





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			tempfile.mkdtemp()

          # fmt: off
          __A		:					int						=			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
          # fmt: on
          __A		:					Optional[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]))

          __A		:					Any						=			{
              '''do_resize''': True,
              '''size''': {'''height''': 18, '''width''': 18},
              '''do_normalize''': True,
              '''image_mean''': [0.5, 0.5, 0.5],
              '''image_std''': [0.5, 0.5, 0.5],
          }
          __A		:					Union[str, Any]						=			os.path.join(self.tmpdirname		,      _lowerCamelCase)
          with open(self.image_processor_file		,      'w'		,      encoding='utf-8') as fp:
               json.dump(_lowerCamelCase		,      _lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **_lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			[np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta)]

          __A		:					Optional[Any]						=			[Image.fromarray(np.moveaxis(_lowerCamelCase		,      0		,      -1)) for x in image_inputs]

          return image_inputs


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.get_tokenizer()
          __A		:					Union[str, Any]						=			self.get_image_processor()

          __A		:					str						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

          processor.save_pretrained(self.tmpdirname)
          __A		:					Union[str, Any]						=			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)

          self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer.get_vocab())
          self.assertIsInstance(processor.tokenizer		,      (BertTokenizer, BertTokenizerFast))

          self.assertEqual(processor.image_processor.to_json_string()		,      image_processor.to_json_string())
          self.assertIsInstance(processor.image_processor		,      _lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A		:					Dict						=			self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)')
          __A		:					List[str]						=			self.get_image_processor(do_normalize=_lowerCamelCase		,      padding_value=1.0)

          __A		:					List[Any]						=			VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname		,      bos_token='(BOS)'		,      eos_token='(EOS)'		,      do_normalize=_lowerCamelCase		,      padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer		,      (BertTokenizer, BertTokenizerFast))

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			self.get_image_processor()
          __A		:					Dict						=			self.get_tokenizer()

          __A		:					Tuple						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

          __A		:					Dict						=			self.prepare_image_inputs()

          __A		:					str						=			image_processor(_lowerCamelCase		,      return_tensors='np')
          __A		:					int						=			processor(images=_lowerCamelCase		,      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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.get_image_processor()
          __A		:					Optional[int]						=			self.get_tokenizer()

          __A		:					Any						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

          __A		:					str						=			'''lower newer'''

          __A		:					List[str]						=			processor(text=_lowerCamelCase)

          __A		:					Optional[Any]						=			tokenizer(_lowerCamelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.get_image_processor()
          __A		:					int						=			self.get_tokenizer()

          __A		:					Optional[int]						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

          __A		:					Union[str, Any]						=			'''lower newer'''
          __A		:					Dict						=			self.prepare_image_inputs()

          __A		:					Optional[int]						=			processor(text=_lowerCamelCase		,      images=_lowerCamelCase)

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

          # test if it raises when no input is passed
          with self.assertRaises(_lowerCamelCase):
               processor()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.get_image_processor()
          __A		:					Optional[int]						=			self.get_tokenizer()

          __A		:					str						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

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

          __A		:					Optional[Any]						=			processor.batch_decode(_lowerCamelCase)
          __A		:					List[Any]						=			tokenizer.batch_decode(_lowerCamelCase)

          self.assertListEqual(_lowerCamelCase		,      _lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			self.get_image_processor()
          __A		:					Dict						=			self.get_tokenizer()

          __A		:					str						=			VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase		,      image_processor=_lowerCamelCase)

          __A		:					Dict						=			'''lower newer'''
          __A		:					Dict						=			self.prepare_image_inputs()

          __A		:					List[Any]						=			processor(text=_lowerCamelCase		,      images=_lowerCamelCase)

          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 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			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       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

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

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		: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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





