



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				'''ZinengTang/tvlt-base'''
           __a						       =				tempfile.mkdtemp()



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_feature_extractor()

           __a						       =				TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE     ,		feature_extractor=__SCREAMING_SNAKE_CASE)
           processor.save_pretrained(self.tmpdirname)
           __a						       =				TvltProcessor.from_pretrained(self.tmpdirname)

           self.assertIsInstance(processor.feature_extractor     ,		__SCREAMING_SNAKE_CASE)
           self.assertIsInstance(processor.image_processor     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_feature_extractor()

           __a						       =				TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE     ,		feature_extractor=__SCREAMING_SNAKE_CASE)

           __a						       =				np.ones([12_000])

           __a						       =				feature_extractor(__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')
           __a						       =				processor(audio=__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')

           for key in audio_dict.keys():
               self.assertAlmostEqual(audio_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1E-2)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_feature_extractor()

           __a						       =				TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE     ,		feature_extractor=__SCREAMING_SNAKE_CASE)

           __a						       =				np.ones([3, 224, 224])

           __a						       =				image_processor(__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')
           __a						       =				processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''np''')

           for key in image_dict.keys():
               self.assertAlmostEqual(image_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1E-2)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_feature_extractor()

           __a						       =				TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE     ,		feature_extractor=__SCREAMING_SNAKE_CASE)

           __a						       =				np.ones([12_000])
           __a						       =				np.ones([3, 224, 224])

           __a						       =				processor(audio=__SCREAMING_SNAKE_CASE     ,		images=__SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())     ,		['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask'''])

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE):
               processor()





       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.get_image_processor()
           __a						       =				self.get_feature_extractor()

           __a						       =				TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE     ,		feature_extractor=__SCREAMING_SNAKE_CASE)

           self.assertListEqual(
               processor.model_input_names     ,		image_processor.model_input_names + feature_extractor.model_input_names     ,		msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,		)












import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=__lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

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

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

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

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

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

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					if i + 1 == timesteps.shape[0]:
						lowerCamelCase__								=		None
					else:
						lowerCamelCase__								=		timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



