

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 SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]			,				lowerCAmelCase					:       List[str]       )				->      Any:






     """simple docstring"""







     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result["""bs"""]			,				model_result["""ss"""]       ):
           __lowerCAmelCase    :     str          =		model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]       )				->      Any:






     """simple docstring"""







     __lowerCAmelCase    :     Dict          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     Optional[int]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				eager_mode=__snake_case			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     List[str]          =		TensorFlowBenchmark(__snake_case       )
     __lowerCAmelCase    :     List[Any]          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		"""sgugger/tiny-distilbert-classification"""
     __lowerCAmelCase    :     List[str]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				only_pretrain_model=__snake_case			,				)
     __lowerCAmelCase    :     Dict          =		TensorFlowBenchmark(__snake_case       )
     __lowerCAmelCase    :     List[Any]          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     List[Any]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     int          =		TensorFlowBenchmark(__snake_case       )
     __lowerCAmelCase    :     List[Any]          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     int          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     Optional[int]          =		AutoConfig.from_pretrained(__snake_case       )
     __lowerCAmelCase    :     Optional[int]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				eager_mode=__snake_case			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     str          =		TensorFlowBenchmark(__snake_case			,				[config]       )
     __lowerCAmelCase    :     List[Any]          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     Tuple          =		AutoConfig.from_pretrained(__snake_case       )
     __lowerCAmelCase    :     Dict          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     Union[str, Any]          =		TensorFlowBenchmark(__snake_case			,				[config]       )
     __lowerCAmelCase    :     str          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      Any:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     Optional[int]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     Optional[Any]          =		TensorFlowBenchmark(__snake_case       )
     __lowerCAmelCase    :     Any          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result       )
     self.check_results_dict_not_empty(results.memory_train_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[Any]          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     Tuple          =		AutoConfig.from_pretrained(__snake_case       )
     __lowerCAmelCase    :     str          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     Optional[Any]          =		TensorFlowBenchmark(__snake_case			,				[config]       )
     __lowerCAmelCase    :     int          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result       )
     self.check_results_dict_not_empty(results.memory_train_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]       )				->      List[str]:






     """simple docstring"""







     __lowerCAmelCase    :     List[str]          =		"""patrickvonplaten/t5-tiny-random"""
     __lowerCAmelCase    :     Optional[Any]          =		AutoConfig.from_pretrained(__snake_case       )
     __lowerCAmelCase    :     str          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     List[Any]          =		TensorFlowBenchmark(__snake_case			,				configs=[config]       )
     __lowerCAmelCase    :     Union[str, Any]          =		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 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		"""sshleifer/tiny-gpt2"""
     __lowerCAmelCase    :     List[Any]          =		TensorFlowBenchmarkArguments(
         models=[MODEL_ID]			,				training=__snake_case			,				inference=__snake_case			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				use_xla=__snake_case			,				multi_process=__snake_case			,				)
     __lowerCAmelCase    :     List[str]          =		TensorFlowBenchmark(__snake_case       )
     __lowerCAmelCase    :     Optional[Any]          =		benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result       )
     self.check_results_dict_not_empty(results.memory_inference_result       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Any       )				->      str:






     """simple docstring"""







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







  def 			SCREAMING_SNAKE_CASE       (     self					:       str       )				->      Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase    :     int          =		"""sshleifer/tiny-gpt2"""

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

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








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




