

'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_snake_case         =  False


class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=32  ):





      """simple docstring"""





      set_seed(0  )
      _lowercase      :				List[str]      					=       UNetaDModel(sample_size=_UpperCamelCase      ,    in_channels=3      ,    out_channels=3  )
      _lowercase      :				str      					=       torch.optim.SGD(model.parameters()      ,    lr=0.0_0_0_1  )
      return model, optimizer






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
      _lowercase      :				Tuple      					=       DDPMScheduler(
          num_train_timesteps=1000      ,    beta_start=0.0_0_0_1      ,    beta_end=0.0_2      ,    beta_schedule="linear"      ,    clip_sample=_UpperCamelCase      ,    )
      _lowercase      :				Any      					=       DDIMScheduler(
          num_train_timesteps=1000      ,    beta_start=0.0_0_0_1      ,    beta_end=0.0_2      ,    beta_schedule="linear"      ,    clip_sample=_UpperCamelCase      ,    )

      assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

      # shared batches for DDPM and DDIM
      set_seed(0  )
      _lowercase      :				List[Any]      					=       [torch.randn((4, 3, 32, 32)  ).clip(-1      ,    1  ).to(_UpperCamelCase  ) for _ in range(4  )]
      _lowercase      :				List[Any]      					=       [torch.randn((4, 3, 32, 32)  ).to(_UpperCamelCase  ) for _ in range(4  )]
      _lowercase      :				Any      					=       [torch.randint(0      ,    1000      ,    (4,)  ).long().to(_UpperCamelCase  ) for _ in range(4  )]

      # train with a DDPM scheduler
      _lowercase					,				_lowercase      :				Union[str, Any]      					=       self.get_model_optimizer(resolution=32  )
      model.train().to(_UpperCamelCase  )
      for i in range(4  ):
         optimizer.zero_grad()
         _lowercase      :				List[Any]      					=       ddpm_scheduler.add_noise(clean_images[i]      ,    noise[i]      ,    timesteps[i]  )
         _lowercase      :				Any      					=       model(_UpperCamelCase      ,    timesteps[i]  ).sample
         _lowercase      :				int      					=       torch.nn.functional.mse_loss(_UpperCamelCase      ,    noise[i]  )
         loss.backward()
         optimizer.step()
      del model, optimizer

      # recreate the model and optimizer, and retry with DDIM
      _lowercase					,				_lowercase      :				Any      					=       self.get_model_optimizer(resolution=32  )
      model.train().to(_UpperCamelCase  )
      for i in range(4  ):
         optimizer.zero_grad()
         _lowercase      :				Union[str, Any]      					=       ddim_scheduler.add_noise(clean_images[i]      ,    noise[i]      ,    timesteps[i]  )
         _lowercase      :				List[str]      					=       model(_UpperCamelCase      ,    timesteps[i]  ).sample
         _lowercase      :				int      					=       torch.nn.functional.mse_loss(_UpperCamelCase      ,    noise[i]  )
         loss.backward()
         optimizer.step()
      del model, optimizer

      self.assertTrue(torch.allclose(_UpperCamelCase      ,    _UpperCamelCase      ,    atol=1E-5  )  )
      self.assertTrue(torch.allclose(_UpperCamelCase      ,    _UpperCamelCase      ,    atol=1E-5  )  )









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






