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    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE    				=			"""OwlViTImageProcessor"""
  _SCREAMING_SNAKE_CASE    				=			("""CLIPTokenizer""", """CLIPTokenizerFast""")

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							__SCREAMING_SNAKE_CASE :      Any=None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   __a       =  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     ,							)
    __a       =  kwargs.pop("feature_extractor"       )

   __a       =  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[int]     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=None     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="max_length"     ,							__SCREAMING_SNAKE_CASE :      List[str]="np"     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):

   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       )):
     __a       =  [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       ):
     __a       =  []

     # Maximum number of queries across batch
     __a       =  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:
       __a       =  t + [" "] * (max_num_queries - len(__SCREAMING_SNAKE_CASE       ))

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

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

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

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

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

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

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

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

    __a       =  BatchEncoding()
    __a       =  input_ids
    __a       =  attention_mask

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

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

   if text is not None and images is not None:
    __a       =  image_features.pixel_values
    return encoding
   elif query_images is not None and images is not None:
    __a       =  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 :      List[Any]     ,							*__SCREAMING_SNAKE_CASE :      Dict     ,							**__SCREAMING_SNAKE_CASE :      List[str]       ):
   return self.image_processor.post_process(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							*__SCREAMING_SNAKE_CASE :      str     ,							**__SCREAMING_SNAKE_CASE :      int       ):
   return self.image_processor.post_process_object_detection(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							*__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
   return self.image_processor.post_process_image_guided_detection(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str     ,							*__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							**__SCREAMING_SNAKE_CASE :      List[Any]       ):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							*__SCREAMING_SNAKE_CASE :      int     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   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 :      str       ):
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__SCREAMING_SNAKE_CASE     ,							)
   return self.image_processor



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  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

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

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

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

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

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


