





"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
		"""simple docstring"""




		def 					__lowercase  (     self  :str			):
									__lowerCamelCase      :      Union[str, Any]         =tempfile.mkdtemp()

									__lowerCamelCase      :      Optional[Any]         =[
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """的""",
									    """价""",
									    """格""",
									    """是""",
									    """15""",
									    """便""",
									    """alex""",
									    """##andra""",
									    """，""",
									    """。""",
									    """-""",
									    """t""",
									    """shirt""",
									]
									__lowerCamelCase      :      Optional[int]         =os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['''vocab_file''']			)
									with open(self.vocab_file						,    '''w'''						,    encoding='''utf-8'''			) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)

									__lowerCamelCase      :      str         ={
									    """do_resize""": True,
									    """size""": {"""height""": 224, """width""": 224},
									    """do_center_crop""": True,
									    """crop_size""": {"""height""": 18, """width""": 18},
									    """do_normalize""": True,
									    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
									    """image_std""": [0.26862954, 0.26130258, 0.27577711],
									    """do_convert_rgb""": True,
									}
									__lowerCamelCase      :      Tuple         =os.path.join(self.tmpdirname						,    __a			)
									with open(self.image_processor_file						,    '''w'''						,    encoding='''utf-8'''			) as fp:
																json.dump(__a						,    __a			)




		def 					__lowercase  (     self  :int						,    **__lowercase  :str			):
									return BertTokenizer.from_pretrained(self.tmpdirname						,    **__a			)




		def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
									return BertTokenizerFast.from_pretrained(self.tmpdirname						,    **__a			)




		def 					__lowercase  (     self  :Tuple						,    **__lowercase  :List[Any]			):
									return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname						,    **__a			)




		def 					__lowercase  (     self  :int			):
									shutil.rmtree(self.tmpdirname			)




		def 					__lowercase  (     self  :Any			):
									__lowerCamelCase      :      Any         =[np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta			)]

									__lowerCamelCase      :      List[Any]         =[Image.fromarray(np.moveaxis(__a						,    0						,    -1			)			) for x in image_inputs]

									return image_inputs




		def 					__lowercase  (     self  :Dict			):
									__lowerCamelCase      :      List[Any]         =self.get_tokenizer()
									__lowerCamelCase      :      Optional[Any]         =self.get_rust_tokenizer()
									__lowerCamelCase      :      Optional[Any]         =self.get_image_processor()

									__lowerCamelCase      :      Any         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)
									processor_slow.save_pretrained(self.tmpdirname			)
									__lowerCamelCase      :      Tuple         =ChineseCLIPProcessor.from_pretrained(self.tmpdirname						,    use_fast=__a			)

									__lowerCamelCase      :      Tuple         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)
									processor_fast.save_pretrained(self.tmpdirname			)
									__lowerCamelCase      :      List[str]         =ChineseCLIPProcessor.from_pretrained(self.tmpdirname			)

									self.assertEqual(processor_slow.tokenizer.get_vocab()						,    tokenizer_slow.get_vocab()			)
									self.assertEqual(processor_fast.tokenizer.get_vocab()						,    tokenizer_fast.get_vocab()			)
									self.assertEqual(tokenizer_slow.get_vocab()						,    tokenizer_fast.get_vocab()			)
									self.assertIsInstance(processor_slow.tokenizer						,    __a			)
									self.assertIsInstance(processor_fast.tokenizer						,    __a			)

									self.assertEqual(processor_slow.image_processor.to_json_string()						,    image_processor.to_json_string()			)
									self.assertEqual(processor_fast.image_processor.to_json_string()						,    image_processor.to_json_string()			)
									self.assertIsInstance(processor_slow.image_processor						,    __a			)
									self.assertIsInstance(processor_fast.image_processor						,    __a			)




		def 					__lowercase  (     self  :List[str]			):
									__lowerCamelCase      :      List[Any]         =ChineseCLIPProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()			)
									processor.save_pretrained(self.tmpdirname			)

									__lowerCamelCase      :      Union[str, Any]         =self.get_tokenizer(cls_token='''(CLS)'''						,    sep_token='''(SEP)'''			)
									__lowerCamelCase      :      int         =self.get_image_processor(do_normalize=__a			)

									__lowerCamelCase      :      Tuple         =ChineseCLIPProcessor.from_pretrained(
									    self.tmpdirname						,    cls_token='''(CLS)'''						,    sep_token='''(SEP)'''						,    do_normalize=__a			)

									self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()			)
									self.assertIsInstance(processor.tokenizer						,    __a			)

									self.assertEqual(processor.image_processor.to_json_string()						,    image_processor_add_kwargs.to_json_string()			)
									self.assertIsInstance(processor.image_processor						,    __a			)




		def 					__lowercase  (     self  :List[Any]			):
									__lowerCamelCase      :      Optional[int]         =self.get_image_processor()
									__lowerCamelCase      :      Union[str, Any]         =self.get_tokenizer()

									__lowerCamelCase      :      Any         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)

									__lowerCamelCase      :      Union[str, Any]         =self.prepare_image_inputs()

									__lowerCamelCase      :      Optional[int]         =image_processor(__a						,    return_tensors='''np'''			)
									__lowerCamelCase      :      List[Any]         =processor(images=__a						,    return_tensors='''np'''			)

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




		def 					__lowercase  (     self  :Union[str, Any]			):
									__lowerCamelCase      :      Dict         =self.get_image_processor()
									__lowerCamelCase      :      Any         =self.get_tokenizer()

									__lowerCamelCase      :      Dict         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)

									__lowerCamelCase      :      Tuple         ="""Alexandra，T-shirt的价格是15便士。"""

									__lowerCamelCase      :      Tuple         =processor(text=__a			)

									__lowerCamelCase      :      str         =tokenizer(__a			)

									for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]			)




		def 					__lowercase  (     self  :Optional[int]			):
									__lowerCamelCase      :      List[Any]         =self.get_image_processor()
									__lowerCamelCase      :      List[Any]         =self.get_tokenizer()

									__lowerCamelCase      :      Optional[int]         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)

									__lowerCamelCase      :      Tuple         ="""Alexandra，T-shirt的价格是15便士。"""
									__lowerCamelCase      :      List[Any]         =self.prepare_image_inputs()

									__lowerCamelCase      :      int         =processor(text=__a						,    images=__a			)

									self.assertListEqual(list(inputs.keys()			)						,    ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']			)

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




		def 					__lowercase  (     self  :Tuple			):
									__lowerCamelCase      :      Dict         =self.get_image_processor()
									__lowerCamelCase      :      int         =self.get_tokenizer()

									__lowerCamelCase      :      str         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)

									__lowerCamelCase      :      int         =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									__lowerCamelCase      :      List[Any]         =processor.batch_decode(__a			)
									__lowerCamelCase      :      Union[str, Any]         =tokenizer.batch_decode(__a			)

									self.assertListEqual(__a						,    __a			)




		def 					__lowercase  (     self  :Union[str, Any]			):
									__lowerCamelCase      :      int         =self.get_image_processor()
									__lowerCamelCase      :      List[Any]         =self.get_tokenizer()

									__lowerCamelCase      :      List[str]         =ChineseCLIPProcessor(tokenizer=__a						,    image_processor=__a			)

									__lowerCamelCase      :      List[str]         ="""Alexandra，T-shirt的价格是15便士。"""
									__lowerCamelCase      :      Union[str, Any]         =self.prepare_image_inputs()

									__lowerCamelCase      :      Dict         =processor(text=__a						,    images=__a			)

									self.assertListEqual(list(inputs.keys()			)						,    processor.model_input_names			)











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




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




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




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




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

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




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




