercase_							:	Union[str, Any] = "epsilon"      ,		lowercase_							:	Optional[int] = "linear"      ,		lowercase_							:	List[Any] = False      ,		lowercase_							:	List[str] = "linspace"      ,		lowercase_							:	int = 0      ,		)   ->		Union[str, Any]:
       if trained_betas is not None:
           lowercase__					:  str          =							torch.tensor(UpperCAmelCase_      ,		dtype=torch.floataa    )
       elif beta_schedule == "linear":
           lowercase__					:  List[str]          =							torch.linspace(UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		dtype=torch.floataa    )
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowercase__					:  Tuple          =							(
               torch.linspace(beta_start**0.5      ,		beta_end**0.5      ,		UpperCAmelCase_      ,		dtype=torch.floataa    ) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowercase__					:  Optional[Any]          =							betas_for_alpha_bar(UpperCAmelCase_    )
       else:
           raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

       lowercase__					:  Optional[int]          =							1.0 - self.betas
       lowercase__					:  Any          =							torch.cumprod(self.alphas      ,		dim=0    )

       lowercase__					:  List[str]          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
       lowercase__					:  Optional[Any]          =							np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
       lowercase__					:  List[str]          =							torch.from_numpy(UpperCAmelCase_    )

       # setable values
       lowercase__					:  Tuple          =							None
       lowercase__					:  Any          =							np.linspace(0      ,		num_train_timesteps - 1      ,		UpperCAmelCase_      ,		dtype=UpperCAmelCase_    )[::-1].copy()
       lowercase__					:  List[Any]          =							torch.from_numpy(UpperCAmelCase_    )
       lowercase__					:  Optional[int]          =							False
       lowercase__					:  List[str]          =							use_karras_sigmas






   @property
   def  __UpperCamelCase	( self							:	Any    )   ->		Any:
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	str      ,		lowercase_							:	Optional[int]    )   ->		torch.FloatTensor:
       if isinstance(UpperCAmelCase_      ,		torch.Tensor    ):
           lowercase__					:  Dict          =							timestep.to(self.timesteps.device    )
       lowercase__					:  Union[str, Any]          =							(self.timesteps == timestep).nonzero().item()
       lowercase__					:  List[str]          =							self.sigmas[step_index]

       lowercase__					:  Optional[Any]          =							sample / ((sigma**2 + 1) ** 0.5)

       lowercase__					:  Dict          =							True
       return sample






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	int      ,		lowercase_							:	List[Any] = None    )   ->		Tuple:
       lowercase__					:  Any          =							num_inference_steps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           lowercase__					:  Optional[int]          =							np.linspace(0      ,		self.config.num_train_timesteps - 1      ,		UpperCAmelCase_      ,		dtype=UpperCAmelCase_    )[
               ::-1
           ].copy()
       elif self.config.timestep_spacing == "leading":
           lowercase__					:  List[str]          =							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
           lowercase__					:  List[Any]          =							(np.arange(0      ,		UpperCAmelCase_    ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_    )
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           lowercase__					:  int          =							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
           lowercase__					:  str          =							(np.arange(self.config.num_train_timesteps      ,		0      ,		-step_ratio    )).round().copy().astype(UpperCAmelCase_    )
           timesteps -= 1
       else:
           raise ValueError(
               F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

       lowercase__					:  Optional[Any]          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
       lowercase__					:  str          =							np.log(UpperCAmelCase_    )

       if self.config.interpolation_type == "linear":
           lowercase__					:  int          =							np.interp(UpperCAmelCase_      ,		np.arange(0      ,		len(UpperCAmelCase_    )    )      ,		UpperCAmelCase_    )
       elif self.config.interpolation_type == "log_linear":
           lowercase__					:  Any          =							torch.linspace(np.log(sigmas[-1]    )      ,		np.log(sigmas[0]    )      ,		num_inference_steps + 1    ).exp()
       else:
           raise ValueError(
               F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
               " \'linear\' or \'log_linear\'"    )

       if self.use_karras_sigmas:
           lowercase__					:  Tuple          =							self._convert_to_karras(in_sigmas=UpperCAmelCase_      ,		num_inference_steps=self.num_inference_steps    )
           lowercase__					:  Optional[int]          =							np.array([self._sigma_to_t(UpperCAmelCase_      ,		UpperCAmelCase_    ) for sigma in sigmas]    )

       lowercase__					:  List[Any]          =							np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
       lowercase__					:  List[str]          =							torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    )
       if str(UpperCAmelCase_    ).startswith("mps"    ):
           # mps does not support float64
           lowercase__					:  List[str]          =							torch.from_numpy(UpperCAmelCase_    ).to(UpperCAmelCase_      ,		dtype=torch.floataa    )
       else:
           lowercase__					:  Union[str, Any]          =							torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    )






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	str      ,		lowercase_							:	Dict    )   ->		int:
       # get log sigma
       lowercase__					:  int          =							np.log(UpperCAmelCase_    )

       # get distribution
       lowercase__					:  Any          =							log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       lowercase__					:  Optional[int]          =							np.cumsum((dists >= 0)      ,		axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
       lowercase__					:  Dict          =							low_idx + 1

       lowercase__					:  Optional[Any]          =							log_sigmas[low_idx]
       lowercase__					:  Dict          =							log_sigmas[high_idx]

       # interpolate sigmas
       lowercase__					:  Optional[Any]          =							(low - log_sigma) / (low - high)
       lowercase__					:  Optional[Any]          =							np.clip(UpperCAmelCase_      ,		0      ,		1    )

       # transform interpolation to time range
       lowercase__					:  Any          =							(1 - w) * low_idx + w * high_idx
       lowercase__					:  Optional[Any]          =							t.reshape(sigma.shape    )
       return t






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	str    )   ->		torch.FloatTensor:
       lowercase__					:  List[str]          =							in_sigmas[-1].item()
       lowercase__					:  List[str]          =							in_sigmas[0].item()

       lowercase__					:  List[str]          =							7.0  # 7.0 is the value used in the paper
       lowercase__					:  List[Any]          =							np.linspace(0      ,		1      ,		UpperCAmelCase_    )
       lowercase__					:  Optional[int]          =							sigma_min ** (1 / rho)
       lowercase__					:  Optional[Any]          =							sigma_max ** (1 / rho)
       lowercase__					:  List[str]          =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	str      ,		lowercase_							:	List[Any]      ,		lowercase_							:	List[str]      ,		lowercase_							:	Dict = 0.0      ,		lowercase_							:	Tuple = 0.0      ,		lowercase_							:	int = float("inf"    )      ,		lowercase_							:	Tuple = 1.0      ,		lowercase_							:	Optional[int] = None      ,		lowercase_							:	Optional[Any] = True      ,		)   ->		Union[EulerDiscreteSchedulerOutput, Tuple]:

       if (
           isinstance(UpperCAmelCase_      ,		UpperCAmelCase_    )
           or isinstance(UpperCAmelCase_      ,		torch.IntTensor    )
           or isinstance(UpperCAmelCase_      ,		torch.LongTensor    )
       ):
           raise ValueError(
               (
                   "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                   " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                   " one of the `scheduler.timesteps` as a timestep."
               )      ,		)

       if not self.is_scale_input_called:
           logger.warning(
               "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
               "See `StableDiffusionPipeline` for a usage example."    )

       if isinstance(UpperCAmelCase_      ,		torch.Tensor    ):
           lowercase__					:  List[Any]          =							timestep.to(self.timesteps.device    )

       lowercase__					:  List[str]          =							(self.timesteps == timestep).nonzero().item()
       lowercase__					:  List[str]          =							self.sigmas[step_index]

       lowercase__					:  Any          =							min(s_churn / (len(self.sigmas    ) - 1)      ,		2**0.5 - 1    ) if s_tmin <= sigma <= s_tmax else 0.0

       lowercase__					:  Optional[int]          =							randn_tensor(
           model_output.shape      ,		dtype=model_output.dtype      ,		device=model_output.device      ,		generator=UpperCAmelCase_    )

       lowercase__					:  List[str]          =							noise * s_noise
       lowercase__					:  Dict          =							sigma * (gamma + 1)

       if gamma > 0:
           lowercase__					:  int          =							sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       # NOTE: "original_sample" should not be an expected prediction_type but is left in for
       # backwards compatibility
       if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
           lowercase__					:  Optional[int]          =							model_output
       elif self.config.prediction_type == "epsilon":
           lowercase__					:  str          =							sample - sigma_hat * model_output
       elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase__					:  List[str]          =							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
       lowercase__					:  List[str]          =							(sample - pred_original_sample) / sigma_hat

       lowercase__					:  str          =							self.sigmas[step_index + 1] - sigma_hat

       lowercase__					:  Tuple          =							sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_      ,		pred_original_sample=UpperCAmelCase_    )






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	List[str]      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	List[str]      ,		)   ->		torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       lowercase__					:  Union[str, Any]          =							self.sigmas.to(device=original_samples.device      ,		dtype=original_samples.dtype    )
       if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_    ):
           # mps does not support float64
           lowercase__					:  Tuple          =							self.timesteps.to(original_samples.device      ,		dtype=torch.floataa    )
           lowercase__					:  Dict          =							timesteps.to(original_samples.device      ,		dtype=torch.floataa    )
       else:
           lowercase__					:  Any          =							self.timesteps.to(original_samples.device    )
           lowercase__					:  Any          =							timesteps.to(original_samples.device    )

       lowercase__					:  Tuple          =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       lowercase__					:  Optional[Any]          =							sigmas[step_indices].flatten()
       while len(sigma.shape    ) < len(original_samples.shape    ):
           lowercase__					:  Union[str, Any]          =							sigma.unsqueeze(-1    )

       lowercase__					:  Optional[int]          =							original_samples + noise * sigma
       return noisy_samples







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






"""simple docstring"""

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


a__			:						Union[str, Any]							=			torch.cuda.is_available()
a__			:						Union[str, Any]							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						List[Any]							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						str							=			{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def 					__UpperCamelCase (     a__:			Path			,						a__:			list      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )





def 					__UpperCamelCase (     a__:			int      )					->      List[str]:




					"""simple docstring"""



					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(a__			,						f'''{split}.source'''      )			,						a__      )
										_dump_articles(os.path.join(a__			,						f'''{split}.target'''      )			,						a__      )
					return tmp_dir


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    List[str]:
											return cls







						@require_torch_multi_gpu
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Tuple:
											__lowercase        =      {
											    '''label_smoothing''': 0.0,
											    '''early_stopping_patience''': -1,
											    '''train_batch_size''': 1,
											    '''eval_batch_size''': 2,
											    '''max_epochs''': 2,
											    '''alpha_mlm''': 0.2,
											    '''alpha_ce''': 0.8,
											    '''do_predict''': True,
											    '''model_name_or_path''': '''sshleifer/tinier_bart''',
											    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
											    '''val_check_interval''': 0.5,
											}
											default_updates.update(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)

											def convert(UpperCAmelCase_  ,				UpperCAmelCase_				):
																if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																					return ""
																if v is False or v is None:
																					return ""
																if v is True:  # or len(str(v))==0:
																					return f'''--{k}'''
																return f'''--{k}={v}'''

											__lowercase        =      [x for x in (convert(UpperCAmelCase_  ,				UpperCAmelCase_				) for k, v in args_d.items()) if len(UpperCAmelCase_				)]
											__lowercase        =      [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(UpperCAmelCase_  ,				env=self.get_env()				)

											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

											self.assertIn('''test_generations.txt'''  ,				UpperCAmelCase_				)
											self.assertIn('''test_results.txt'''  ,				UpperCAmelCase_				)

											# get the following from the module, (we don't have access to `model` here)
											__lowercase        =      os.path.join(UpperCAmelCase_  ,				'''metrics.json'''				)
											__lowercase        =      '''rouge2'''

											__lowercase        =      load_json(UpperCAmelCase_				)
											# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
											print(UpperCAmelCase_				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)


