

'''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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    tempfile.mkdtemp()

      __magic_name__						       =    BlipImageProcessor()
      __magic_name__						       =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''      )
      __magic_name__						       =    BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

      __magic_name__						       =    InstructBlipProcessor(__UpperCAmelCase			,				__UpperCAmelCase			,				__UpperCAmelCase      )

      processor.save_pretrained(self.tmpdirname      )



 def 			snake_case__  (   self		:   Optional[Any]			,				**a__		:   Tuple      ):
      return AutoProcessor.from_pretrained(self.tmpdirname			,				**__UpperCAmelCase      ).tokenizer



 def 			snake_case__  (   self		:   int			,				**a__		:   str      ):
      return AutoProcessor.from_pretrained(self.tmpdirname			,				**__UpperCAmelCase      ).image_processor



 def 			snake_case__  (   self		:   Optional[Any]			,				**a__		:   Any      ):
      return AutoProcessor.from_pretrained(self.tmpdirname			,				**__UpperCAmelCase      ).qformer_tokenizer



 def 			snake_case__  (   self		:   str      ):
      shutil.rmtree(self.tmpdirname      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    [np.random.randint(255			,				size=(3, 30, 400)			,				dtype=np.uinta      )]

      __magic_name__						       =    [Image.fromarray(np.moveaxis(__UpperCAmelCase			,				0			,				-1      )      ) for x in image_inputs]

      return image_inputs



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=self.get_tokenizer()			,				image_processor=self.get_image_processor()			,				qformer_tokenizer=self.get_qformer_tokenizer()			,				)
      processor.save_pretrained(self.tmpdirname      )

      __magic_name__						       =    self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''      )
      __magic_name__						       =    self.get_image_processor(do_normalize=__UpperCAmelCase			,				padding_value=1.0      )

      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_qformer_tokenizer()

      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=__UpperCAmelCase			,				image_processor=__UpperCAmelCase			,				qformer_tokenizer=__UpperCAmelCase      )

      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    image_processor(__UpperCAmelCase			,				return_tensors='''np'''      )
      __magic_name__						       =    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 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_qformer_tokenizer()

      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=__UpperCAmelCase			,				image_processor=__UpperCAmelCase			,				qformer_tokenizer=__UpperCAmelCase      )

      __magic_name__						       =    '''lower newer'''

      __magic_name__						       =    processor(text=__UpperCAmelCase      )

      __magic_name__						       =    tokenizer(__UpperCAmelCase			,				return_token_type_ids=__UpperCAmelCase      )
      __magic_name__						       =    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 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_qformer_tokenizer()

      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=__UpperCAmelCase			,				image_processor=__UpperCAmelCase			,				qformer_tokenizer=__UpperCAmelCase      )

      __magic_name__						       =    '''lower newer'''
      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    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 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_qformer_tokenizer()

      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=__UpperCAmelCase			,				image_processor=__UpperCAmelCase			,				qformer_tokenizer=__UpperCAmelCase      )

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

      __magic_name__						       =    processor.batch_decode(__UpperCAmelCase      )
      __magic_name__						       =    tokenizer.batch_decode(__UpperCAmelCase      )

      self.assertListEqual(__UpperCAmelCase			,				__UpperCAmelCase      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.get_image_processor()
      __magic_name__						       =    self.get_tokenizer()
      __magic_name__						       =    self.get_qformer_tokenizer()

      __magic_name__						       =    InstructBlipProcessor(
          tokenizer=__UpperCAmelCase			,				image_processor=__UpperCAmelCase			,				qformer_tokenizer=__UpperCAmelCase      )

      __magic_name__						       =    '''lower newer'''
      __magic_name__						       =    self.prepare_image_inputs()

      __magic_name__						       =    processor(text=__UpperCAmelCase			,				images=__UpperCAmelCase      )

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









'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






