import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     (EulerDiscreteScheduler,)
							_UpperCAmelCase						    =     1_0







							def 				snake_case  (				self     :    Dict	,					**__snake_case     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     {
										    '''num_train_timesteps''': 1100,
										    '''beta_start''': 0.0_0_0_1,
										    '''beta_end''': 0.0_2,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**__snake_case		)
										return config







							def 				snake_case  (				self     :    Any		):
										for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=__snake_case		)







							def 				snake_case  (				self     :    List[str]		):
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,					[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
													self.check_over_configs(beta_start=__snake_case	,					beta_end=__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__snake_case		)







							def 				snake_case  (				self     :    int		):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__snake_case		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:int						  =     self.scheduler_classes[0]
										lowerCamelCase						:List[Any]						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps		)

										lowerCamelCase						:Optional[Any]						  =     torch.manual_seed(0		)

										lowerCamelCase						:List[str]						  =     self.dummy_model()
										lowerCamelCase						:List[Any]						  =     self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase						:List[str]						  =     sample.to(__snake_case		)

										for i, t in enumerate(scheduler.timesteps		):
													lowerCamelCase						:Optional[int]						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:Dict						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:List[str]						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:Any						  =     output.prev_sample

										lowerCamelCase						:Optional[int]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:Tuple						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 10.0807		) < 1e-2
										assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[Any]						  =     self.scheduler_classes[0]
										lowerCamelCase						:List[Any]						  =     self.get_scheduler_config(prediction_type='''v_prediction'''		)
										lowerCamelCase						:Dict						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps		)

										lowerCamelCase						:Optional[int]						  =     torch.manual_seed(0		)

										lowerCamelCase						:Union[str, Any]						  =     self.dummy_model()
										lowerCamelCase						:str						  =     self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase						:int						  =     sample.to(__snake_case		)

										for i, t in enumerate(scheduler.timesteps		):
													lowerCamelCase						:int						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:List[str]						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:Optional[Any]						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:str						  =     output.prev_sample

										lowerCamelCase						:List[Any]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:str						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 0.0_0_0_2		) < 1e-2
										assert abs(result_mean.item() - 2.2_6_7_6e-0_6		) < 1e-3







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Optional[int]						  =     self.scheduler_classes[0]
										lowerCamelCase						:Any						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps	,					device=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     torch.manual_seed(0		)

										lowerCamelCase						:str						  =     self.dummy_model()
										lowerCamelCase						:int						  =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										lowerCamelCase						:List[Any]						  =     sample.to(__snake_case		)

										for t in scheduler.timesteps:
													lowerCamelCase						:Union[str, Any]						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:int						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:Any						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:Tuple						  =     output.prev_sample

										lowerCamelCase						:List[str]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:Tuple						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 10.0807		) < 1e-2
										assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Union[str, Any]						  =     self.scheduler_classes[0]
										lowerCamelCase						:Tuple						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case	,					use_karras_sigmas=__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps	,					device=__snake_case		)

										lowerCamelCase						:int						  =     torch.manual_seed(0		)

										lowerCamelCase						:List[str]						  =     self.dummy_model()
										lowerCamelCase						:Optional[Any]						  =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										lowerCamelCase						:Optional[int]						  =     sample.to(__snake_case		)

										for t in scheduler.timesteps:
													lowerCamelCase						:str						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:Optional[int]						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:int						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:Optional[Any]						  =     output.prev_sample

										lowerCamelCase						:str						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:List[str]						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 124.52299499511719		) < 1e-2
										assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3		) < 1e-3




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     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:List[Any]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     -1

										lowerCamelCase						:List[str]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.decode(greedy_ids[0]		)

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

										lowerCamelCase						:List[str]						  =     TextIteratorStreamer(__snake_case		)
										lowerCamelCase						:List[str]						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()
										lowerCamelCase						:Any						  =     ''''''
										for new_text in streamer:
													streamer_text += new_text

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

										lowerCamelCase						:Optional[Any]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[str]						  =     greedy_ids[:, input_ids.shape[1] :]
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.decode(new_greedy_ids[0]		)

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# 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						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Union[str, Any]						  =     torch.ones((1, 5)	,					device=__snake_case		).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
													lowerCamelCase						:Dict						  =     TextStreamer(__snake_case	,					skip_special_tokens=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=1	,					do_sample=__snake_case	,					streamer=__snake_case		)

										# 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						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

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



