'''simple docstring'''




import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_A								=   {
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     return torch.atana(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							) / math.pi * 2

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[Any]     				=					torch.sin(t * math.pi / 2							) ** 2
     lowercase_					:       int     				=					(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  (       nn.Module			):






   """simple docstring"""
   def __init__(self       ,				_a				)    ->     Tuple:
        super().__init__()

        lowercase_					:       Union[str, Any]     				=					DiffusionAttnUnetaD(_a       ,				n_attn_layers=4				)
        lowercase_					:       Any     				=					deepcopy(self.diffusion				)
        lowercase_					:       str     				=					torch.quasirandom.SobolEngine(1       ,				scramble=_a				)



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[Any]     				=					MODELS_MAP[model_name]['url']
     os.system(f'''wget {url} ./'''							)

     return f'''./{model_name}.ckpt'''


_A								=   {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
_A								=   {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
_A								=   {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
_A								=   {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

_A								=   {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

_A								=   {
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if name.startswith('skip'							):
          return name.replace('skip'  ,       RES_CONV_MAP['skip']							)

     # name has to be of format main.{digit}
     if not name.startswith('main.'							):
          raise ValueError(f'''ResConvBlock error with {name}'''							)

     return name.replace(name[:6]  ,       RES_CONV_MAP[name[:6]]							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     for key, value in ATTN_MAP.items():
          if name.startswith(SCREAMING_SNAKE_CASE_							) and not isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
               return name.replace(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          elif name.startswith(SCREAMING_SNAKE_CASE_							):
               return [name.replace(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							) for v in value]
     raise ValueError(f'''Attn error with {name}'''							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=13							):
     lowercase_					:       Dict     				=					input_string

     if string.split('.'							)[0] == "timestep_embed":
          return string.replace('timestep_embed'  ,       'time_proj'							)

     lowercase_					:       Optional[Any]     				=					0
     if string.startswith('net.3.'							):
          depth += 1
          lowercase_					:       Optional[Any]     				=					string[6:]
     elif string.startswith('net.'							):
          lowercase_					:       Union[str, Any]     				=					string[4:]

     while string.startswith('main.7.'							):
          depth += 1
          lowercase_					:       Union[str, Any]     				=					string[7:]

     if string.startswith('main.'							):
          lowercase_					:       int     				=					string[5:]

     # mid block
     if string[:2].isdigit():
          lowercase_					:       Union[str, Any]     				=					string[:2]
          lowercase_					:       List[str]     				=					string[2:]
     else:
          lowercase_					:       Optional[int]     				=					string[0]
          lowercase_					:       int     				=					string[1:]

     if depth == max_depth:
          lowercase_					:       Optional[int]     				=					MID_NUM_TO_LAYER[layer_num]
          lowercase_					:       Union[str, Any]     				=					'mid_block'
     elif depth > 0 and int(SCREAMING_SNAKE_CASE_							) < 7:
          lowercase_					:       Union[str, Any]     				=					DOWN_NUM_TO_LAYER[layer_num]
          lowercase_					:       Dict     				=					f'''down_blocks.{depth}'''
     elif depth > 0 and int(SCREAMING_SNAKE_CASE_							) > 7:
          lowercase_					:       List[Any]     				=					UP_NUM_TO_LAYER[layer_num]
          lowercase_					:       Optional[Any]     				=					f'''up_blocks.{max_depth - depth - 1}'''
     elif depth == 0:
          lowercase_					:       Tuple     				=					DEPTH_0_TO_LAYER[layer_num]
          lowercase_					:       Union[str, Any]     				=					f'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE_							) > 3 else 'down_blocks.0'

     if not string_left.startswith('.'							):
          raise ValueError(f'''Naming error with {input_string} and string_left: {string_left}.'''							)

     lowercase_					:       str     				=					string_left[1:]

     if "resnets" in new_layer:
          lowercase_					:       List[Any]     				=					convert_resconv_naming(SCREAMING_SNAKE_CASE_							)
     elif "attentions" in new_layer:
          lowercase_					:       List[str]     				=					convert_attn_naming(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Dict     				=					new_string_left

     if not isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
          lowercase_					:       List[str]     				=					prefix + '.' + new_layer + '.' + string_left
     else:
          lowercase_					:       int     				=					[prefix + '.' + new_layer + '.' + s for s in string_left]
     return new_string

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					{}
     for k, v in state_dict.items():
          if k.endswith('kernel'							):
               # up- and downsample layers, don't have trainable weights
               continue

          lowercase_					:       List[Any]     				=					rename(SCREAMING_SNAKE_CASE_							)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
               lowercase_					:       Tuple     				=					transform_conv_attns(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          else:
               lowercase_					:       str     				=					v

     return new_state_dict

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if len(SCREAMING_SNAKE_CASE_							) == 1:
          if len(v.shape							) == 3:
               # weight
               lowercase_					:       List[str]     				=					v[:, :, 0]
          else:
               # bias
               lowercase_					:       List[Any]     				=					v
     else:
          # qkv matrices
          lowercase_					:       Any     				=					v.shape[0]
          lowercase_					:       str     				=					trippled_shape // 3
          for i in range(3							):
               if len(v.shape							) == 3:
                    lowercase_					:       List[str]     				=					v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    lowercase_					:       Tuple     				=					v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Tuple     				=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

     lowercase_					:       str     				=					args.model_path.split('/'							)[-1].split('.'							)[0]
     if not os.path.isfile(args.model_path							):
          assert (
              model_name == args.model_path
          ), f'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
          lowercase_					:       List[Any]     				=					download(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Any     				=					MODELS_MAP[model_name]['sample_rate']
     lowercase_					:       List[Any]     				=					MODELS_MAP[model_name]['sample_size']

     lowercase_					:       List[str]     				=					Object()
     lowercase_					:       Union[str, Any]     				=					sample_size
     lowercase_					:       str     				=					sample_rate
     lowercase_					:       str     				=					0

     lowercase_					:       Any     				=					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_  ,       sample_rate=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Optional[int]     				=					diffusers_model.state_dict()

     lowercase_					:       Optional[Any]     				=					DiffusionUncond(SCREAMING_SNAKE_CASE_							)
     orig_model.load_state_dict(torch.load(args.model_path  ,       map_location=SCREAMING_SNAKE_CASE_							)['state_dict']							)
     lowercase_					:       Tuple     				=					orig_model.diffusion_ema.eval()
     lowercase_					:       Any     				=					orig_model.state_dict()
     lowercase_					:       Tuple     				=					rename_orig_weights(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Optional[Any]     				=					set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
     lowercase_					:       Dict     				=					set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

     assert len(SCREAMING_SNAKE_CASE_							) == 0, f'''Problem with {renamed_minus_diffusers}'''
     assert all(k.endswith('kernel'							) for k in list(SCREAMING_SNAKE_CASE_							)							), f'''Problem with {diffusers_minus_renamed}'''

     for key, value in renamed_state_dict.items():
          assert (
              diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
          ), f'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
          if key == "time_proj.weight":
               lowercase_					:       Optional[int]     				=					value.squeeze()

          lowercase_					:       int     				=					value

     diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Any     				=					100
     lowercase_					:       List[str]     				=					33

     lowercase_					:       int     				=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					torch.manual_seed(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Optional[Any]     				=					torch.randn([1, 2, config.sample_size]  ,       generator=SCREAMING_SNAKE_CASE_							).to(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Dict     				=					torch.linspace(1  ,       0  ,       steps + 1  ,       device=SCREAMING_SNAKE_CASE_							)[:-1]
     lowercase_					:       int     				=					get_crash_schedule(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Tuple     				=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_  ,       scheduler=SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Optional[Any]     				=					torch.manual_seed(33							)
     lowercase_					:       Optional[Any]     				=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_  ,       generator=SCREAMING_SNAKE_CASE_							).audios

     lowercase_					:       Union[str, Any]     				=					sampling.iplms_sample(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       {}							)
     lowercase_					:       Dict     				=					generated.clamp(-1  ,       1							)

     lowercase_					:       List[str]     				=					(generated - audio).abs().sum()
     lowercase_					:       int     				=					(generated - audio).abs().max()

     if args.save:
          pipe.save_pretrained(args.checkpoint_path							)

     print('Diff sum'  ,       SCREAMING_SNAKE_CASE_							)
     print('Diff max'  ,       SCREAMING_SNAKE_CASE_							)

     assert diff_max < 1e-3, f'''Diff max: {diff_max} is too much :-/'''

     print(f'''Conversion for {model_name} successful!'''							)


if __name__ == "__main__":
 _A								=   argparse.ArgumentParser()

 parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
 parser.add_argument(
     '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
 )
 parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
 _A								=   parser.parse_args()

 main(args)

'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_pytesseract_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor


class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=7       ,				_a=3       ,				_a=18       ,				_a=30       ,				_a=400       ,				_a=True       ,				_a=None       ,				_a=True       ,				)    ->     int:
        lowercase_					:       List[str]     				=					size if size is not None else {'height': 18, 'width': 18}
        lowercase_					:       Dict     				=					parent
        lowercase_					:       Optional[Any]     				=					batch_size
        lowercase_					:       int     				=					num_channels
        lowercase_					:       List[Any]     				=					image_size
        lowercase_					:       Dict     				=					min_resolution
        lowercase_					:       List[Any]     				=					max_resolution
        lowercase_					:       Optional[Any]     				=					do_resize
        lowercase_					:       int     				=					size
        lowercase_					:       Optional[int]     				=					apply_ocr





   def 		_lowerCamelCase (self				)    ->     List[str]:
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       int     =    LayoutLMvaImageProcessor if is_pytesseract_available() else None
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       str     				=					LayoutLMvaImageProcessingTester(self				)
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Optional[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_a       ,				'do_resize'				)				)
        self.assertTrue(hasattr(_a       ,				'size'				)				)
        self.assertTrue(hasattr(_a       ,				'apply_ocr'				)				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size       ,				{'height': 18, 'width': 18}				)

        lowercase_					:       Tuple     				=					self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42				)
        self.assertEqual(image_processor.size       ,				{'height': 42, 'width': 42}				)
   def 		_lowerCamelCase (self				)    ->     int:
        pass
   def 		_lowerCamelCase (self				)    ->     Dict:
        # Initialize image_processing
        lowercase_					:       int     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        lowercase_					:       Tuple     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				Image.Image				)

        # Test not batched input
        lowercase_					:       Any     				=					image_processing(image_inputs[0]       ,				return_tensors='pt'				)
        self.assertEqual(
            encoding.pixel_values.shape       ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        self.assertIsInstance(encoding.words       ,				_a				)
        self.assertIsInstance(encoding.boxes       ,				_a				)

        # Test batched
        lowercase_					:       Union[str, Any]     				=					image_processing(_a       ,				return_tensors='pt'				).pixel_values
        self.assertEqual(
            encoded_images.shape       ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        lowercase_					:       str     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				numpify=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				np.ndarray				)

        # Test not batched input
        lowercase_					:       List[Any]     				=					image_processing(image_inputs[0]       ,				return_tensors='pt'				).pixel_values
        self.assertEqual(
            encoded_images.shape       ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        # Test batched
        lowercase_					:       str     				=					image_processing(_a       ,				return_tensors='pt'				).pixel_values
        self.assertEqual(
            encoded_images.shape       ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        lowercase_					:       str     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				torchify=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				torch.Tensor				)

        # Test not batched input
        lowercase_					:       List[Any]     				=					image_processing(image_inputs[0]       ,				return_tensors='pt'				).pixel_values
        self.assertEqual(
            encoded_images.shape       ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        # Test batched
        lowercase_					:       str     				=					image_processing(_a       ,				return_tensors='pt'				).pixel_values
        self.assertEqual(
            encoded_images.shape       ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)





   def 		_lowerCamelCase (self				)    ->     str:
        # with apply_OCR = True
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor()

        from datasets import load_dataset

        lowercase_					:       List[Any]     				=					load_dataset('hf-internal-testing/fixtures_docvqa'       ,				split='test'				)

        lowercase_					:       Union[str, Any]     				=					Image.open(ds[0]['file']				).convert('RGB'				)

        lowercase_					:       List[str]     				=					image_processing(_a       ,				return_tensors='pt'				)

        self.assertEqual(encoding.pixel_values.shape       ,				(1, 3, 224, 224)				)
        self.assertEqual(len(encoding.words				)       ,				len(encoding.boxes				)				)

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        lowercase_					:       str     				=					[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
        lowercase_					:       Optional[Any]     				=					[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words       ,				_a				)
        self.assertListEqual(encoding.boxes       ,				_a				)

        # with apply_OCR = False
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor(apply_ocr=_a				)

        lowercase_					:       Dict     				=					image_processing(_a       ,				return_tensors='pt'				)

        self.assertEqual(encoding.pixel_values.shape       ,				(1, 3, 224, 224)				)




