
'''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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			tempfile.mkdtemp()

												_lowerCAmelCase  						=			[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												_lowerCAmelCase  						=			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  						=			{
												    """do_resize""": True,
												    """size""": 20,
												    """do_center_crop""": True,
												    """crop_size""": 18,
												    """do_normalize""": True,
												    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
												    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
												}
												_lowerCAmelCase  						=			os.path.join(self.tmpdirname   ,       _lowercase				)
												with open(self.image_processor_file   ,       """w"""   ,       encoding="""utf-8"""				) as fp:
																		json.dump(_lowercase   ,       _lowercase				)




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												shutil.rmtree(self.tmpdirname				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[np.random.randint(255   ,       size=(3, 30, 400)   ,       dtype=np.uinta				)]
												_lowerCAmelCase  						=			[Image.fromarray(np.moveaxis(_lowercase   ,       0   ,       -1				)				) for x in image_inputs]
												return image_inputs




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_tokenizer()
												_lowerCAmelCase  						=			self.get_rust_tokenizer()
												_lowerCAmelCase  						=			self.get_image_processor()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)
												processor_slow.save_pretrained(self.tmpdirname				)
												_lowerCAmelCase  						=			AlignProcessor.from_pretrained(self.tmpdirname   ,       use_fast=_lowercase				)

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)
												processor_fast.save_pretrained(self.tmpdirname				)
												_lowerCAmelCase  						=			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   ,       _lowercase				)
												self.assertIsInstance(processor_fast.tokenizer   ,       _lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AlignProcessor(tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()				)
												processor.save_pretrained(self.tmpdirname				)

												_lowerCAmelCase  						=			self.get_tokenizer(bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""				)
												_lowerCAmelCase  						=			self.get_image_processor(do_normalize=_lowercase   ,       padding_value=1.0				)

												_lowerCAmelCase  						=			AlignProcessor.from_pretrained(
												    self.tmpdirname   ,       bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""   ,       do_normalize=_lowercase   ,       padding_value=1.0				)

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

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_image_processor()
												_lowerCAmelCase  						=			self.get_tokenizer()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)

												_lowerCAmelCase  						=			self.prepare_image_inputs()

												_lowerCAmelCase  						=			image_processor(_lowercase   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_image_processor()
												_lowerCAmelCase  						=			self.get_tokenizer()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)

												_lowerCAmelCase  						=			"""lower newer"""

												_lowerCAmelCase  						=			processor(text=_lowercase				)

												_lowerCAmelCase  						=			tokenizer(_lowercase   ,       padding="""max_length"""   ,       max_length=64				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_image_processor()
												_lowerCAmelCase  						=			self.get_tokenizer()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)

												_lowerCAmelCase  						=			"""lower newer"""
												_lowerCAmelCase  						=			self.prepare_image_inputs()

												_lowerCAmelCase  						=			processor(text=_lowercase   ,       images=_lowercase				)

												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(_lowercase				):
																		processor()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_image_processor()
												_lowerCAmelCase  						=			self.get_tokenizer()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)

												_lowerCAmelCase  						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												_lowerCAmelCase  						=			processor.batch_decode(_lowercase				)
												_lowerCAmelCase  						=			tokenizer.batch_decode(_lowercase				)

												self.assertListEqual(_lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_image_processor()
												_lowerCAmelCase  						=			self.get_tokenizer()

												_lowerCAmelCase  						=			AlignProcessor(tokenizer=_lowercase   ,       image_processor=_lowercase				)

												_lowerCAmelCase  						=			"""lower newer"""
												_lowerCAmelCase  						=			self.prepare_image_inputs()

												_lowerCAmelCase  						=			processor(text=_lowercase   ,       images=_lowercase				)

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


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
