

'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )





@require_vision
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







    def    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       List[Any]								=tempfile.mkdtemp()

          lowercase   :       Optional[Any]								=BlipImageProcessor()
          lowercase   :       List[str]								=GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''      )
          lowercase   :       Optional[int]								=BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

          lowercase   :       Tuple								=InstructBlipProcessor(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )

          processor.save_pretrained(self.tmpdirname      )







    def    lowerCamelCase_  (					self    :		List[Any]		,				**UpperCAmelCase__    :		Optional[int]      ):
          '''simple docstring'''
          return AutoProcessor.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      ).tokenizer







    def    lowerCamelCase_  (					self    :		List[str]		,				**UpperCAmelCase__    :		Any      ):
          '''simple docstring'''
          return AutoProcessor.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      ).image_processor







    def    lowerCamelCase_  (					self    :		str		,				**UpperCAmelCase__    :		List[Any]      ):
          '''simple docstring'''
          return AutoProcessor.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      ).qformer_tokenizer







    def    lowerCamelCase_  (					self    :		List[str]      ):
          '''simple docstring'''
          shutil.rmtree(self.tmpdirname      )







    def    lowerCamelCase_  (					self    :		Any      ):
          '''simple docstring'''
          lowercase   :       int								=[np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta      )]

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

          return image_inputs







    def    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       Tuple								=InstructBlipProcessor(
              tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
          processor.save_pretrained(self.tmpdirname      )

          lowercase   :       Dict								=self.get_tokenizer(bos_token='''(BOS)'''		,				eos_token='''(EOS)'''      )
          lowercase   :       Tuple								=self.get_image_processor(do_normalize=UpperCAmelCase__		,				padding_value=1.0      )

          lowercase   :       Dict								=InstructBlipProcessor.from_pretrained(
              self.tmpdirname		,				bos_token='''(BOS)'''		,				eos_token='''(EOS)'''		,				do_normalize=UpperCAmelCase__		,				padding_value=1.0      )

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

          self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()      )
          self.assertIsInstance(processor.image_processor		,				UpperCAmelCase__      )
          self.assertIsInstance(processor.qformer_tokenizer		,				UpperCAmelCase__      )







    def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
          '''simple docstring'''
          lowercase   :       Tuple								=self.get_image_processor()
          lowercase   :       Optional[int]								=self.get_tokenizer()
          lowercase   :       Union[str, Any]								=self.get_qformer_tokenizer()

          lowercase   :       int								=InstructBlipProcessor(
              tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__		,				qformer_tokenizer=UpperCAmelCase__      )

          lowercase   :       Optional[Any]								=self.prepare_image_inputs()

          lowercase   :       Any								=image_processor(UpperCAmelCase__		,				return_tensors='''np'''      )
          lowercase   :       Any								=processor(images=UpperCAmelCase__		,				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    lowerCamelCase_  (					self    :		Any      ):
          '''simple docstring'''
          lowercase   :       Optional[Any]								=self.get_image_processor()
          lowercase   :       str								=self.get_tokenizer()
          lowercase   :       Any								=self.get_qformer_tokenizer()

          lowercase   :       int								=InstructBlipProcessor(
              tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__		,				qformer_tokenizer=UpperCAmelCase__      )

          lowercase   :       Any								='''lower newer'''

          lowercase   :       List[Any]								=processor(text=UpperCAmelCase__      )

          lowercase   :       int								=tokenizer(UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__      )
          lowercase   :       Optional[Any]								=qformer_tokenizer(UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__      )

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

          for key in encoded_tokens_qformer.keys():
                self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor['''qformer_''' + key]      )







    def    lowerCamelCase_  (					self    :		Any      ):
          '''simple docstring'''
          lowercase   :       Optional[int]								=self.get_image_processor()
          lowercase   :       Union[str, Any]								=self.get_tokenizer()
          lowercase   :       int								=self.get_qformer_tokenizer()

          lowercase   :       List[Any]								=InstructBlipProcessor(
              tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__		,				qformer_tokenizer=UpperCAmelCase__      )

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

          lowercase   :       Union[str, Any]								=processor(text=UpperCAmelCase__		,				images=UpperCAmelCase__      )

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

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







    def    lowerCamelCase_  (					self    :		str      ):
          '''simple docstring'''
          lowercase   :       int								=self.get_image_processor()
          lowercase   :       int								=self.get_tokenizer()
          lowercase   :       List[Any]								=self.get_qformer_tokenizer()

          lowercase   :       List[str]								=InstructBlipProcessor(
              tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__		,				qformer_tokenizer=UpperCAmelCase__      )

          lowercase   :       Any								=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase   :       List[Any]								=processor.batch_decode(UpperCAmelCase__      )
          lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )

          self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







    def    lowerCamelCase_  (					self    :		Optional[int]      ):
          '''simple docstring'''
          lowercase   :       Any								=self.get_image_processor()
          lowercase   :       Any								=self.get_tokenizer()
          lowercase   :       List[str]								=self.get_qformer_tokenizer()

          lowercase   :       Union[str, Any]								=InstructBlipProcessor(
              tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__		,				qformer_tokenizer=UpperCAmelCase__      )

          lowercase   :       str								='''lower newer'''
          lowercase   :       List[Any]								=self.prepare_image_inputs()

          lowercase   :       Optional[int]								=processor(text=UpperCAmelCase__		,				images=UpperCAmelCase__      )

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








'''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 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

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

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







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

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

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).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
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





