'''simple docstring'''






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def    __UpperCAmelCase			(  a_:       Dict,					a_:       str=0.9_99,					a_:       Union[str, Any]="cosine",					):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(a_:       Union[str, Any]							):
         return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

   elif alpha_transform_type == "exp":

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

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

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






class     A__							(       UpperCamelCase					,		UpperCamelCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Dict    				=     [e.name for e in KarrasDiffusionSchedulers]
   UpperCamelCase_       :						List[str]    				=     2




   @register_to_config
   def __init__( self     :		Tuple						,     lowerCAmelCase__     :		int = 1_0_0_0						,     lowerCAmelCase__     :		float = 0.0_0085						,     lowerCAmelCase__     :		float = 0.012						,     lowerCAmelCase__     :		str = "linear"						,     lowerCAmelCase__     :		Optional[Union[np.ndarray, List[float]]] = None						,     lowerCAmelCase__     :		str = "epsilon"						,     lowerCAmelCase__     :		Optional[bool] = False						,     lowerCAmelCase__     :		Optional[bool] = False						,     lowerCAmelCase__     :		float = 1.0						,     lowerCAmelCase__     :		str = "linspace"						,     lowerCAmelCase__     :		int = 0						,     )       ->    List[str]:







      """simple docstring"""




      if trained_betas is not None:
         _UpperCAmelCase							:	int							=   torch.tensor(lowerCAmelCase__						,     dtype=torch.floataa			)
      elif beta_schedule == "linear":
         _UpperCAmelCase							:	Optional[int]							=   torch.linspace(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     dtype=torch.floataa			)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _UpperCAmelCase							:	Dict							=   (
             torch.linspace(beta_start**0.5						,     beta_end**0.5						,     lowerCAmelCase__						,     dtype=torch.floataa			) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _UpperCAmelCase							:	int							=   betas_for_alpha_bar(lowerCAmelCase__						,     alpha_transform_type="cosine"			)
      elif beta_schedule == "exp":
         _UpperCAmelCase							:	Tuple							=   betas_for_alpha_bar(lowerCAmelCase__						,     alpha_transform_type="exp"			)
      else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""			)

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

      #  set all values
      self.set_timesteps(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			)
      _UpperCAmelCase							:	Tuple							=   use_karras_sigmas




   def 			_lowerCAmelCase		( self     :		Any						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Any=None			)       ->    Tuple:







      """simple docstring"""




      if schedule_timesteps is None:
         _UpperCAmelCase							:	List[Any]							=   self.timesteps

      _UpperCAmelCase							:	Union[str, Any]							=   (schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter			) == 0:
         _UpperCAmelCase							:	List[Any]							=   1 if len(lowerCAmelCase__			) > 1 else 0
      else:
         _UpperCAmelCase							:	List[str]							=   timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__			) else timestep
         _UpperCAmelCase							:	Tuple							=   self._index_counter[timestep_int]

      return indices[pos].item()




   @property
   def 			_lowerCAmelCase		( self     :		Any			)       ->    Any:







      """simple docstring"""




      if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

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




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		torch.FloatTensor						,     lowerCAmelCase__     :		Union[float, torch.FloatTensor]						,     )       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.index_for_timestep(lowerCAmelCase__			)

      _UpperCAmelCase							:	Optional[Any]							=   self.sigmas[step_index]
      _UpperCAmelCase							:	Union[str, Any]							=   sample / ((sigma**2 + 1) ** 0.5)
      return sample




   def 			_lowerCAmelCase		( self     :		Any						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Union[str, torch.device] = None						,     lowerCAmelCase__     :		Optional[int] = None						,     )       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   num_inference_steps

      _UpperCAmelCase							:	List[Any]							=   num_train_timesteps or self.config.num_train_timesteps

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

      _UpperCAmelCase							:	Any							=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
      _UpperCAmelCase							:	Optional[int]							=   np.log(lowerCAmelCase__			)
      _UpperCAmelCase							:	Optional[int]							=   np.interp(lowerCAmelCase__						,     np.arange(0						,     len(lowerCAmelCase__			)			)						,     lowerCAmelCase__			)

      if self.config.use_karras_sigmas:
         _UpperCAmelCase							:	int							=   self._convert_to_karras(in_sigmas=lowerCAmelCase__						,     num_inference_steps=self.num_inference_steps			)
         _UpperCAmelCase							:	Any							=   np.array([self._sigma_to_t(lowerCAmelCase__						,     lowerCAmelCase__			) for sigma in sigmas]			)

      _UpperCAmelCase							:	List[str]							=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
      _UpperCAmelCase							:	Any							=   torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__			)
      _UpperCAmelCase							:	str							=   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2			), sigmas[-1:]]			)

      _UpperCAmelCase							:	Optional[int]							=   torch.from_numpy(lowerCAmelCase__			)
      _UpperCAmelCase							:	int							=   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2			)]			)

      if str(lowerCAmelCase__			).startswith("mps"			):
         # mps does not support float64
         _UpperCAmelCase							:	str							=   timesteps.to(lowerCAmelCase__						,     dtype=torch.floataa			)
      else:
         _UpperCAmelCase							:	Optional[int]							=   timesteps.to(device=lowerCAmelCase__			)

      # empty dt and derivative
      _UpperCAmelCase							:	List[Any]							=   None
      _UpperCAmelCase							:	Tuple							=   None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      _UpperCAmelCase							:	int							=   defaultdict(lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		List[Any]			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   np.log(lowerCAmelCase__			)

      # get distribution
      _UpperCAmelCase							:	str							=   log_sigma - log_sigmas[:, np.newaxis]

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

      _UpperCAmelCase							:	Optional[Any]							=   log_sigmas[low_idx]
      _UpperCAmelCase							:	Union[str, Any]							=   log_sigmas[high_idx]

      # interpolate sigmas
      _UpperCAmelCase							:	int							=   (low - log_sigma) / (low - high)
      _UpperCAmelCase							:	Any							=   np.clip(lowerCAmelCase__						,     0						,     1			)

      # transform interpolation to time range
      _UpperCAmelCase							:	Tuple							=   (1 - w) * low_idx + w * high_idx
      _UpperCAmelCase							:	List[str]							=   t.reshape(sigma.shape			)
      return t




   def 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		torch.FloatTensor						,     lowerCAmelCase__     :		str			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   in_sigmas[-1].item()
      _UpperCAmelCase							:	List[Any]							=   in_sigmas[0].item()

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




   @property
   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Dict:







      """simple docstring"""




      return self.dt is None




   def 			_lowerCAmelCase		( self     :		List[Any]						,     lowerCAmelCase__     :		Union[torch.FloatTensor, np.ndarray]						,     lowerCAmelCase__     :		Union[float, torch.FloatTensor]						,     lowerCAmelCase__     :		Union[torch.FloatTensor, np.ndarray]						,     lowerCAmelCase__     :		bool = True						,     )       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   self.index_for_timestep(lowerCAmelCase__			)

      # advance index counter by 1
      _UpperCAmelCase							:	Tuple							=   timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__			) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
         _UpperCAmelCase							:	Tuple							=   self.sigmas[step_index]
         _UpperCAmelCase							:	Any							=   self.sigmas[step_index + 1]
      else:
         # 2nd order / Heun's method
         _UpperCAmelCase							:	int							=   self.sigmas[step_index - 1]
         _UpperCAmelCase							:	Optional[Any]							=   self.sigmas[step_index]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      _UpperCAmelCase							:	Union[str, Any]							=   0
      _UpperCAmelCase							:	int							=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
         _UpperCAmelCase							:	List[str]							=   sigma_hat if self.state_in_first_order else sigma_next
         _UpperCAmelCase							:	Tuple							=   sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         _UpperCAmelCase							:	Optional[Any]							=   sigma_hat if self.state_in_first_order else sigma_next
         _UpperCAmelCase							:	List[Any]							=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
      elif self.config.prediction_type == "sample":
         _UpperCAmelCase							:	Dict							=   model_output
      else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""			)

      if self.config.clip_sample:
         _UpperCAmelCase							:	int							=   pred_original_sample.clamp(
             -self.config.clip_sample_range						,     self.config.clip_sample_range			)

      if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         _UpperCAmelCase							:	Union[str, Any]							=   (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         _UpperCAmelCase							:	List[str]							=   sigma_next - sigma_hat

         # store for 2nd order step
         _UpperCAmelCase							:	int							=   derivative
         _UpperCAmelCase							:	List[Any]							=   dt
         _UpperCAmelCase							:	Optional[Any]							=   sample
      else:
         # 2. 2nd order / Heun's method
         _UpperCAmelCase							:	str							=   (sample - pred_original_sample) / sigma_next
         _UpperCAmelCase							:	Tuple							=   (self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         _UpperCAmelCase							:	Optional[Any]							=   self.dt
         _UpperCAmelCase							:	Any							=   self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         _UpperCAmelCase							:	Any							=   None
         _UpperCAmelCase							:	Union[str, Any]							=   None
         _UpperCAmelCase							:	List[Any]							=   None

      _UpperCAmelCase							:	Optional[int]							=   sample + derivative * dt

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		Dict						,     lowerCAmelCase__     :		torch.FloatTensor						,     lowerCAmelCase__     :		torch.FloatTensor						,     lowerCAmelCase__     :		torch.FloatTensor						,     )       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   self.sigmas.to(device=original_samples.device						,     dtype=original_samples.dtype			)
      if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__			):
         # mps does not support float64
         _UpperCAmelCase							:	Any							=   self.timesteps.to(original_samples.device						,     dtype=torch.floataa			)
         _UpperCAmelCase							:	Tuple							=   timesteps.to(original_samples.device						,     dtype=torch.floataa			)
      else:
         _UpperCAmelCase							:	Dict							=   self.timesteps.to(original_samples.device			)
         _UpperCAmelCase							:	List[Any]							=   timesteps.to(original_samples.device			)

      _UpperCAmelCase							:	Union[str, Any]							=   [self.index_for_timestep(lowerCAmelCase__						,     lowerCAmelCase__			) for t in timesteps]

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

      _UpperCAmelCase							:	Tuple							=   original_samples + noise * sigma
      return noisy_samples




   def __len__( self     :		Dict			)       ->    Tuple:







      """simple docstring"""




      return self.config.num_train_timesteps

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
			from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    List[Any]			,  lowercase	:    Dict	):


				'''simple docstring'''


				for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['bs']			,  model_result['ss']	):
										_snake_case		=			model_result['result'][batch_size][sequence_length]
										self.assertIsNotNone(lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			'sgugger/tiny-distilbert-classification'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  only_pretrain_model=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  torchscript=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	@unittest.skipIf(torch_device == 'cpu'			,  'Cant do half precision'	)
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  fpaa=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				# set architectures equal to `None`
				_snake_case		=			None
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	@unittest.skipIf(torch_device == 'cpu'			,  'Can\'t do half precision'	)
	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  fpaa=lowercase			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  save_to_csv=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  inference_time_csv_file=os.path.join(lowercase			,  'inf_time.csv'	)			,  train_memory_csv_file=os.path.join(lowercase			,  'train_mem.csv'	)			,  inference_memory_csv_file=os.path.join(lowercase			,  'inf_mem.csv'	)			,  train_time_csv_file=os.path.join(lowercase			,  'train_time.csv'	)			,  env_info_csv_file=os.path.join(lowercase			,  'env.csv'	)			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							benchmark.run()
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'env.csv'	)	).exists()	)





	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(lowercase	:    Optional[Any]	):
							self.assertTrue(hasattr(lowercase			,  'sequential'	)	)
							self.assertTrue(hasattr(lowercase			,  'cumulative'	)	)
							self.assertTrue(hasattr(lowercase			,  'current'	)	)
							self.assertTrue(hasattr(lowercase			,  'total'	)	)

				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  log_filename=os.path.join(lowercase			,  'log.txt'	)			,  log_print=lowercase			,  trace_memory_line_by_line=lowercase			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							_snake_case		=			benchmark.run()
							_check_summary_is_not_empty(result.inference_summary	)
							_check_summary_is_not_empty(result.train_summary	)
							self.assertTrue(Path(os.path.join(lowercase			,  'log.txt'	)	).exists()	)