





'''simple docstring'''

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	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """tokenizer"""]
 __lowercase   		=				"""LayoutLMv2ImageProcessor"""
 __lowercase   		=				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
 def __init__(				self      :Dict						,       lowercase_      :str=None						,       lowercase_      :List[Any]=None						,       **lowercase_      :Tuple							)->							Tuple:
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         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__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :List[str]						,       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] = None						,       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_      :Optional[int]						,       )->							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
     A__				=			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_							):
             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=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
     A__				=			features.pop("pixel_values"							)
     if return_overflowing_tokens is True:
         A__				=			self.get_overflowing_images(lowercase_						,       encoded_inputs["overflow_to_sample_mapping"]							)
     A__				=			images

     return encoded_inputs
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Dict						,       lowercase_      :Optional[int]							)->							Dict:
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     A__				=			[]
     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      :Tuple						,       *lowercase_      :Optional[Any]						,       **lowercase_      :Union[str, Any]							)->							Tuple:
     return self.tokenizer.batch_decode(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       *lowercase_      :Optional[int]						,       **lowercase_      :List[Any]							)->							Union[str, Any]:
     return self.tokenizer.decode(*lowercase_						,       **lowercase_							)
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Union[str, Any]:
     return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[int]:
     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      :Union[str, Any]							)->							Any:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,       lowercase_						,       )
     return self.image_processor










'''simple docstring'''

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				(DPMSolverSDEScheduler,)
 __lowercase   		=				10
 def      UpperCAmelCase_							(				self      :List[Any]						,       **lowercase_      :Optional[int]							)->							str:
     A__				=			{
         "num_train_timesteps": 11_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "noise_sampler_seed": 0,
     }

     config.update(**lowercase_							)
     return config
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,       [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,       beta_end=lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Union[str, Any]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

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

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config(prediction_type="v_prediction"							)
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

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

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[str]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps						,       device=lowercase_							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3



 def      UpperCAmelCase_							(				self      :Tuple							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_						,       use_karras_sigmas=lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps						,       device=lowercase_							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2



