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				( __lowercase ):

     '''simple docstring'''

     A_										=  ['image_processor', 'tokenizer']
     A_										=  'LayoutLMv2ImageProcessor'
     A_										=  ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')



     def __init__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Tuple:






           """simple docstring"""
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                     ''' instead.'''				,				A_				,				)
                 _lowerCamelCase			    =				kwargs.pop('''feature_extractor'''						)

           _lowerCamelCase			    =				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__(A_				,				A_						)



     def __call__(					self				,				A_				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = True				,				A_ = False				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = True				,				A_ = None				,				**A_				,				) ->				BatchEncoding:






           """simple docstring"""
           # 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
           _lowerCamelCase			    =				self.image_processor(images=A_				,				return_tensors=A_						)

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

           _lowerCamelCase			    =				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=A_				,				add_special_tokens=A_				,				padding=A_				,				truncation=A_				,				max_length=A_				,				stride=A_				,				pad_to_multiple_of=A_				,				return_token_type_ids=A_				,				return_attention_mask=A_				,				return_overflowing_tokens=A_				,				return_special_tokens_mask=A_				,				return_offsets_mapping=A_				,				return_length=A_				,				verbose=A_				,				return_tensors=A_				,				**A_				,				)

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

           return encoded_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
           _lowerCamelCase			    =				[]
           for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]						)

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

           return images_with_overflow



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				Any:






           """simple docstring"""
           return self.tokenizer.batch_decode(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				Any:






           """simple docstring"""
           return self.tokenizer.decode(*A_				,				**A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return ["input_ids", "bbox", "attention_mask", "image"]



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






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





     @property
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,				A_				,				)
           return self.image_processor
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size						) == int:
                 _lowerCamelCase			    =				(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _lowerCamelCase			    =				randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )				,				generator=A_				,				device=self.device				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

                 if self.vqvae is not None:
                       _lowerCamelCase			    =				self.vqvae.encode(torch.unsqueeze(A_				,				0						)						).latent_dist.sample(
                           generator=A_						)[0]
                       _lowerCamelCase			    =				self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				self.scheduler.timesteps[start_step - 1]						)

                 _lowerCamelCase			    =				(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 _lowerCamelCase			    =				int(mask_start_secs * pixels_per_second						)
                 _lowerCamelCase			    =				int(mask_end_secs * pixels_per_second						)
                 _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				torch.tensor(self.scheduler.timesteps[start_step:]						)						)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                 if isinstance(self.unet				,				A_						):
                       _lowerCamelCase			    =				self.unet(A_				,				A_				,				A_						)['''sample''']
                 else:
                       _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']

                 if isinstance(self.scheduler				,				A_						):
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
                 else:
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''prev_sample''']

                 if mask is not None:
                       if mask_start > 0:
                             _lowerCamelCase			    =				mask[:, step, :, :mask_start]
                       if mask_end > 0:
                             _lowerCamelCase			    =				mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 _lowerCamelCase			    =				1 / self.vqvae.config.scaling_factor * images
                 _lowerCamelCase			    =				self.vqvae.decode(A_						)['''sample''']

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

           _lowerCamelCase			    =				[self.mel.image_to_audio(A_						) for _ in images]
           if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(A_						)[:, np.newaxis, :]						)				,				**ImagePipelineOutput(A_						)						)



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,				(0,)						)						):
                 _lowerCamelCase			    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 _lowerCamelCase			    =				self.scheduler.alphas_cumprod[t]
                 _lowerCamelCase			    =				(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _lowerCamelCase			    =				1 - alpha_prod_t
                 _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']
                 _lowerCamelCase			    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _lowerCamelCase			    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _lowerCamelCase			    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)