'''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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Any,  _UpperCamelCase       :      int=0.9_9_9,  _UpperCamelCase       :      Dict="cosine",  )      -> Optional[int]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCamelCase       :      List[str]  ):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_UpperCamelCase       :      Union[str, Any]  ):
            return math.exp(t * -12.0  )

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

    A_      =  []
    for i in range(lowercase_  ):
        A_      =  i / num_diffusion_timesteps
        A_      =  (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       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							[e.name for e in KarrasDiffusionSchedulers]
       __lowercase   :   Tuple  					=							2

       @register_to_config
       def __init__( self       ,	_SCREAMING_SNAKE_CASE = 1000       ,	_SCREAMING_SNAKE_CASE = 0.00_085       ,	_SCREAMING_SNAKE_CASE = 0.012       ,	_SCREAMING_SNAKE_CASE = "linear"       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = "epsilon"       ,	_SCREAMING_SNAKE_CASE = "linspace"       ,	_SCREAMING_SNAKE_CASE = 0       ,	)						->	List[str]:

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

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

           #  set all values
           self.set_timesteps(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	Any:

           if schedule_timesteps is None:
               A_      =  self.timesteps

           A_      =  (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:
               A_      =  1 if len(_SCREAMING_SNAKE_CASE					) > 1 else 0
           else:
               A_      =  timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE					) else timestep
               A_      =  self._index_counter[timestep_int]

           return indices[pos].item()

       @property
       def    __A			( self					)						->	str:

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

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

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	)						->	List[Any]:
           A_      =  self.index_for_timestep(_SCREAMING_SNAKE_CASE					)

           if self.state_in_first_order:
               A_      =  self.sigmas[step_index]
           else:
               A_      =  self.sigmas_interpol[step_index - 1]

           A_      =  sample / ((sigma**2 + 1) ** 0.5)
           return sample

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	)						->	Optional[int]:
           A_      =  num_inference_steps

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

           A_      =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           A_      =  torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)

           A_      =  np.interp(_SCREAMING_SNAKE_CASE       ,	np.arange(0       ,	len(_SCREAMING_SNAKE_CASE					)					)       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
           A_      =  torch.from_numpy(_SCREAMING_SNAKE_CASE					).to(device=_SCREAMING_SNAKE_CASE					)

           # compute up and down sigmas
           A_      =  sigmas.roll(-1					)
           A_      =  0.0
           A_      =  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
           A_      =  (sigmas_next**2 - sigmas_up**2) ** 0.5
           A_      =  0.0

           # compute interpolated sigmas
           A_      =  sigmas.log().lerp(sigmas_down.log()       ,	0.5					).exp()
           A_      =  0.0

           # set sigmas
           A_      =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
           A_      =  torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
           A_      =  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
           A_      =  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

           if str(_SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
               # mps does not support float64
               A_      =  torch.from_numpy(_SCREAMING_SNAKE_CASE					).to(_SCREAMING_SNAKE_CASE       ,	dtype=torch.floataa					)
           else:
               A_      =  torch.from_numpy(_SCREAMING_SNAKE_CASE					).to(_SCREAMING_SNAKE_CASE					)

           A_      =  self.sigma_to_t(_SCREAMING_SNAKE_CASE					).to(_SCREAMING_SNAKE_CASE       ,	dtype=timesteps.dtype					)
           A_      =  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])       ,	dim=-1					).flatten()

           A_      =  torch.cat([timesteps[:1], interleaved_timesteps]					)

           A_      =  None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           A_      =  defaultdict(_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           A_      =  sigma.log()

           # get distribution
           A_      =  log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           A_      =  dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
           A_      =  low_idx + 1

           A_      =  self.log_sigmas[low_idx]
           A_      =  self.log_sigmas[high_idx]

           # interpolate sigmas
           A_      =  (low - log_sigma) / (low - high)
           A_      =  w.clamp(0       ,	1					)

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

       @property
       def    __A			( self					)						->	List[Any]:
           return self.sample is None

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = True       ,	)						->	List[str]:
           A_      =  self.index_for_timestep(_SCREAMING_SNAKE_CASE					)

           # advance index counter by 1
           A_      =  timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE					) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
               A_      =  self.sigmas[step_index]
               A_      =  self.sigmas_interpol[step_index]
               A_      =  self.sigmas_up[step_index]
               A_      =  self.sigmas_down[step_index - 1]
           else:
               # 2nd order / KPDM2's method
               A_      =  self.sigmas[step_index - 1]
               A_      =  self.sigmas_interpol[step_index - 1]
               A_      =  self.sigmas_up[step_index - 1]
               A_      =  self.sigmas_down[step_index - 1]

           # 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
           A_      =  0
           A_      =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           A_      =  model_output.device
           A_      =  randn_tensor(model_output.shape       ,	dtype=model_output.dtype       ,	device=_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE					)

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               A_      =  sigma_hat if self.state_in_first_order else sigma_interpol
               A_      =  sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
               A_      =  sigma_hat if self.state_in_first_order else sigma_interpol
               A_      =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
           elif self.config.prediction_type == "sample":
               raise NotImplementedError('''prediction_type not implemented yet: sample'''					)
           else:
               raise ValueError(
                   F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

           if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               A_      =  (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               A_      =  sigma_interpol - sigma_hat

               # store for 2nd order step
               A_      =  sample
               A_      =  dt
               A_      =  sample + derivative * dt
           else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               A_      =  (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               A_      =  sigma_down - sigma_hat

               A_      =  self.sample
               A_      =  None

               A_      =  sample + derivative * dt
               A_      =  prev_sample + noise * sigma_up

           if not return_dict:
               return (prev_sample,)

           return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	)						->	Optional[Any]:
           A_      =  self.sigmas.to(device=original_samples.device       ,	dtype=original_samples.dtype					)
           if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE					):
               # mps does not support float64
               A_      =  self.timesteps.to(original_samples.device       ,	dtype=torch.floataa					)
               A_      =  timesteps.to(original_samples.device       ,	dtype=torch.floataa					)
           else:
               A_      =  self.timesteps.to(original_samples.device					)
               A_      =  timesteps.to(original_samples.device					)

           A_      =  [self.index_for_timestep(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) for t in timesteps]

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

           A_      =  original_samples + noise * sigma
           return noisy_samples

       def __len__( self					)						->	str:
           return self.config.num_train_timesteps







"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

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

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

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

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

      __lowerCamelCase    		=					CTRLConfig(
          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 ,				)

      __lowerCamelCase    		=					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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, None, input_mask]  # None is the input for 'past'
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFCTRLForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   __UpperCAmelCase	:							List[str]    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
   __UpperCAmelCase	:							Dict    =    (
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							str    =    False
   __UpperCAmelCase	:							Tuple    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` 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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					[TFCTRLLMHeadModel]
      __lowerCamelCase    		=					[TFCTRLForSequenceClassification]

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.build()  # may be needed for the get_bias() call below
         assert isinstance(model.get_input_embeddings() ,				tf.keras.layers.Layer	)

         if model_class in list_lm_models:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.Variable	)
         elif model_class in list_other_models_with_output_ebd:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert name is None
         else:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert x is None
            __lowerCamelCase    		=					model.get_bias()
            assert name is None







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFCTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel.from_pretrained('ctrl'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[11859, 0, 1611, 8]] ,				dtype=tf.intaa	)  # Legal the president is
      __lowerCamelCase    		=					[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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






