






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 							lowercase_				(    __A :       int			)       ->      Union[str, Any]:



	"""simple docstring"""




	lowercase					:   str				  =[tensor.shape for tensor in tensor_list]
	return all(shape == shapes[0] for shape in shapes[1:]			)
class UpperCAmelCase_					(    __A				,       __A				,       __A				,       unittest.TestCase				):




						"""simple docstring"""




						UpperCamelCase_								=			StableDiffusionLatentUpscalePipeline
						UpperCamelCase_								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''height''',
						    '''width''',
						    '''cross_attention_kwargs''',
						    '''negative_prompt_embeds''',
						    '''prompt_embeds''',
						}
						UpperCamelCase_								=			PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
						UpperCamelCase_								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						UpperCamelCase_								=			frozenset(
						    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_								=			frozenset([]				)

						UpperCamelCase_								=			True






						@property
						def 				A__      ( self			:		Optional[Any]		)  ->  Optional[Any]:


							'''simple docstring'''

							lowercase					:   str				  =1
							lowercase					:   List[str]				  =4
							lowercase					:   Union[str, Any]				  =(16, 16)

							lowercase					:   Dict				  =floats_tensor((batch_size, num_channels) + sizes     ,      rng=random.Random(0		)		).to(UpperCAmelCase		)
							return image






						def 				A__      ( self			:		Optional[Any]		)  ->  List[str]:


							'''simple docstring'''

							torch.manual_seed(0		)
							lowercase					:   Optional[int]				  =UNetaDConditionModel(
							    act_fn='''gelu'''     ,      attention_head_dim=8     ,      norm_num_groups=UpperCAmelCase     ,      block_out_channels=[32, 32, 64, 64]     ,      time_cond_proj_dim=160     ,      conv_in_kernel=1     ,      conv_out_kernel=1     ,      cross_attention_dim=32     ,      down_block_types=(
							        '''KDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							        '''KCrossAttnDownBlock2D''',
							    )     ,      in_channels=8     ,      mid_block_type=UpperCAmelCase     ,      only_cross_attention=UpperCAmelCase     ,      out_channels=5     ,      resnet_time_scale_shift='''scale_shift'''     ,      time_embedding_type='''fourier'''     ,      timestep_post_act='''gelu'''     ,      up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,      )
							lowercase					:   List[str]				  =AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]     ,      in_channels=3     ,      out_channels=3     ,      down_block_types=[
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							        '''DownEncoderBlock2D''',
							    ]     ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,      latent_channels=4     ,      )
							lowercase					:   Union[str, Any]				  =EulerDiscreteScheduler(prediction_type='''sample'''		)
							lowercase					:   int				  =CLIPTextConfig(
							    bos_token_id=0     ,      eos_token_id=2     ,      hidden_size=32     ,      intermediate_size=37     ,      layer_norm_eps=1e-05     ,      num_attention_heads=4     ,      num_hidden_layers=5     ,      pad_token_id=1     ,      vocab_size=1000     ,      hidden_act='''quick_gelu'''     ,      projection_dim=512     ,      )
							lowercase					:   Dict				  =CLIPTextModel(UpperCAmelCase		)
							lowercase					:   int				  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

							lowercase					:   List[Any]				  ={
							    '''unet''': model.eval(),
							    '''vae''': vae.eval(),
							    '''scheduler''': scheduler,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							}

							return components






						def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Optional[int]=0		)  ->  List[str]:


							'''simple docstring'''

							if str(UpperCAmelCase		).startswith('''mps'''		):
								lowercase					:   int				  =torch.manual_seed(UpperCAmelCase		)
							else:
								lowercase					:   Union[str, Any]				  =torch.Generator(device=UpperCAmelCase		).manual_seed(UpperCAmelCase		)
							lowercase					:   List[str]				  ={
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': self.dummy_image.cpu(),
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs






						def 				A__      ( self			:		str		)  ->  List[Any]:


							'''simple docstring'''

							lowercase					:   str				  ='''cpu'''

							lowercase					:   List[str]				  =self.get_dummy_components()
							lowercase					:   str				  =self.pipeline_class(**UpperCAmelCase		)
							pipe.to(UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase		)

							lowercase					:   List[str]				  =self.get_dummy_inputs(UpperCAmelCase		)
							lowercase					:   int				  =pipe(**UpperCAmelCase		).images
							lowercase					:   str				  =image[0, -3:, -3:, -1]

							self.assertEqual(image.shape     ,      (1, 256, 256, 3)		)
							lowercase					:   Dict				  =np.array(
							    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]		)
							lowercase					:   int				  =np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(UpperCAmelCase     ,      1e-3		)






						def 				A__      ( self			:		List[Any]		)  ->  List[str]:


							'''simple docstring'''

							super().test_attention_slicing_forward_pass(expected_max_diff=7e-3		)






						def 				A__      ( self			:		str		)  ->  Optional[Any]:


							'''simple docstring'''

							super().test_cpu_offload_forward_pass(expected_max_diff=3e-3		)






						def 				A__      ( self			:		str		)  ->  int:


							'''simple docstring'''

							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)






						def 				A__      ( self			:		str		)  ->  List[str]:


							'''simple docstring'''

							super().test_inference_batch_single_identical(expected_max_diff=7e-3		)






						def 				A__      ( self			:		Dict		)  ->  int:


							'''simple docstring'''

							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3		)






						def 				A__      ( self			:		int		)  ->  Tuple:


							'''simple docstring'''

							super().test_save_load_local(expected_max_difference=3e-3		)






						def 				A__      ( self			:		List[Any]		)  ->  Optional[int]:


							'''simple docstring'''

							super().test_save_load_optional_components(expected_max_difference=3e-3		)






						def 				A__      ( self			:		Dict		)  ->  Dict:


							'''simple docstring'''

							lowercase					:   Dict				  =[
							    '''DDIMScheduler''',
							    '''DDPMScheduler''',
							    '''PNDMScheduler''',
							    '''HeunDiscreteScheduler''',
							    '''EulerAncestralDiscreteScheduler''',
							    '''KDPM2DiscreteScheduler''',
							    '''KDPM2AncestralDiscreteScheduler''',
							    '''DPMSolverSDEScheduler''',
							]
							lowercase					:   int				  =self.get_dummy_components()
							lowercase					:   Dict				  =self.pipeline_class(**UpperCAmelCase		)

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase		)

							pipe.to(UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase		)
							lowercase					:   int				  =self.get_dummy_inputs(UpperCAmelCase		)
							lowercase					:   int				  =2

							lowercase					:   Optional[Any]				  =[]
							for scheduler_enum in KarrasDiffusionSchedulers:
								if scheduler_enum.name in skip_schedulers:
									# no sigma schedulers are not supported
									# no schedulers
									continue

								lowercase					:   Dict				  =getattr(UpperCAmelCase     ,      scheduler_enum.name		)
								lowercase					:   Tuple				  =scheduler_cls.from_config(pipe.scheduler.config		)
								lowercase					:   Dict				  =pipe(**UpperCAmelCase		)[0]
								outputs.append(UpperCAmelCase		)

							assert check_same_shape(UpperCAmelCase		)





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




						"""simple docstring"""




						def 				A__      ( self			:		Tuple		)  ->  Tuple:


							'''simple docstring'''

							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						def 				A__      ( self			:		Optional[Any]		)  ->  List[Any]:


							'''simple docstring'''

							lowercase					:   Tuple				  =torch.manual_seed(33		)

							lowercase					:   Dict				  =StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,      torch_dtype=torch.floataa		)
							pipe.to('''cuda'''		)

							lowercase					:   List[str]				  =StableDiffusionLatentUpscalePipeline.from_pretrained(
							    '''stabilityai/sd-x2-latent-upscaler'''     ,      torch_dtype=torch.floataa		)
							upscaler.to('''cuda'''		)

							lowercase					:   str				  ='''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

							lowercase					:   Dict				  =pipe(UpperCAmelCase     ,      generator=UpperCAmelCase     ,      output_type='''latent'''		).images

							lowercase					:   Union[str, Any]				  =upscaler(
							    prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      num_inference_steps=20     ,      guidance_scale=0     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      ).images[0]

							lowercase					:   str				  =load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''		)
							assert np.abs((expected_image - image).mean()		) < 5e-2






						def 				A__      ( self			:		Union[str, Any]		)  ->  List[Any]:


							'''simple docstring'''

							lowercase					:   str				  =torch.manual_seed(33		)

							lowercase					:   Optional[int]				  =StableDiffusionLatentUpscalePipeline.from_pretrained(
							    '''stabilityai/sd-x2-latent-upscaler'''     ,      torch_dtype=torch.floataa		)
							upscaler.to('''cuda'''		)

							lowercase					:   Union[str, Any]				  ='''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

							lowercase					:   Union[str, Any]				  =load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''		)

							lowercase					:   Optional[Any]				  =upscaler(
							    prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      num_inference_steps=20     ,      guidance_scale=0     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      ).images[0]

							lowercase					:   str				  =load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''		)
							assert np.abs((expected_image - image).max()		) < 5e-2











'''simple docstring'''



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       List[Any]					,      __A :       int					,      __A :       int			)       ->      Optional[int]:



 """simple docstring"""




 return [
     int(1_0_0_0 * (box[0] / width)			),
     int(1_0_0_0 * (box[1] / height)			),
     int(1_0_0_0 * (box[2] / width)			),
     int(1_0_0_0 * (box[3] / height)			),
 ]


def 							lowercase_				(    __A :       np.ndarray					,      __A :       Optional[str]					,      __A :       Optional[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   int				  =to_pil_image(__A			)
 lowercase  ,		lowercase					:   Tuple				  =pil_image.size
 lowercase					:   Optional[Any]				  =pytesseract.image_to_data(__A					,      lang=__A					,      output_type='''dict'''					,      config=__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

 # filter empty words and corresponding coordinates
 lowercase					:   Dict				  =[idx for idx, word in enumerate(__A			) if not word.strip()]
 lowercase					:   str				  =[word for idx, word in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   Optional[int]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   List[Any]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   str				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   int				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 lowercase					:   Tuple				  =[]
 for x, y, w, h in zip(__A					,      __A					,      __A					,      __A			):
  lowercase					:   str				  =[x, y, x + w, y + h]
  actual_boxes.append(__A			)

 # finally, normalize the bounding boxes
 lowercase					:   List[str]				  =[]
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(__A					,      __A					,      __A			)			)

 assert len(__A			) == len(__A			), "Not as many words as there are bounding boxes"

 return words, normalized_boxes





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		float = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = ""     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase					:   Tuple				  =size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   Optional[Any]				  =get_size_dict(UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =do_resize
       lowercase					:   List[Any]				  =size
       lowercase					:   List[str]				  =resample
       lowercase					:   Dict				  =do_rescale
       lowercase					:   str				  =rescale_value
       lowercase					:   Optional[int]				  =do_normalize
       lowercase					:   Any				  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase					:   Union[str, Any]				  =image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowercase					:   List[Any]				  =apply_ocr
       lowercase					:   Union[str, Any]				  =ocr_lang
       lowercase					:   str				  =tesseract_config






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Tuple				  =get_size_dict(UpperCAmelCase		)
       if "height" not in size or "width" not in size:
        raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
       lowercase					:   Optional[Any]				  =(size['''height'''], size['''width'''])
       return resize(UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       return rescale(UpperCAmelCase     ,      scale=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       return normalize(UpperCAmelCase     ,      mean=UpperCAmelCase     ,      std=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		ChannelDimension = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =do_resize if do_resize is not None else self.do_resize
       lowercase					:   Tuple				  =size if size is not None else self.size
       lowercase					:   Optional[int]				  =get_size_dict(UpperCAmelCase		)
       lowercase					:   List[str]				  =resample if resample is not None else self.resample
       lowercase					:   List[Any]				  =do_rescale if do_rescale is not None else self.do_rescale
       lowercase					:   List[Any]				  =rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase					:   Optional[int]				  =do_normalize if do_normalize is not None else self.do_normalize
       lowercase					:   List[Any]				  =image_mean if image_mean is not None else self.image_mean
       lowercase					:   Optional[int]				  =image_std if image_std is not None else self.image_std
       lowercase					:   Any				  =apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase					:   Any				  =ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase					:   Dict				  =tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase					:   str				  =make_list_of_images(UpperCAmelCase		)

       if not valid_images(UpperCAmelCase		):
        raise ValueError(
            '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
            '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

       if do_resize and size is None:
        raise ValueError('''Size must be specified if do_resize is True.'''		)

       if do_rescale and rescale_factor is None:
        raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''		)

       # All transformations expect numpy arrays.
       lowercase					:   Tuple				  =[to_numpy_array(UpperCAmelCase		) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
        requires_backends(self     ,      '''pytesseract'''		)
        lowercase					:   int				  =[]
        lowercase					:   Tuple				  =[]
        for image in images:
         lowercase  ,		lowercase					:   Dict				  =apply_tesseract(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
         words_batch.append(UpperCAmelCase		)
         boxes_batch.append(UpperCAmelCase		)

       if do_resize:
        lowercase					:   int				  =[self.resize(image=UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase		) for image in images]

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

       if do_normalize:
        lowercase					:   str				  =[self.normalize(image=UpperCAmelCase     ,      mean=UpperCAmelCase     ,      std=UpperCAmelCase		) for image in images]

       lowercase					:   Optional[Any]				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Dict				  =BatchFeature(data={'''pixel_values''': images}     ,      tensor_type=UpperCAmelCase		)

       if apply_ocr:
        lowercase					:   int				  =words_batch
        lowercase					:   List[str]				  =boxes_batch
       return data



