

'''simple docstring'''



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      ["image_processor", "tokenizer"]
 lowerCAmelCase__  					=      "OwlViTImageProcessor"
 lowerCAmelCase__  					=      ("CLIPTokenizer", "CLIPTokenizerFast")
 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Any=None					,							__SCREAMING_SNAKE_CASE					:       str=None					,							**__SCREAMING_SNAKE_CASE					:       int			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""					,							__SCREAMING_SNAKE_CASE					,							)
           __SCREAMING_SNAKE_CASE     =      kwargs.pop("""feature_extractor"""			)

      __SCREAMING_SNAKE_CASE     =      image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""			)
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""			)

      super().__init__(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def __call__(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=None					,							__SCREAMING_SNAKE_CASE					:       int=None					,							__SCREAMING_SNAKE_CASE					:       List[Any]="max_length"					,							__SCREAMING_SNAKE_CASE					:       int="np"					,							**__SCREAMING_SNAKE_CASE					:       List[str]			)  -> str:


      """simple docstring"""




      if text is None and query_images is None and images is None:
           raise ValueError(
               """You have to specify at least one text or query image or image. All three cannot be none."""			)

      if text is not None:
           if isinstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			) or (isinstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			) and not isinstance(text[0]					,							__SCREAMING_SNAKE_CASE			)):
                __SCREAMING_SNAKE_CASE     =      [self.tokenizer(__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)]

           elif isinstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			) and isinstance(text[0]					,							__SCREAMING_SNAKE_CASE			):
                __SCREAMING_SNAKE_CASE     =      []

                # Maximum number of queries across batch
                __SCREAMING_SNAKE_CASE     =      max([len(__SCREAMING_SNAKE_CASE			) for t in text]			)

                # Pad all batch samples to max number of text queries
                for t in text:
                     if len(__SCREAMING_SNAKE_CASE			) != max_num_queries:
                          __SCREAMING_SNAKE_CASE     =      t + [""" """] * (max_num_queries - len(__SCREAMING_SNAKE_CASE			))

                     __SCREAMING_SNAKE_CASE     =      self.tokenizer(__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                     encodings.append(__SCREAMING_SNAKE_CASE			)
           else:
                raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""			)

           if return_tensors == "np":
                __SCREAMING_SNAKE_CASE     =      np.concatenate([encoding["""input_ids"""] for encoding in encodings]					,							axis=0			)
                __SCREAMING_SNAKE_CASE     =      np.concatenate([encoding["""attention_mask"""] for encoding in encodings]					,							axis=0			)

           elif return_tensors == "jax" and is_flax_available():
                import jax.numpy as jnp

                __SCREAMING_SNAKE_CASE     =      jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]					,							axis=0			)
                __SCREAMING_SNAKE_CASE     =      jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]					,							axis=0			)

           elif return_tensors == "pt" and is_torch_available():
                import torch

                __SCREAMING_SNAKE_CASE     =      torch.cat([encoding["""input_ids"""] for encoding in encodings]					,							dim=0			)
                __SCREAMING_SNAKE_CASE     =      torch.cat([encoding["""attention_mask"""] for encoding in encodings]					,							dim=0			)

           elif return_tensors == "tf" and is_tf_available():
                import tensorflow as tf

                __SCREAMING_SNAKE_CASE     =      tf.stack([encoding["""input_ids"""] for encoding in encodings]					,							axis=0			)
                __SCREAMING_SNAKE_CASE     =      tf.stack([encoding["""attention_mask"""] for encoding in encodings]					,							axis=0			)

           else:
                raise ValueError("""Target return tensor type could not be returned"""			)

           __SCREAMING_SNAKE_CASE     =      BatchEncoding()
           __SCREAMING_SNAKE_CASE     =      input_ids
           __SCREAMING_SNAKE_CASE     =      attention_mask

      if query_images is not None:
           __SCREAMING_SNAKE_CASE     =      BatchEncoding()
           __SCREAMING_SNAKE_CASE     =      self.image_processor(
               __SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			).pixel_values
           __SCREAMING_SNAKE_CASE     =      query_pixel_values

      if images is not None:
           __SCREAMING_SNAKE_CASE     =      self.image_processor(__SCREAMING_SNAKE_CASE					,							return_tensors=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE     =      image_features.pixel_values
           return encoding
      elif query_images is not None and images is not None:
           __SCREAMING_SNAKE_CASE     =      image_features.pixel_values
           return encoding
      elif text is not None or query_images is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE			)					,							tensor_type=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any					,							*__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							**__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> str:


      """simple docstring"""




      return self.image_processor.post_process(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							*__SCREAMING_SNAKE_CASE					:       int					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> Any:


      """simple docstring"""




      return self.image_processor.post_process_object_detection(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							*__SCREAMING_SNAKE_CASE					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Any			)  -> Optional[Any]:


      """simple docstring"""




      return self.image_processor.post_process_image_guided_detection(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]					,							*__SCREAMING_SNAKE_CASE					:       List[str]					,							**__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> int:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]					,							*__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							**__SCREAMING_SNAKE_CASE					:       int			)  -> Tuple:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 @property
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Tuple:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""					,							__SCREAMING_SNAKE_CASE					,							)
      return self.image_processor_class


 @property
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""					,							__SCREAMING_SNAKE_CASE					,							)
      return self.image_processor









'''simple docstring'''



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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""					,							return_dict=__SCREAMING_SNAKE_CASE			)[0]

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE     =      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]			)

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






