



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					Any							    =    (DDPMParallelScheduler,)






						def 			_A      (       self ,				**_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   {
													    'num_train_timesteps': 1_000,
													    'beta_start': 0.0_0_0_1,
													    'beta_end': 0.0_2,
													    'beta_schedule': 'linear',
													    'variance_type': 'fixed_small',
													    'clip_sample': True,
													}

													config.update(**_lowerCAmelCase						)
													return config






						def 			_A      (       self						):







													'''simple docstring'''

													for timesteps in [1, 5, 100, 1_000]:
																				self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,				[0.0_0_2, 0.0_2, 0.2, 2]						):
																				self.check_over_configs(beta_start=_lowerCAmelCase ,				beta_end=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													for schedule in ["linear", "squaredcos_cap_v2"]:
																				self.check_over_configs(beta_schedule=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													for variance in ["fixed_small", "fixed_large", "other"]:
																				self.check_over_configs(variance_type=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													for clip_sample in [True, False]:
																				self.check_over_configs(clip_sample=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													self.check_over_configs(thresholding=_lowerCAmelCase						)
													for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "sample", "v_prediction"]:
																											self.check_over_configs(
																											    thresholding=_lowerCAmelCase ,				prediction_type=_lowerCAmelCase ,				sample_max_value=_lowerCAmelCase ,				)






						def 			_A      (       self						):







													'''simple docstring'''

													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																				self.check_over_configs(prediction_type=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													for t in [0, 500, 999]:
																				self.check_over_forward(time_step=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.0_0_9_7_9						)						) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.0_2						)						) < 1e-5






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   len(_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   self.dummy_model()
													__SCREAMING_SNAKE_CASE										=   self.dummy_sample_deter
													__SCREAMING_SNAKE_CASE										=   self.dummy_sample_deter + 0.1
													__SCREAMING_SNAKE_CASE										=   self.dummy_sample_deter - 0.1

													__SCREAMING_SNAKE_CASE										=   samplea.shape[0]
													__SCREAMING_SNAKE_CASE										=   torch.stack([samplea, samplea, samplea] ,				dim=0						)
													__SCREAMING_SNAKE_CASE										=   torch.arange(_lowerCAmelCase						)[0:3, None].repeat(1 ,				_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   model(samples.flatten(0 ,				1						) ,				timesteps.flatten(0 ,				1						)						)
													__SCREAMING_SNAKE_CASE										=   scheduler.batch_step_no_noise(_lowerCAmelCase ,				timesteps.flatten(0 ,				1						) ,				samples.flatten(0 ,				1						)						)

													__SCREAMING_SNAKE_CASE										=   torch.sum(torch.abs(_lowerCAmelCase						)						)
													__SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(_lowerCAmelCase						)						)

													assert abs(result_sum.item() - 1_1_5_3.1_8_3_3						) < 1e-2
													assert abs(result_mean.item() - 0.5_0_0_5						) < 1e-3






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   len(_lowerCAmelCase						)

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

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

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

																				__SCREAMING_SNAKE_CASE										=   pred_prev_sample

													__SCREAMING_SNAKE_CASE										=   torch.sum(torch.abs(_lowerCAmelCase						)						)
													__SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(_lowerCAmelCase						)						)

													assert abs(result_sum.item() - 2_5_8.9_6_0_6						) < 1e-2
													assert abs(result_mean.item() - 0.3_3_7_2						) < 1e-3






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config(prediction_type='v_prediction'						)
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   len(_lowerCAmelCase						)

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

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

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

																				__SCREAMING_SNAKE_CASE										=   pred_prev_sample

													__SCREAMING_SNAKE_CASE										=   torch.sum(torch.abs(_lowerCAmelCase						)						)
													__SCREAMING_SNAKE_CASE										=   torch.mean(torch.abs(_lowerCAmelCase						)						)

													assert abs(result_sum.item() - 2_0_2.0_2_9_6						) < 1e-2
													assert abs(result_mean.item() - 0.2_6_3_1						) < 1e-3






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

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

													scheduler.set_timesteps(timesteps=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   scheduler.timesteps

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

																				__SCREAMING_SNAKE_CASE										=   scheduler.previous_timestep(_lowerCAmelCase						)
																				__SCREAMING_SNAKE_CASE										=   prev_t.item()

																				self.assertEqual(_lowerCAmelCase ,				_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

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

													with self.assertRaises(_lowerCAmelCase ,				msg='`custom_timesteps` must be in descending order.'						):
																				scheduler.set_timesteps(timesteps=_lowerCAmelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   [100, 87, 50, 1, 0]
													__SCREAMING_SNAKE_CASE										=   len(_lowerCAmelCase						)

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






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.scheduler_classes[0]
													__SCREAMING_SNAKE_CASE										=   self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE										=   scheduler_class(**_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   [scheduler.config.num_train_timesteps]

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













'''simple docstring'''


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers import AutoModelForCausalLM

@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()
     _lowerCAmelCase						      =   ""
     for new_text in streamer:
         streamer_text += new_text

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   greedy_ids[:, input_ids.shape[1] :]
     _lowerCAmelCase						      =   tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_prompt=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
     # with actual models -- the dummy models' tokenizers are not aligned with their models, and
     # `skip_special_tokens=True` has no effect on them
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   torch.ones((1, 5)      ,  device=_lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=1      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ""
         for new_text in streamer:
             streamer_text += new_text


