




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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     List[str]:







				'''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}'''   )

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

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Union[str, Any],     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		Optional[bool] = False,     _lowercase:		str = "epsilon",     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


									if trained_betas is not None:
													__lowerCAmelCase					=  torch.tensor(_lowercase,     dtype=torch.floataa)
									elif beta_schedule == "linear":
													__lowerCAmelCase					=  torch.linspace(_lowercase,     _lowercase,     _lowercase,     dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCAmelCase					=  (
													    torch.linspace(beta_start**0.5,     beta_end**0.5,     _lowercase,     dtype=torch.floataa) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCAmelCase					=  betas_for_alpha_bar(_lowercase)
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

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

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase)

									# setable values
									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  use_karras_sigmas
									self.set_timesteps(_lowercase,     _lowercase)
									__lowerCAmelCase					=  []
									__lowerCAmelCase					=  False




					@property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


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

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




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]
									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)
									__lowerCAmelCase					=  True
									return sample




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		Tuple,     _lowercase:		Optional[int],     _lowercase:		List[str]):


									'''simple docstring'''


									def lms_derivative(_lowercase:		List[str]):
													__lowerCAmelCase					=  1.0
													for k in range(_lowercase):
																	if current_order == k:
																					continue
																	prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
													return prod

									__lowerCAmelCase					=  integrate.quad(_lowercase,     self.sigmas[t],     self.sigmas[t + 1],     epsrel=1e-4)[0]

									return integrated_coeff




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  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":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)
									__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)

									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)

									__lowerCAmelCase					=  []




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		List[Any],     _lowercase:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

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

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

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		torch.FloatTensor):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     self.num_inference_steps)
									__lowerCAmelCase					=  sigma_min ** (1 / rho)
									__lowerCAmelCase					=  sigma_max ** (1 / rho)
									__lowerCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




					def     _lowercase						(   self:		List[Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		int = 4,     _lowercase:		bool = True,     ):


									'''simple docstring'''


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

									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									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
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma
									self.derivatives.append(_lowercase)
									if len(self.derivatives) > order:
													self.derivatives.pop(0)

									# 3. Compute linear multistep coefficients
									__lowerCAmelCase					=  min(step_index + 1,     _lowercase)
									__lowerCAmelCase					=  [self.get_lms_coefficient(_lowercase,     _lowercase,     _lowercase) for curr_order in range(_lowercase)]

									# 4. Compute previous sample based on the derivatives path
									__lowerCAmelCase					=  sample + sum(
									    coeff * derivative for coeff, derivative in zip(_lowercase,     reversed(self.derivatives)))

									if not return_dict:
													return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase):
													# mps does not support float64
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device,     dtype=torch.floataa)
													__lowerCAmelCase					=  timesteps.to(original_samples.device,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device)
													__lowerCAmelCase					=  timesteps.to(original_samples.device)

									__lowerCAmelCase					=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									__lowerCAmelCase					=  sigmas[step_indices].flatten()
									while len(sigma.shape) < len(original_samples.shape):
													__lowerCAmelCase					=  sigma.unsqueeze(-1)

									__lowerCAmelCase					=  original_samples + noise * sigma
									return noisy_samples







					def __len__(   self:		Tuple):


									'''simple docstring'''


									return self.config.num_train_timesteps






"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )
class   UpperCAmelCase_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       parent
       _lowercase							     =       1_3
       _lowercase							     =       7
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       9_9
       _lowercase							     =       3_2
       _lowercase							     =       2
       _lowercase							     =       4
       _lowercase							     =       3_7
       _lowercase							     =       "gelu"
       _lowercase							     =       0.1
       _lowercase							     =       0.1
       _lowercase							     =       5_1_2
       _lowercase							     =       1_6
       _lowercase							     =       2
       _lowercase							     =       0.02
       _lowercase							     =       3
       _lowercase							     =       4
       _lowercase							     =       None
       _lowercase							     =       self.vocab_size - 1
    def 		_lowerCamelCase      (	self			:       int		)						->    List[Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

       _lowercase							     =       None
       if self.use_input_mask:
          _lowercase							     =       random_attention_mask([self.batch_size, self.seq_length]		)

       _lowercase							     =       None
       if self.use_token_type_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

       _lowercase							     =       None
       if self.use_mc_token_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.num_choices]   ,						self.seq_length		)

       _lowercase							     =       None
       _lowercase							     =       None
       _lowercase							     =       None
       if self.use_labels:
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.num_choices		)

       _lowercase							     =       OpenAIGPTConfig(
           vocab_size=self.vocab_size   ,						n_embd=self.hidden_size   ,						n_layer=self.num_hidden_layers   ,						n_head=self.num_attention_heads   ,						n_positions=self.max_position_embeddings   ,						pad_token_id=self.pad_token_id   ,						)

       _lowercase							     =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2		)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]   ,						_lowercase			:       int   ,						*_lowercase			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       [input_ids, input_mask]
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       model(_lowercase		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						*_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       TFOpenAIGPTLMHeadModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTDoubleHeadsModel(config=_lowercase		)

       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)

       _lowercase							     =       {
           "input_ids": multiple_choice_inputs_ids,
           "mc_token_ids": mc_token_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(
           result.logits.shape   ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.mc_logits.shape   ,						(self.batch_size, self.num_choices)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       str		)						->    str:
       _lowercase							     =       self.num_labels
       _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
       _lowercase							     =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       _lowercase							     =       TFOpenAIGPTForSequenceClassification(_lowercase		)
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       self.prepare_config_and_inputs()

       (
           (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  
       )							     =       config_and_inputs

       _lowercase							     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
@require_tf
class   UpperCAmelCase_	(				lowercase__,    lowercase__,    unittest.TestCase   ):
    snake_case_											=      (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    snake_case_											=      (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    snake_case_											=      (
        {
            """feature-extraction""": TFOpenAIGPTModel,
            """text-classification""": TFOpenAIGPTForSequenceClassification,
            """text-generation""": TFOpenAIGPTLMHeadModel,
            """zero-shot""": TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any		)						->    List[str]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
          # tiny config could not be created.
          return True

       return False
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       TFOpenAIGPTModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						n_embd=3_7		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase		)




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       TFOpenAIGPTModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)
@require_tf
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @slow
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Union[str, Any]:
       _lowercase							     =       TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
       _lowercase							     =       tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]   ,						dtype=tf.intaa		)  # the president is
       _lowercase							     =       [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       _lowercase							     =       model.generate(_lowercase   ,						do_sample=_lowercase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,						_lowercase		)