
import time
import unittest

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

from ..test_modeling_common import ids_tensor


if is_torch_available():
							import torch

							from transformers.generation import (
							    MaxLengthCriteria,
							    MaxNewTokensCriteria,
							    MaxTimeCriteria,
							    StoppingCriteriaList,
							    validate_stopping_criteria,
							)







@require_torch
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self   ,A__):
												lowercase							=						3
												lowercase							=						2_5_0

												lowercase							=						ids_tensor((batch_size, length)   ,UpperCamelCase__)
												lowercase							=						torch.ones((batch_size, length)   ,device=UpperCamelCase__   ,dtype=torch.float) / length
												return input_ids, scores




							def  A__      (				self):
												lowercase	,						lowercase							=						self._get_tensors(5)

												lowercase							=						StoppingCriteriaList(
												    [
												        MaxLengthCriteria(max_length=1_0),
												        MaxTimeCriteria(max_time=0.1),
												    ])

												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(9)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(1_0)
												self.assertTrue(criteria(UpperCamelCase__   ,UpperCamelCase__))




							def  A__      (				self):
												lowercase							=						MaxLengthCriteria(max_length=1_0)

												lowercase	,						lowercase							=						self._get_tensors(5)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(9)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(1_0)
												self.assertTrue(criteria(UpperCamelCase__   ,UpperCamelCase__))




							def  A__      (				self):
												lowercase							=						MaxNewTokensCriteria(start_length=5   ,max_new_tokens=5)

												lowercase	,						lowercase							=						self._get_tensors(5)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(9)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase	,						lowercase							=						self._get_tensors(1_0)
												self.assertTrue(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase							=						StoppingCriteriaList([criteria])
												self.assertEqual(criteria_list.max_length   ,1_0)




							def  A__      (				self):
												lowercase	,						lowercase							=						self._get_tensors(5)

												lowercase							=						MaxTimeCriteria(max_time=0.1)
												self.assertFalse(criteria(UpperCamelCase__   ,UpperCamelCase__))

												lowercase							=						MaxTimeCriteria(max_time=0.1   ,initial_timestamp=time.time() - 0.2)
												self.assertTrue(criteria(UpperCamelCase__   ,UpperCamelCase__))





							def  A__      (				self):
												validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0)])   ,1_0)

												with self.assertWarns(UpperCamelCase__):
																	validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0)])   ,1_1)

												lowercase							=						validate_stopping_criteria(StoppingCriteriaList()   ,1_1)

												self.assertEqual(len(UpperCamelCase__)   ,1)










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




