





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   ):

  """simple docstring"""


  a_      =			["""image_processor""", """tokenizer"""]
  a_      =			"""LayoutLMv2ImageProcessor"""
  a_      =			("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")






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






  def __call__(    self			:		List[Any] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,  lowerCAmelCase_			:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,  lowerCAmelCase_			:		Union[List[List[int]], List[List[List[int]]]] = None ,  lowerCAmelCase_			:		Optional[Union[List[int], List[List[int]]]] = None ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Union[bool, str, PaddingStrategy] = False ,  lowerCAmelCase_			:		Union[bool, str, TruncationStrategy] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[bool] = None ,  lowerCAmelCase_			:		Optional[bool] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  **lowerCAmelCase_			:		Dict ,  )							->						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
     __lowerCAmelCase							     =     self.image_processor(images=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_					)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
           __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=lowerCAmelCase_ ,  add_special_tokens=lowerCAmelCase_ ,  padding=lowerCAmelCase_ ,  truncation=lowerCAmelCase_ ,  max_length=lowerCAmelCase_ ,  stride=lowerCAmelCase_ ,  pad_to_multiple_of=lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  return_overflowing_tokens=lowerCAmelCase_ ,  return_special_tokens_mask=lowerCAmelCase_ ,  return_offsets_mapping=lowerCAmelCase_ ,  return_length=lowerCAmelCase_ ,  verbose=lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )

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

     return encoded_inputs






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any					)							->						List[Any]:
     # 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(lowerCAmelCase_					) != len(lowerCAmelCase_					):
        raise ValueError(
            'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
            f""" {len(lowerCAmelCase_					)} and {len(lowerCAmelCase_					)}"""					)

     return images_with_overflow






  def     lowercase		(    self			:		List[Any] ,  *lowerCAmelCase_			:		List[str] ,  **lowerCAmelCase_			:		Optional[int]					)							->						str:
     return self.tokenizer.batch_decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  *lowerCAmelCase_			:		Tuple ,  **lowerCAmelCase_			:		Dict					)							->						str:
     return self.tokenizer.decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						List[str]:
     return ["input_ids", "bbox", "attention_mask", "image"]






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,  lowerCAmelCase_ ,  )
     return self.image_processor_class






  @property
  def     lowercase		(    self			:		int					)							->						str:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,  lowerCAmelCase_ ,  )
     return self.image_processor








import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			DanceDiffusionPipeline
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  a_      =			PipelineTesterMixin.required_optional_params - {
      """callback""",
      """latents""",
      """callback_steps""",
      """output_type""",
      """num_images_per_prompt""",
  }
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         block_out_channels=(3_2, 3_2, 6_4) ,  extra_in_channels=1_6 ,  sample_size=5_1_2 ,  sample_rate=1_6_0_0_0 ,  in_channels=2 ,  out_channels=2 ,  flip_sin_to_cos=lowerCAmelCase_ ,  use_timestep_embedding=lowerCAmelCase_ ,  time_embedding_type='fourier' ,  mid_block_type='UNetMidBlock1D' ,  down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') ,  up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') ,  )
     __lowerCAmelCase							     =     IPNDMScheduler()

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
     }
     return components






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[str]=0					)							->						Any:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 4,
     }
     return inputs






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     DanceDiffusionPipeline(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.get_dummy_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, components["unet"].sample_size)
     __lowerCAmelCase							     =     np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]					)
     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  @skip_mps
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return super().test_save_load_local()






  @skip_mps
  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






  @skip_mps
  def     lowercase		(    self			:		str					)							->						List[str]:
     return super().test_save_load_optional_components()






  @skip_mps
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return super().test_attention_slicing_forward_pass()






  def     lowercase		(    self			:		str					)							->						int:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		List[str]					)							->						List[str]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]					)

     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' ,  torch_dtype=torch.floataa					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]					)

     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2

