
import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	{}
  for name in unet.attn_processors.keys():
    __magic_name__    					=	None if name.endswith("""attn1.processor""" ) else unet.config.cross_attention_dim
    if name.startswith("""mid_block""" ):
      __magic_name__    					=	unet.config.block_out_channels[-1]
    elif name.startswith("""up_blocks""" ):
      __magic_name__    					=	int(name[len("""up_blocks.""" )] )
      __magic_name__    					=	list(reversed(unet.config.block_out_channels ) )[block_id]
    elif name.startswith("""down_blocks""" ):
      __magic_name__    					=	int(name[len("""down_blocks.""" )] )
      __magic_name__    					=	unet.config.block_out_channels[block_id]
    __magic_name__    					=	(
        LoRAAttnProcessor2_0 if hasattr(A_,					"""scaled_dot_product_attention""" ) else LoRAAttnProcessor
    )
    __magic_name__    					=	lora_attn_processor_class(
        hidden_size=A_,					cross_attention_dim=A_ )
  __magic_name__    					=	AttnProcsLayers(A_ )
  return lora_attn_procs, unet_lora_layers

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	{}
  __magic_name__    					=	(
      LoRAAttnProcessor2_0 if hasattr(A_,					"""scaled_dot_product_attention""" ) else LoRAAttnProcessor
  )
  for name, module in text_encoder.named_modules():
    if name.endswith(A_ ):
      __magic_name__    					=	lora_attn_processor_class(
          hidden_size=module.out_proj.out_features,					cross_attention_dim=A_ )
  return text_lora_attn_procs

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	create_text_encoder_lora_attn_procs(A_ )
  __magic_name__    					=	AttnProcsLayers(A_ )
  return text_encoder_lora_layers

def a__   (	A_,					A_=False ):






  '''simple docstring'''





  for _, attn_proc in text_lora_attn_procs.items():
    # set up.weights
    for layer_name, layer_module in attn_proc.named_modules():
      if layer_name.endswith("""_lora""" ):
        __magic_name__    					=	(
            torch.randn_like(layer_module.up.weight )
            if randn_weight
            else torch.zeros_like(layer_module.up.weight )
        )
        __magic_name__    					=	torch.nn.Parameter(A_ )





class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			int   )      ->     int:







    """simple docstring"""






    torch.manual_seed(0   )
    __magic_name__    					=	UNetaDConditionModel(
        block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,							cross_attention_dim=32      ,							)
    __magic_name__    					=	DDIMScheduler(
        beta_start=0.00085      ,							beta_end=0.012      ,							beta_schedule="""scaled_linear"""      ,							clip_sample=UpperCamelCase__      ,							set_alpha_to_one=UpperCamelCase__      ,							steps_offset=1      ,							)
    torch.manual_seed(0   )
    __magic_name__    					=	AutoencoderKL(
        block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,							latent_channels=4      ,							)
    __magic_name__    					=	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=1000      ,							)
    __magic_name__    					=	CLIPTextModel(UpperCamelCase__   )
    __magic_name__    					=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

    __magic_name__					,   __magic_name__    					=	create_unet_lora_layers(UpperCamelCase__   )
    __magic_name__    					=	create_text_encoder_lora_layers(UpperCamelCase__   )

    __magic_name__    					=	{
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """safety_checker""": None,
        """feature_extractor""": None,
    }
    __magic_name__    					=	{
        """unet_lora_layers""": unet_lora_layers,
        """text_encoder_lora_layers""": text_encoder_lora_layers,
        """unet_lora_attn_procs""": unet_lora_attn_procs,
    }
    return pipeline_components, lora_components
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	1
    __magic_name__    					=	10
    __magic_name__    					=	4
    __magic_name__    					=	(32, 32)

    __magic_name__    					=	torch.manual_seed(0   )
    __magic_name__    					=	floats_tensor((batch_size, num_channels) + sizes   )
    __magic_name__    					=	torch.randint(1      ,							UpperCamelCase__      ,							size=(batch_size, sequence_length)      ,							generator=UpperCamelCase__   )

    __magic_name__    					=	{
        """prompt""": """A painting of a squirrel eating a burger""",
        """generator""": generator,
        """num_inference_steps""": 2,
        """guidance_scale""": 6.0,
        """output_type""": """numpy""",
    }

    return noise, input_ids, pipeline_inputs

    # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  def 							_lowercase							(			self      :			Optional[int]   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	77

    __magic_name__    					=	torch.randint(2      ,							56      ,							size=(1, max_seq_length)      ,							generator=torch.manual_seed(0   )   )

    __magic_name__    					=	{}
    __magic_name__    					=	inputs
    return prepared_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Any   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    LoraLoaderMixin.save_lora_weights(
        save_directory=UpperCamelCase__      ,							unet_lora_layers=lora_components["""unet_lora_layers"""]      ,							text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]      ,							)
    self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.bin"""   )   )   )
  def 							_lowercase							(			self      :			Tuple   )      ->     int:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
    __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	self.get_dummy_inputs()

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=UpperCamelCase__      ,							unet_lora_layers=lora_components["""unet_lora_layers"""]      ,							text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]      ,							)
      self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.bin"""   )   )   )
      sd_pipe.load_lora_weights(UpperCamelCase__   )

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__   )      ,							torch.from_numpy(UpperCamelCase__   )   )   )
  def 							_lowercase							(			self      :			int   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
    __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	self.get_dummy_inputs()

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=UpperCamelCase__      ,							unet_lora_layers=lora_components["""unet_lora_layers"""]      ,							text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]      ,							safe_serialization=UpperCamelCase__      ,							)
      self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.safetensors"""   )   )   )
      sd_pipe.load_lora_weights(UpperCamelCase__   )

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__   )      ,							torch.from_numpy(UpperCamelCase__   )   )   )
  def 							_lowercase							(			self      :			Optional[int]   )      ->     int:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	lora_components["""unet_lora_attn_procs"""]
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
    __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	self.get_dummy_inputs()

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__    					=	sd_pipe.unet
      unet.set_attn_processor(UpperCamelCase__   )
      unet.save_attn_procs(UpperCamelCase__   )
      self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.bin"""   )   )   )
      sd_pipe.load_lora_weights(UpperCamelCase__   )

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__   )      ,							torch.from_numpy(UpperCamelCase__   )   )   )
  def 							_lowercase							(			self      :			int   )      ->     int:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )

    __magic_name__    					=	self.get_dummy_tokens()

    # inference without lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_without_lora.shape == (1, 77, 32)

    # create lora_attn_procs with zeroed out up.weights
    __magic_name__    					=	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
    set_lora_up_weights(UpperCamelCase__      ,							randn_weight=UpperCamelCase__   )

    # monkey patch
    pipe._modify_text_encoder(UpperCamelCase__   )

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert torch.allclose(
        UpperCamelCase__      ,							UpperCamelCase__   ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

    # create lora_attn_procs with randn up.weights
    __magic_name__    					=	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
    set_lora_up_weights(UpperCamelCase__      ,							randn_weight=UpperCamelCase__   )

    # monkey patch
    pipe._modify_text_encoder(UpperCamelCase__   )

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert not torch.allclose(
        UpperCamelCase__      ,							UpperCamelCase__   ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  def 							_lowercase							(			self      :			Optional[int]   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )

    __magic_name__    					=	self.get_dummy_tokens()

    # inference without lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_without_lora.shape == (1, 77, 32)

    # create lora_attn_procs with randn up.weights
    __magic_name__    					=	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
    set_lora_up_weights(UpperCamelCase__      ,							randn_weight=UpperCamelCase__   )

    # monkey patch
    pipe._modify_text_encoder(UpperCamelCase__   )

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert not torch.allclose(
        UpperCamelCase__      ,							UpperCamelCase__   ), "lora outputs should be different to without lora outputs"

    # remove monkey patch
    pipe._remove_text_encoder_monkey_patch()

    # inference with removed lora
    __magic_name__    					=	pipe.text_encoder(**UpperCamelCase__   )[0]
    assert outputs_without_lora_removed.shape == (1, 77, 32)

    assert torch.allclose(
        UpperCamelCase__      ,							UpperCamelCase__   ), "remove lora monkey patch should restore the original outputs"
  def 							_lowercase							(			self      :			Tuple   )      ->     Dict:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
    __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	self.get_dummy_inputs()

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=UpperCamelCase__      ,							unet_lora_layers=lora_components["""unet_lora_layers"""]      ,							text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]      ,							)
      self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.bin"""   )   )   )
      sd_pipe.load_lora_weights(UpperCamelCase__   )

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	lora_images[0, -3:, -3:, -1]

    __magic_name__    					=	sd_pipe(**UpperCamelCase__      ,							cross_attention_kwargs={"""scale""": 0.5}   ).images
    __magic_name__    					=	lora_images_with_scale[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(
        torch.allclose(torch.from_numpy(UpperCamelCase__   )      ,							torch.from_numpy(UpperCamelCase__   )   )   )
  def 							_lowercase							(			self      :			str   )      ->     Tuple:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdirname:
      self.create_lora_weight_file(UpperCamelCase__   )

      __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
      __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
      __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
      sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

      # check if vanilla attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
          self.assertIsInstance(module.processor      ,							(AttnProcessor, AttnProcessor2_0)   )

            # load LoRA weight file
      sd_pipe.load_lora_weights(UpperCamelCase__   )

      # check if lora attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
          __magic_name__    					=	(
              LoRAAttnProcessor2_0 if hasattr(UpperCamelCase__      ,							"""scaled_dot_product_attention"""   ) else LoRAAttnProcessor
          )
          self.assertIsInstance(module.processor      ,							UpperCamelCase__   )
  @unittest.skipIf(torch_device != """cuda"""      ,							"""This test is supposed to run on GPU"""   )
  def 							_lowercase							(			self      :			List[str]   )      ->     str:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdirname:
      self.create_lora_weight_file(UpperCamelCase__   )

      __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
      __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
      __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
      sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

      # enable XFormers
      sd_pipe.enable_xformers_memory_efficient_attention()

      # check if xFormers attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
          self.assertIsInstance(module.processor      ,							UpperCamelCase__   )

            # load LoRA weight file
      sd_pipe.load_lora_weights(UpperCamelCase__   )

      # check if lora attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
          self.assertIsInstance(module.processor      ,							UpperCamelCase__   )





  @unittest.skipIf(torch_device != """cuda"""      ,							"""This test is supposed to run on GPU"""   )
  def 							_lowercase							(			self      :			int   )      ->     List[str]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.get_dummy_components()
    __magic_name__    					=	StableDiffusionPipeline(**UpperCamelCase__   )
    __magic_name__    					=	sd_pipe.to(UpperCamelCase__   )
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	self.get_dummy_inputs()

    # enable XFormers
    sd_pipe.enable_xformers_memory_efficient_attention()

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=UpperCamelCase__      ,							unet_lora_layers=lora_components["""unet_lora_layers"""]      ,							text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]      ,							)
      self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__      ,							"""pytorch_lora_weights.bin"""   )   )   )
      sd_pipe.load_lora_weights(UpperCamelCase__   )

    __magic_name__    					=	sd_pipe(**UpperCamelCase__   ).images
    __magic_name__    					=	lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__   )      ,							torch.from_numpy(UpperCamelCase__   )   )   )



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






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

    __magic_name__    					=	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__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	self.tokenizer(
        text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

    if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__   )} and {len(UpperCamelCase__   )}'''   )

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






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





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






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

