'''simple docstring'''




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					A						:				Optional[Any]						,					A						:				int=14						,					A						:				Tuple=7						,					A						:				str=True						,					A						:				Tuple=True						,					A						:				Optional[int]=True						,					A						:				Tuple=True						,					A						:				List[str]=True						,					A						:				List[str]=99						,					A						:				Optional[Any]=32						,					A						:				str=5						,					A						:				Tuple=4						,					A						:				str=37						,					A						:				Any="gelu"						,					A						:				Any=0.1						,					A						:				str=0.1						,					A						:				int=512						,					A						:				List[str]=16						,					A						:				List[Any]=2						,					A						:				List[str]=0.02						,					A						:				List[Any]=3						,					A						:				Any=4						,					A						:				int=None						,					):
             _UpperCAmelCase     :  Optional[int]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  List[str]							      =    seq_length
             _UpperCAmelCase     :  Any							      =    is_training
             _UpperCAmelCase     :  Union[str, Any]							      =    use_token_type_ids
             _UpperCAmelCase     :  Optional[int]							      =    use_input_mask
             _UpperCAmelCase     :  Dict							      =    use_labels
             _UpperCAmelCase     :  Optional[Any]							      =    use_mc_token_ids
             _UpperCAmelCase     :  Any							      =    vocab_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_hidden_layers
             _UpperCAmelCase     :  Optional[int]							      =    num_attention_heads
             _UpperCAmelCase     :  Tuple							      =    intermediate_size
             _UpperCAmelCase     :  Dict							      =    hidden_act
             _UpperCAmelCase     :  List[str]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Optional[int]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Union[str, Any]							      =    max_position_embeddings
             _UpperCAmelCase     :  Dict							      =    type_vocab_size
             _UpperCAmelCase     :  Tuple							      =    type_sequence_label_size
             _UpperCAmelCase     :  List[Any]							      =    initializer_range
             _UpperCAmelCase     :  Union[str, Any]							      =    num_labels
             _UpperCAmelCase     :  Optional[int]							      =    num_choices
             _UpperCAmelCase     :  Dict							      =    scope
             _UpperCAmelCase     :  Optional[Any]							      =    self.vocab_size - 1




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Dict							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size       )

             _UpperCAmelCase     :  str							      =    None
             if self.use_input_mask:
                    _UpperCAmelCase     :  Any							      =    random_attention_mask([self.batch_size, self.seq_length]       )

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

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

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

             _UpperCAmelCase     :  List[Any]							      =    self.get_config()

             _UpperCAmelCase     :  Optional[int]							      =    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						:				Optional[Any]       ):
             return 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						,					)




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[int]						,					A						:				str						,					*A						:				Any       ):
             _UpperCAmelCase     :  str							      =    CTRLModel(config=A       )
             model.to(A       )
             model.eval()

             model(A						,					token_type_ids=A						,					head_mask=A       )
             model(A						,					token_type_ids=A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A       )
             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )
             self.parent.assertEqual(len(result.past_key_values       )						,					config.n_layer       )




      def      _A	(       self						:				Dict						,					A						:				Any						,					A						:				int						,					A						:				List[str]						,					A						:				Dict						,					A						:				int						,					*A						:				List[Any]       ):
             _UpperCAmelCase     :  Dict							      =    CTRLLMHeadModel(A       )
             model.to(A       )
             model.eval()

             _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					token_type_ids=A						,					labels=A       )
             self.parent.assertEqual(result.loss.shape						,					()       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.prepare_config_and_inputs()

             (
                 (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  (
                 _UpperCAmelCase					
             )      ,  					
             )     :  Dict							      =    config_and_inputs

             _UpperCAmelCase     :  Any							      =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

             return config, inputs_dict





      def      _A	(       self						:				Optional[Any]						,					A						:				List[str]						,					A						:				List[Any]						,					A						:				List[Any]						,					A						:				Union[str, Any]						,					*A						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    self.num_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    CTRLForSequenceClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    ids_tensor([self.batch_size]						,					self.type_sequence_label_size       )
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					token_type_ids=A						,					labels=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)       )

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
      __UpperCamelCase:  Optional[int]						=						(CTRLLMHeadModel,) if is_torch_available() else ()
      __UpperCamelCase:  Dict						=						(
          {
              "feature-extraction": CTRLModel,
              "text-classification": CTRLForSequenceClassification,
              "text-generation": CTRLLMHeadModel,
              "zero-shot": CTRLForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase:  str						=						True
      __UpperCamelCase:  int						=						False
      __UpperCamelCase:  Any						=						False




      def      _A	(       self						:				List[str]						,					A						:				int						,					A						:				Union[str, Any]						,					A						:				Tuple						,					A						:				int						,					A						:				Tuple       ):


             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						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    CTRLModelTester(self       )
             _UpperCAmelCase     :  int							      =    ConfigTester(self						,					config_class=A						,					n_embd=37       )




      def      _A	(       self						:				Union[str, Any]       ):
             super().tearDown()
             # clean-up as much as possible GPU memory occupied by PyTorch
             gc.collect()
             torch.cuda.empty_cache()




      def      _A	(       self						:				Optional[int]       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  str							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_model(*A       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(*A       )




      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
      def      _A	(       self						:				Optional[Any]       ):
             pass




      @slow
      def      _A	(       self						:				List[Any]       ):


             for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase     :  Optional[int]							      =    CTRLModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )





      @unittest.skip("The model doesn\'t support left padding"       )  # and it's not used enough to be worth fixing :)
      def      _A	(       self						:				Tuple       ):
             pass

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Union[str, Any]       ):
             super().tearDown()
             # clean-up as much as possible GPU memory occupied by PyTorch
             gc.collect()
             torch.cuda.empty_cache()





      @slow
      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  List[Any]							      =    CTRLLMHeadModel.from_pretrained("ctrl"       )
             model.to(A       )
             _UpperCAmelCase     :  Tuple							      =    torch.tensor(
                 [[11859, 0, 1611, 8]]						,					dtype=torch.long						,					device=A       )  # Legal the president is
             _UpperCAmelCase     :  Dict							      =    [
                 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

             _UpperCAmelCase     :  Any							      =    model.generate(A						,					do_sample=A       )
             self.assertListEqual(output_ids[0].tolist()						,					A       )



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)