import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						snake_case_ (	__A ):
   __A			:	int        =       (EulerAncestralDiscreteScheduler,)
   __A			:	Tuple        =       10






   def    __UpperCamelCase  (   self       :  List[Any]			,		**lowercase_       :  Tuple						)      ->		List[str]:
       lowercase__				:     Dict					      =       {
           "num_train_timesteps": 11_00,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**lowercase_						)
       return config






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Optional[int]:
       for timesteps in [10, 50, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		List[str]:
       for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,		[0.00_02, 0.0_02, 0.02]						):
           self.check_over_configs(beta_start=lowercase_			,		beta_end=lowercase_						)






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Tuple:
       for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=lowercase_						)






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Union[str, Any]:
       for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Dict:
       lowercase__				:     Union[str, Any]					      =       self.scheduler_classes[0]
       lowercase__				:     Union[str, Any]					      =       self.get_scheduler_config()
       lowercase__				:     Union[str, Any]					      =       scheduler_class(**lowercase_						)

       scheduler.set_timesteps(self.num_inference_steps						)

       lowercase__				:     List[Any]					      =       torch.manual_seed(0						)

       lowercase__				:     Dict					      =       self.dummy_model()
       lowercase__				:     Tuple					      =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase__				:     Tuple					      =       sample.to(lowercase_						)

       for i, t in enumerate(scheduler.timesteps						):
           lowercase__				:     int					      =       scheduler.scale_model_input(lowercase_			,		lowercase_						)

           lowercase__				:     Tuple					      =       model(lowercase_			,		lowercase_						)

           lowercase__				:     Any					      =       scheduler.step(lowercase_			,		lowercase_			,		lowercase_			,		generator=lowercase_						)
           lowercase__				:     List[Any]					      =       output.prev_sample

       lowercase__				:     Optional[Any]					      =       torch.sum(torch.abs(lowercase_						)						)
       lowercase__				:     Any					      =       torch.mean(torch.abs(lowercase_						)						)

       assert abs(result_sum.item() - 1_52.31_92						) < 1E-2
       assert abs(result_mean.item() - 0.19_83						) < 1E-3






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Dict:
       lowercase__				:     Any					      =       self.scheduler_classes[0]
       lowercase__				:     str					      =       self.get_scheduler_config(prediction_type="v_prediction"						)
       lowercase__				:     List[Any]					      =       scheduler_class(**lowercase_						)

       scheduler.set_timesteps(self.num_inference_steps						)

       lowercase__				:     List[Any]					      =       torch.manual_seed(0						)

       lowercase__				:     int					      =       self.dummy_model()
       lowercase__				:     str					      =       self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase__				:     int					      =       sample.to(lowercase_						)

       for i, t in enumerate(scheduler.timesteps						):
           lowercase__				:     Any					      =       scheduler.scale_model_input(lowercase_			,		lowercase_						)

           lowercase__				:     Any					      =       model(lowercase_			,		lowercase_						)

           lowercase__				:     Any					      =       scheduler.step(lowercase_			,		lowercase_			,		lowercase_			,		generator=lowercase_						)
           lowercase__				:     Tuple					      =       output.prev_sample

       lowercase__				:     Dict					      =       torch.sum(torch.abs(lowercase_						)						)
       lowercase__				:     List[Any]					      =       torch.mean(torch.abs(lowercase_						)						)

       assert abs(result_sum.item() - 1_08.44_39						) < 1E-2
       assert abs(result_mean.item() - 0.14_12						) < 1E-3







   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		int:
       lowercase__				:     Tuple					      =       self.scheduler_classes[0]
       lowercase__				:     str					      =       self.get_scheduler_config()
       lowercase__				:     List[str]					      =       scheduler_class(**lowercase_						)

       scheduler.set_timesteps(self.num_inference_steps			,		device=lowercase_						)
       lowercase__				:     int					      =       torch.manual_seed(0						)

       lowercase__				:     List[str]					      =       self.dummy_model()
       lowercase__				:     int					      =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase__				:     List[Any]					      =       sample.to(lowercase_						)

       for t in scheduler.timesteps:
           lowercase__				:     Union[str, Any]					      =       scheduler.scale_model_input(lowercase_			,		lowercase_						)

           lowercase__				:     Dict					      =       model(lowercase_			,		lowercase_						)

           lowercase__				:     Tuple					      =       scheduler.step(lowercase_			,		lowercase_			,		lowercase_			,		generator=lowercase_						)
           lowercase__				:     List[Any]					      =       output.prev_sample

       lowercase__				:     Tuple					      =       torch.sum(torch.abs(lowercase_						)						)
       lowercase__				:     Dict					      =       torch.mean(torch.abs(lowercase_						)						)

       assert abs(result_sum.item() - 1_52.31_92						) < 1E-2
       assert abs(result_mean.item() - 0.19_83						) < 1E-3




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 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       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__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       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
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

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

       lowercase__				:     List[str]					      =       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=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

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

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






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






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



