





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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   List[str]           =  tempfile.mkdtemp()

							snake_case_						:   List[str]           =  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							snake_case_						:   Any           =  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]   )   )

							snake_case_						:   List[Any]           =  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
							    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
							}
							snake_case_						:   Optional[Any]           =  os.path.join(self.tmpdirname ,							__magic_name__   )
							with open(self.image_processor_file ,							'''w''' ,							encoding='''utf-8'''   ) as fp:
											json.dump(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       str:


							'''simple docstring'''



							return BertTokenizer.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Dict:


							'''simple docstring'''



							return BertTokenizerFast.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Any:


							'''simple docstring'''



							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							shutil.rmtree(self.tmpdirname   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  [np.random.randint(255 ,							size=(3, 30, 400) ,							dtype=np.uinta   )]
							snake_case_						:   Dict           =  [Image.fromarray(np.moveaxis(__magic_name__ ,							0 ,							-1   )   ) for x in image_inputs]
							return image_inputs






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  self.get_tokenizer()
							snake_case_						:   List[Any]           =  self.get_rust_tokenizer()
							snake_case_						:   List[Any]           =  self.get_image_processor()

							snake_case_						:   int           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )
							processor_slow.save_pretrained(self.tmpdirname   )
							snake_case_						:   List[str]           =  AlignProcessor.from_pretrained(self.tmpdirname ,							use_fast=__magic_name__   )

							snake_case_						:   Optional[int]           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )
							processor_fast.save_pretrained(self.tmpdirname   )
							snake_case_						:   Union[str, Any]           =  AlignProcessor.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 ,							__magic_name__   )
							self.assertIsInstance(processor_fast.tokenizer ,							__magic_name__   )

							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 ,							__magic_name__   )
							self.assertIsInstance(processor_fast.image_processor ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  AlignProcessor(tokenizer=self.get_tokenizer() ,							image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							snake_case_						:   str           =  self.get_tokenizer(bos_token='''(BOS)''' ,							eos_token='''(EOS)'''   )
							snake_case_						:   Optional[int]           =  self.get_image_processor(do_normalize=__magic_name__ ,							padding_value=1.0   )

							snake_case_						:   Optional[Any]           =  AlignProcessor.from_pretrained(
							    self.tmpdirname ,							bos_token='''(BOS)''' ,							eos_token='''(EOS)''' ,							do_normalize=__magic_name__ ,							padding_value=1.0   )

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

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






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Any           =  self.get_image_processor()
							snake_case_						:   Any           =  self.get_tokenizer()

							snake_case_						:   Optional[Any]           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )

							snake_case_						:   Optional[int]           =  self.prepare_image_inputs()

							snake_case_						:   Tuple           =  image_processor(__magic_name__ ,							return_tensors='''np'''   )
							snake_case_						:   Union[str, Any]           =  processor(images=__magic_name__ ,							return_tensors='''np'''   )

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






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.get_image_processor()
							snake_case_						:   Dict           =  self.get_tokenizer()

							snake_case_						:   int           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )

							snake_case_						:   int           =  '''lower newer'''

							snake_case_						:   Optional[int]           =  processor(text=__magic_name__   )

							snake_case_						:   Optional[int]           =  tokenizer(__magic_name__ ,							padding='''max_length''' ,							max_length=64   )

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






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  self.get_image_processor()
							snake_case_						:   Optional[int]           =  self.get_tokenizer()

							snake_case_						:   Dict           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )

							snake_case_						:   Tuple           =  '''lower newer'''
							snake_case_						:   int           =  self.prepare_image_inputs()

							snake_case_						:   Any           =  processor(text=__magic_name__ ,							images=__magic_name__   )

							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(__magic_name__   ):
											processor()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self.get_image_processor()
							snake_case_						:   int           =  self.get_tokenizer()

							snake_case_						:   List[Any]           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )

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

							snake_case_						:   str           =  processor.batch_decode(__magic_name__   )
							snake_case_						:   Optional[Any]           =  tokenizer.batch_decode(__magic_name__   )

							self.assertListEqual(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.get_image_processor()
							snake_case_						:   List[str]           =  self.get_tokenizer()

							snake_case_						:   Any           =  AlignProcessor(tokenizer=__magic_name__ ,							image_processor=__magic_name__   )

							snake_case_						:   int           =  '''lower newer'''
							snake_case_						:   Optional[int]           =  self.prepare_image_inputs()

							snake_case_						:   Optional[Any]           =  processor(text=__magic_name__ ,							images=__magic_name__   )

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







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

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