

'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(DDPMScheduler,)
		def 			UpperCamelCase(					self   ,			**SCREAMING_SNAKE_CASE_		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**SCREAMING_SNAKE_CASE_		)
							return config
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							for timesteps in [1, 5, 100, 1000]:
												self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]   ,			[0.002, 0.02, 0.2, 2]		):
												self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_   ,			beta_end=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_		)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=SCREAMING_SNAKE_CASE_   ,			prediction_type=SCREAMING_SNAKE_CASE_   ,			sample_max_value=SCREAMING_SNAKE_CASE_   ,			)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							for t in [0, 500, 999]:
												self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00_979		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      len(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      self.dummy_model()
							lowerCamelCase_  				=      self.dummy_sample_deter
							lowerCamelCase_  				=      torch.manual_seed(0		)

							for t in reversed(range(SCREAMING_SNAKE_CASE_		)		):
												# 1. predict noise residual
												lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

												# 2. predict previous mean of sample x_t-1
												lowerCamelCase_  				=      scheduler.step(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												lowerCamelCase_  				=      pred_prev_sample

							lowerCamelCase_  				=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
							lowerCamelCase_  				=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)

							assert abs(result_sum.item() - 258.9_606		) < 1E-2
							assert abs(result_mean.item() - 0.3_372		) < 1E-3
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config(prediction_type='v_prediction'		)
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      len(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      self.dummy_model()
							lowerCamelCase_  				=      self.dummy_sample_deter
							lowerCamelCase_  				=      torch.manual_seed(0		)

							for t in reversed(range(SCREAMING_SNAKE_CASE_		)		):
												# 1. predict noise residual
												lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

												# 2. predict previous mean of sample x_t-1
												lowerCamelCase_  				=      scheduler.step(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												lowerCamelCase_  				=      pred_prev_sample

							lowerCamelCase_  				=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
							lowerCamelCase_  				=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)

							assert abs(result_sum.item() - 202.0_296		) < 1E-2
							assert abs(result_mean.item() - 0.2_631		) < 1E-3
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      scheduler.timesteps

							for i, timestep in enumerate(SCREAMING_SNAKE_CASE_		):
												if i == len(SCREAMING_SNAKE_CASE_		) - 1:
																	lowerCamelCase_  				=      -1
												else:
																	lowerCamelCase_  				=      timesteps[i + 1]

												lowerCamelCase_  				=      scheduler.previous_timestep(SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      prev_t.item()

												self.assertEqual(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      [100, 87, 50, 51, 0]

							with self.assertRaises(SCREAMING_SNAKE_CASE_   ,			msg='`custom_timesteps` must be in descending order.'		):
												scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      [100, 87, 50, 1, 0]
							lowerCamelCase_  				=      len(SCREAMING_SNAKE_CASE_		)

							with self.assertRaises(SCREAMING_SNAKE_CASE_   ,			msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'		):
												scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_   ,			timesteps=SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.scheduler_classes[0]
							lowerCamelCase_  				=      self.get_scheduler_config()
							lowerCamelCase_  				=      scheduler_class(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    SCREAMING_SNAKE_CASE_   ,			msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,			):
												scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_		)








import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1_3		,				SCREAMING_SNAKE_CASE=7		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=9_9		,				SCREAMING_SNAKE_CASE=3_2		,				SCREAMING_SNAKE_CASE=5		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=3_7		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=5_1_2		,				SCREAMING_SNAKE_CASE=1_6		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=None		,				) ->			Union[str, Any]:
  a__												= parent
  a__												= batch_size
  a__												= seq_length
  a__												= is_training
  a__												= use_token_type_ids
  a__												= use_labels
  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= intermediate_size
  a__												= hidden_act
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= max_position_embeddings
  a__												= type_vocab_size
  a__												= type_sequence_label_size
  a__												= initializer_range
  a__												= num_labels
  a__												= num_choices
  a__												= scope
  a__												= self.vocab_size - 1

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

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

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

  a__												= 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		,				)

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

  return (
      config,
      input_ids,
      head_mask,
      token_type_ids,
      sequence_labels,
      token_labels,
      choice_labels,
  )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= OpenAIGPTModel(config=SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE      )
  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE      )
  a__												= model(SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Tuple:
  a__												= OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.loss.shape		,				()      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Dict:
  a__												= OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.loss.shape		,				()      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= self.num_labels
  a__												= OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= ids_tensor([self.batch_size]		,				self.type_sequence_label_size      )
  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)      )



 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= self.prepare_config_and_inputs()
  (
      (
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			
  )												= config_and_inputs
  a__												= {
      '''input_ids''': input_ids,
      '''token_type_ids''': token_type_ids,
      '''head_mask''': head_mask,
  }

  return config, inputs_dict





@require_torch
class    __UpperCamelCase			(       _lowercase	,	_lowercase	,	_lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    Dict      						=					(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 _lowercase   :    Optional[Any]      						=					(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 _lowercase   :    Dict      						=					(
     {
         '''feature-extraction''': OpenAIGPTModel,
         '''text-classification''': OpenAIGPTForSequenceClassification,
         '''text-generation''': OpenAIGPTLMHeadModel,
         '''zero-shot''': OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  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 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False      ) ->			List[str]:
  a__												= super()._prepare_for_class(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				return_labels=SCREAMING_SNAKE_CASE      )

  if return_labels:
   if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
    a__												= torch.zeros(
        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE		,				)
    a__												= inputs_dict['''labels''']
    a__												= inputs_dict['''labels''']
    a__												= torch.zeros(
        (self.model_tester.batch_size, self.model_tester.num_choices)		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE		,				)
    a__												= torch.zeros(
        self.model_tester.batch_size		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE      )
  return inputs_dict

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= OpenAIGPTModelTester(self      )
  a__												= ConfigTester(self		,				config_class=SCREAMING_SNAKE_CASE		,				n_embd=3_7      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  self.config_tester.run_common_tests()

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE      )



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   a__												= OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE      )
   self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





@require_torch
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''      )
  model.to(SCREAMING_SNAKE_CASE      )
  a__												= torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE      )  # the president is
  a__												= [
      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

  a__												= model.generate(SCREAMING_SNAKE_CASE		,				do_sample=SCREAMING_SNAKE_CASE      )
  self.assertListEqual(output_ids[0].tolist()		,				SCREAMING_SNAKE_CASE      )





