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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin




@flax.struct.dataclass
class        UpperCAmelCase_			:


  '''simple docstring'''







  a__           =		None
  a__           =		None
  a__           =		None  # sigma(t_i)
  @classmethod
  def 							_lowercase							( cls			: List[str]    )			->	Dict:







    """simple docstring"""






    return cls()







@dataclass
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		42
  a__           =		42
  a__           =		42







class        UpperCAmelCase_			( _A  ,       _A     ):


  '''simple docstring'''







  @property
  def 							_lowercase							( self			: Any    )			->	Tuple:







    """simple docstring"""






    return True
  @register_to_config
  def __init__( self			: Dict			,							UpperCamelCase__			: float = 0.02			,							UpperCamelCase__			: float = 100			,							UpperCamelCase__			: float = 1.007			,							UpperCamelCase__			: float = 80			,							UpperCamelCase__			: float = 0.05			,							UpperCamelCase__			: float = 50			,							)			->	List[Any]:







    """simple docstring"""






    pass
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    return KarrasVeSchedulerState.create()
  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: KarrasVeSchedulerState			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Tuple = ()    )			->	KarrasVeSchedulerState:







    """simple docstring"""






    __magic_name__				     =       jnp.arange(0			,							UpperCamelCase__    )[::-1].copy()
    __magic_name__				     =       [
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in timesteps
    ]

    return state.replace(
        num_inference_steps=UpperCamelCase__			,							schedule=jnp.array(UpperCamelCase__			,							dtype=jnp.floataa    )			,							timesteps=UpperCamelCase__			,							)
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: KarrasVeSchedulerState			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: float			,							UpperCamelCase__			: random.KeyArray			,							)			->	Tuple[jnp.ndarray, float]:







    """simple docstring"""






    if self.config.s_min <= sigma <= self.config.s_max:
      __magic_name__				     =       min(self.config.s_churn / state.num_inference_steps			,							2**0.5 - 1    )
    else:
      __magic_name__				     =       0

    # sample eps ~ N(0, S_noise^2 * I)
    __magic_name__				     =       random.split(UpperCamelCase__			,							num=1    )
    __magic_name__				     =       self.config.s_noise * random.normal(key=UpperCamelCase__			,							shape=sample.shape    )
    __magic_name__				     =       sigma + gamma * sigma
    __magic_name__				     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: KarrasVeSchedulerState			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: float			,							UpperCamelCase__			: float			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: bool = True			,							)			->	Union[FlaxKarrasVeOutput, Tuple]:







    """simple docstring"""






    __magic_name__				     =       sample_hat + sigma_hat * model_output
    __magic_name__				     =       (sample_hat - pred_original_sample) / sigma_hat
    __magic_name__				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
      return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=UpperCamelCase__			,							derivative=UpperCamelCase__			,							state=UpperCamelCase__    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: KarrasVeSchedulerState			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: float			,							UpperCamelCase__			: float			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: jnp.ndarray			,							UpperCamelCase__			: bool = True			,							)			->	Union[FlaxKarrasVeOutput, Tuple]:







    """simple docstring"""






    __magic_name__				     =       sample_prev + sigma_prev * model_output
    __magic_name__				     =       (sample_prev - pred_original_sample) / sigma_prev
    __magic_name__				     =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
      return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=UpperCamelCase__			,							derivative=UpperCamelCase__			,							state=UpperCamelCase__    )





  def 							_lowercase							( self			: str			,							UpperCamelCase__			: KarrasVeSchedulerState			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Any    )			->	str:







    """simple docstring"""






    raise NotImplementedError()



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]    ):
        __magic_name__				     =       model_result["""result"""][batch_size][sequence_length]
        self.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sgugger/tiny-distilbert-classification"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: List[Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """patrickvonplaten/t5-tiny-random"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""    )    ) == 0			,							"""Cannot do xla on CPU."""    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							save_to_csv=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							inference_time_csv_file=os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )			,							inference_memory_csv_file=os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )			,							env_info_csv_file=os.path.join(UpperCamelCase__			,							"""env.csv"""    )			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      benchmark.run()
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""env.csv"""    )    ).exists()    )





  def 							_lowercase							( self			: int    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(UpperCamelCase__			: Dict    ):
      self.assertTrue(hasattr(UpperCamelCase__			,							"""sequential"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""cumulative"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""current"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""total"""    )    )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							log_filename=os.path.join(UpperCamelCase__			,							"""log.txt"""    )			,							log_print=UpperCamelCase__			,							trace_memory_line_by_line=UpperCamelCase__			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      __magic_name__				     =       benchmark.run()
      _check_summary_is_not_empty(result.inference_summary    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""    )    ).exists()    )

