'''simple docstring'''


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[int]		       =						UNetaDModel
  lowerCamelCase_						:int		       =						'''sample'''


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = 4
       UpperCAmelCase_				:  Any  = 1_4
       UpperCAmelCase_				:  Dict  = 1_6

       UpperCAmelCase_				:  Union[str, Any]  = floats_tensor((batch_size, num_features, seq_len)		).to(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = torch.tensor([1_0] * batch_size		).to(snake_case_		)

       return {"sample": noise, "timestep": time_step}


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (4, 1_4, 1_6)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (4, 1_4, 1_6)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_determinism()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_outputs_equivalence()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_from_save_pretrained()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_from_save_pretrained_variant()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_model_from_pretrained()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_output()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {
           'block_out_channels': (3_2, 6_4, 1_2_8, 2_5_6),
           'in_channels': 1_4,
           'out_channels': 1_4,
           'time_embedding_type': 'positional',
           'use_timestep_embedding': True,
           'flip_sin_to_cos': False,
           'freq_shift': 1.0,
           'out_block_type': 'OutConv1DBlock',
           'mid_block_type': 'MidResTemporalBlock1D',
           'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
           'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
           'act_fn': 'mish',
       }
       UpperCAmelCase_				:  List[str]  = self.dummy_input
       return init_dict, inputs_dict


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = UNetaDModel.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32' ,						output_loading_info=snake_case_ ,						subfolder='unet'		)
       self.assertIsNotNone(snake_case_		)
       self.assertEqual(len(loading_info['missing_keys']		) ,						0		)

       model.to(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = model(**self.dummy_input		)

       assert image is not None, "Make sure output is not None"


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32' ,						subfolder='unet'		)
       torch.manual_seed(0		)
       if torch.cuda.is_available():
            torch.cuda.manual_seed_all(0		)

       UpperCAmelCase_				:  Optional[Any]  = model.config.in_channels
       UpperCAmelCase_				:  List[Any]  = 1_6
       UpperCAmelCase_				:  int  = torch.randn((1, seq_len, num_features)		).permute(
           0 ,						2 ,						1		)  # match original, we can update values and remove
       UpperCAmelCase_				:  List[str]  = torch.full((num_features,) ,						0		)

       with torch.no_grad():
            UpperCAmelCase_				:  Union[str, Any]  = model(snake_case_ ,						snake_case_		).sample.permute(0 ,						2 ,						1		)

       UpperCAmelCase_				:  Tuple  = output[0, -3:, -3:].flatten()
       # fmt: off
       UpperCAmelCase_				:  Any  = torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]		)
       # fmt: on
       self.assertTrue(torch.allclose(snake_case_ ,						snake_case_ ,						rtol=1E-3		)		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = 'harmonai/maestro-150k'
       UpperCAmelCase_				:  Optional[Any]  = UNetaDModel.from_pretrained(snake_case_ ,						subfolder='unet'		)
       model.to(snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = 6_5_5_3_6
       UpperCAmelCase_				:  Optional[Any]  = torch.sin(torch.arange(snake_case_		)[None, None, :].repeat(1 ,						2 ,						1		)		).to(snake_case_		)
       UpperCAmelCase_				:  Dict  = torch.tensor([1]		).to(snake_case_		)

       with torch.no_grad():
            UpperCAmelCase_				:  str  = model(snake_case_ ,						snake_case_		).sample

       UpperCAmelCase_				:  Any  = output.abs().sum()
       UpperCAmelCase_				:  Tuple  = output.abs().max()

       assert (output_sum - 2_24.08_96).abs() < 0.5
       assert (output_max - 0.06_07).abs() < 4E-4







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Tuple		       =						UNetaDModel
  lowerCamelCase_						:Any		       =						'''sample'''


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 4
       UpperCAmelCase_				:  int  = 1_4
       UpperCAmelCase_				:  Optional[Any]  = 1_6

       UpperCAmelCase_				:  Dict  = floats_tensor((batch_size, num_features, seq_len)		).to(snake_case_		)
       UpperCAmelCase_				:  Any  = torch.tensor([1_0] * batch_size		).to(snake_case_		)

       return {"sample": noise, "timestep": time_step}


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (4, 1_4, 1_6)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (4, 1_4, 1)


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_determinism()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_outputs_equivalence()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_from_save_pretrained()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_from_save_pretrained_variant()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_model_from_pretrained()


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = self.prepare_init_args_and_inputs_for_common()
       UpperCAmelCase_				:  Optional[Any]  = self.model_class(**snake_case_		)
       model.to(snake_case_		)
       model.eval()

       with torch.no_grad():
            UpperCAmelCase_				:  Optional[Any]  = model(**snake_case_		)

            if isinstance(snake_case_ ,						snake_case_		):
                 UpperCAmelCase_				:  Any  = output.sample

       self.assertIsNotNone(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = torch.Size((inputs_dict['sample'].shape[0], 1)		)
       self.assertEqual(output.shape ,						snake_case_ ,						'Input and output shapes do not match'		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = {
           'in_channels': 1_4,
           'out_channels': 1_4,
           'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
           'up_block_types': [],
           'out_block_type': 'ValueFunction',
           'mid_block_type': 'ValueFunctionMidBlock1D',
           'block_out_channels': [3_2, 6_4, 1_2_8, 2_5_6],
           'layers_per_block': 1,
           'downsample_each_block': True,
           'use_timestep_embedding': True,
           'freq_shift': 1.0,
           'flip_sin_to_cos': False,
           'time_embedding_type': 'positional',
           'act_fn': 'mish',
       }
       UpperCAmelCase_				:  Optional[Any]  = self.dummy_input
       return init_dict, inputs_dict


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  int  = UNetaDModel.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32' ,						output_loading_info=snake_case_ ,						subfolder='value_function'		)
       self.assertIsNotNone(snake_case_		)
       self.assertEqual(len(vf_loading_info['missing_keys']		) ,						0		)

       value_function.to(snake_case_		)
       UpperCAmelCase_				:  List[str]  = value_function(**self.dummy_input		)

       assert image is not None, "Make sure output is not None"


  @unittest.skipIf(torch_device == 'mps' ,						'mish op not supported in MPS'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = UNetaDModel.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32' ,						output_loading_info=snake_case_ ,						subfolder='value_function'		)
       torch.manual_seed(0		)
       if torch.cuda.is_available():
            torch.cuda.manual_seed_all(0		)

       UpperCAmelCase_				:  List[Any]  = value_function.config.in_channels
       UpperCAmelCase_				:  Tuple  = 1_4
       UpperCAmelCase_				:  Any  = torch.randn((1, seq_len, num_features)		).permute(
           0 ,						2 ,						1		)  # match original, we can update values and remove
       UpperCAmelCase_				:  Union[str, Any]  = torch.full((num_features,) ,						0		)

       with torch.no_grad():
            UpperCAmelCase_				:  Tuple  = value_function(snake_case_ ,						snake_case_		).sample

       # fmt: off
       UpperCAmelCase_				:  Tuple  = torch.tensor([1_65.25] * seq_len		)
       # fmt: on
       self.assertTrue(torch.allclose(snake_case_ ,						snake_case_ ,						rtol=1E-3		)		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass








'''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 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Dict								=		parent
    SCREAMING_SNAKE_CASE  :  int								=		13
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		7
    SCREAMING_SNAKE_CASE  :  List[Any]								=		True
    SCREAMING_SNAKE_CASE  :  Dict								=		True
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		True
    SCREAMING_SNAKE_CASE  :  int								=		True
    SCREAMING_SNAKE_CASE  :  str								=		True
    SCREAMING_SNAKE_CASE  :  int								=		99
    SCREAMING_SNAKE_CASE  :  List[str]								=		32
    SCREAMING_SNAKE_CASE  :  List[str]								=		2
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		4
    SCREAMING_SNAKE_CASE  :  Tuple								=		37
    SCREAMING_SNAKE_CASE  :  str								=		'gelu'
    SCREAMING_SNAKE_CASE  :  Tuple								=		0.1
    SCREAMING_SNAKE_CASE  :  Any								=		0.1
    SCREAMING_SNAKE_CASE  :  List[str]								=		512
    SCREAMING_SNAKE_CASE  :  List[Any]								=		16
    SCREAMING_SNAKE_CASE  :  List[Any]								=		2
    SCREAMING_SNAKE_CASE  :  Any								=		0.02
    SCREAMING_SNAKE_CASE  :  List[str]								=		3
    SCREAMING_SNAKE_CASE  :  Any								=		4
    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.vocab_size - 1






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size      )

    SCREAMING_SNAKE_CASE  :  Dict								=		None
    if self.use_input_mask:
     SCREAMING_SNAKE_CASE  :  List[str]								=		random_attention_mask([self.batch_size, self.seq_length]      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    if self.use_token_type_ids:
     SCREAMING_SNAKE_CASE  :  str								=		ids_tensor([self.batch_size, self.seq_length]    ,self.type_vocab_size      )

    SCREAMING_SNAKE_CASE  :  int								=		None
    if self.use_mc_token_ids:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ids_tensor([self.batch_size, self.num_choices]    ,self.seq_length      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		None
    SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    SCREAMING_SNAKE_CASE  :  str								=		None
    if self.use_labels:
     SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ids_tensor([self.batch_size, self.seq_length]    ,self.num_labels      )
     SCREAMING_SNAKE_CASE  :  str								=		ids_tensor([self.batch_size]    ,self.num_choices      )

    SCREAMING_SNAKE_CASE  :  Dict								=		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    ,)

    SCREAMING_SNAKE_CASE  :  Tuple								=		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      ):
    SCREAMING_SNAKE_CASE  :  int								=		TFCTRLModel(config=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[input_ids, None, input_mask]  # None is the input for 'past'
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		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      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		TFCTRLLMHeadModel(config=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
    SCREAMING_SNAKE_CASE  :  Any								=		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      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  Any								=		ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
    SCREAMING_SNAKE_CASE  :  Dict								=		{
        'input_ids': input_ids,
        'token_type_ids': token_type_ids,
        'labels': sequence_labels,
    }
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		TFCTRLForSequenceClassification(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		model(__SCREAMING_SNAKE_CASE      )
    self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.prepare_config_and_inputs()

    (
        (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, (
        SCREAMING_SNAKE_CASE
    )				, 
    )  :  List[Any]								=		config_and_inputs

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



@require_tf
class 		_a     (							SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A		= (TFCTRLLMHeadModel,) if is_tf_available() else ()
   A		= (
       {
           'feature-extraction': TFCTRLModel,
           'text-classification': TFCTRLForSequenceClassification,
           'text-generation': TFCTRLLMHeadModel,
           'zero-shot': TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A		= False
   A		= False






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    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      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		TFCTRLModelTester(self      )
    SCREAMING_SNAKE_CASE  :  Dict								=		ConfigTester(self    ,config_class=__SCREAMING_SNAKE_CASE    ,n_embd=37      )






   def 					__a			(       self      ):
    self.config_tester.run_common_tests()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_model(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_lm_head(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_ctrl_for_sequence_classification(*__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE  :  Any								=		[TFCTRLLMHeadModel]
    SCREAMING_SNAKE_CASE  :  str								=		[TFCTRLForSequenceClassification]

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE  :  List[str]								=		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:
      SCREAMING_SNAKE_CASE  :  Dict								=		model.get_output_embeddings()
      assert isinstance(__SCREAMING_SNAKE_CASE    ,tf.keras.layers.Layer      )
      SCREAMING_SNAKE_CASE  :  Any								=		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:
      SCREAMING_SNAKE_CASE  :  Any								=		model.get_output_embeddings()
      assert isinstance(__SCREAMING_SNAKE_CASE    ,tf.keras.layers.Layer      )
      SCREAMING_SNAKE_CASE  :  str								=		model.get_bias()
      assert name is None
     else:
      SCREAMING_SNAKE_CASE  :  List[Any]								=		model.get_output_embeddings()
      assert x is None
      SCREAMING_SNAKE_CASE  :  int								=		model.get_bias()
      assert name is None






   @slow
   def 					__a			(       self      ):
    for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE  :  List[str]								=		TFCTRLModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
     self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )



@require_tf
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		TFCTRLLMHeadModel.from_pretrained('ctrl'      )
    SCREAMING_SNAKE_CASE  :  Any								=		tf.convert_to_tensor([[11859, 0, 1611, 8]]    ,dtype=tf.intaa      )  # Legal the president is
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[
        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

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model.generate(__SCREAMING_SNAKE_CASE    ,do_sample=__SCREAMING_SNAKE_CASE      )
    self.assertListEqual(output_ids[0].numpy().tolist()    ,__SCREAMING_SNAKE_CASE      )


