
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 							lowerCamelCase       (		_A					):



  """simple docstring"""





  lowerCAmelCase_								=		["""image_processor""", """tokenizer"""]
  lowerCAmelCase_								=		"""LayoutLMv2ImageProcessor"""
  lowerCAmelCase_								=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")

  def __init__(					self						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    **__UpperCamelCase					):
      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."						,    _UpperCAmelCase						,    )
          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__(_UpperCAmelCase						,    _UpperCAmelCase					)

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




      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
      A_								=					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					):
              A_								=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
          A_								=					features['words']

      A_								=					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
      A_								=					features.pop("pixel_values"					)
      if return_overflowing_tokens is True:
          A_								=					self.get_overflowing_images(_UpperCAmelCase						,    encoded_inputs["overflow_to_sample_mapping"]					)
      A_								=					images

      return encoded_inputs

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					[]
      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						,    *__UpperCamelCase						,    **__UpperCamelCase					):
      return self.tokenizer.batch_decode(*_UpperCAmelCase						,    **_UpperCAmelCase					)

  def 					lowercase_						(					self						,    *__UpperCamelCase						,    **__UpperCamelCase					):
      return self.tokenizer.decode(*_UpperCAmelCase						,    **_UpperCAmelCase					)

  @property
  def 					lowercase_						(					self					):
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

  @property
  def 					lowercase_						(					self					):
      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					):
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,    _UpperCAmelCase						,    )
      return self.image_processor










import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class      snake_case__	(   unittest.TestCase	):
				__lowerCAmelCase		=   '''hf-internal-testing/tiny-stable-diffusion-pipe'''
				__lowerCAmelCase		=   1
				__lowerCAmelCase		=   77
				__lowerCAmelCase		=   32
				__lowerCAmelCase		=   4
				__lowerCAmelCase		=   __lowerCAmelCase		=   64
				__lowerCAmelCase		=   torch.manual_seed(0	)
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0.9999   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   UNetaDConditionModel.from_pretrained(self.model_id  ,   subfolder='unet'   )
										__magic_name__					:				int				=   unet.to(_UpperCAmelCase   )
										__magic_name__					:				Optional[int]				=   EMAModel(unet.parameters()  ,   decay=_UpperCAmelCase  ,   model_cls=_UpperCAmelCase  ,   model_config=unet.config   )
										return unet, ema_unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   torch.randn(
										    self.batch_size  ,   self.num_in_channels  ,   self.latent_height  ,   self.latent_width  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   torch.randint(0  ,   1000  ,   size=(self.batch_size,)  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				Any				=   torch.randn(
										    self.batch_size  ,   self.prompt_length  ,   self.text_encoder_hidden_dim  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										return noisy_latents, timesteps, encoder_hidden_states
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   {}
										for k, param in unet.state_dict().items():
																__magic_name__					:				str				=   torch.randn_like(_UpperCAmelCase   ) + (param * torch.randn_like(_UpperCAmelCase   ))
																updated_state_dict.update({k: updated_param}   )
										unet.load_state_dict(_UpperCAmelCase   )
										return unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				int				=   self.get_models()
										# Take the first (hypothetical) EMA step.
										ema_unet.step(unet.parameters()   )
										assert ema_unet.optimization_step == 1

										# Take two more.
										for _ in range(2   ):
																ema_unet.step(unet.parameters()   )
										assert ema_unet.optimization_step == 3
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Union[str, Any]				=   self.get_models()
										# Since the `unet` is not being updated (i.e., backprop'd)
										# there won't be any difference between the `params` of `unet`
										# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
										ema_unet.step(unet.parameters()   )
										__magic_name__					:				Any				=   list(unet.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )

										# The above holds true even if we call `ema.step()` multiple times since
										# `unet` params are still not being updated.
										for _ in range(4   ):
																ema_unet.step(unet.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.get_models()
										# Here we simulate the parameter updates for `unet`. Since there might
										# be some parameters which are initialized to zero we take extra care to
										# initialize their values to something non-zero before the multiplication.
										__magic_name__					:				Any				=   self.simulate_backprop(_UpperCAmelCase   )

										# Take the EMA step.
										ema_unet.step(unet_pseudo_updated_step_one.parameters()   )

										# Now the EMA'd parameters won't be equal to the original model parameters.
										__magic_name__					:				Any				=   list(unet_pseudo_updated_step_one.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )

										# Ensure this is the case when we take multiple EMA steps.
										for _ in range(4   ):
																ema_unet.step(unet.parameters()   )
										for s_param, param in zip(ema_unet.shadow_params  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				List[str]				=   self.get_models()

										# First backprop + EMA
										__magic_name__					:				int				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				Any				=   ema_unet.shadow_params

										# Second backprop + EMA
										__magic_name__					:				Any				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Union[str, Any]				=   ema_unet.shadow_params

										for step_one, step_two in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.get_models(decay=0.0   )
										__magic_name__					:				str				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				List[Any]				=   ema_unet.shadow_params

										__magic_name__					:				Optional[Any]				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Optional[Any]				=   ema_unet.shadow_params

										for step_one, step_two in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[int]				=   self.get_models()
										__magic_name__		,			__magic_name__		,			__magic_name__					:				str				=   self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
																ema_unet.save_pretrained(_UpperCAmelCase   )
																__magic_name__					:				Optional[int]				=   UNetaDConditionModel.from_pretrained(_UpperCAmelCase  ,   model_cls=_UpperCAmelCase   )
																__magic_name__					:				str				=   loaded_unet.to(unet.device   )

										# Since no EMA step has been performed the outputs should match.
										__magic_name__					:				Union[str, Any]				=   unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample
										__magic_name__					:				List[Any]				=   loaded_unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ).sample

										assert torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase  ,   atol=1E-4   )


