

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_snake_case						: Optional[Any]											=							(7_20, 12_80)  # Height, Width
_snake_case						: int											=							(0.4, 0.6)  # if height or width lower than this scale, drop it.
_snake_case						: str											=							1 / 1_00
_snake_case						: List[str]											=							''
_snake_case						: List[Any]											=							''
_snake_case						: List[Any]											=							''
_snake_case						: Dict											=							2_50





def    _A							(						)   -> None:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				get_dataset(lowercase_   , lowercase_  )
   for index in range(lowercase_  ):
      __SCREAMING_SNAKE_CASE       =				random.sample(range(len(lowercase_  )  )   , 4  )
      __SCREAMING_SNAKE_CASE       =				update_image_and_anno(
          lowercase_   , lowercase_   , lowercase_   , lowercase_   , lowercase_   , filter_scale=lowercase_   , )

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      __SCREAMING_SNAKE_CASE       =				random_chars(32  )
      __SCREAMING_SNAKE_CASE       =				path.split(os.sep  )[-1].rsplit("."   , 1  )[0]
      __SCREAMING_SNAKE_CASE       =				f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
      cva.imwrite(f'''{file_root}.jpg'''   , lowercase_   , [cva.IMWRITE_JPEG_QUALITY, 85]  )
      print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''  )
      __SCREAMING_SNAKE_CASE       =				[]
      for anno in new_annos:
         __SCREAMING_SNAKE_CASE       =				anno[3] - anno[1]
         __SCREAMING_SNAKE_CASE       =				anno[4] - anno[2]
         __SCREAMING_SNAKE_CASE       =				anno[1] + width / 2
         __SCREAMING_SNAKE_CASE       =				anno[2] + height / 2
         __SCREAMING_SNAKE_CASE       =				f'''{anno[0]} {x_center} {y_center} {width} {height}'''
         annos_list.append(lowercase_  )
      with open(f'''{file_root}.txt'''   , "w"  ) as outfile:
         outfile.write("\n".join(line for line in annos_list  )  )





def    _A							(						__snake_case  :Optional[int]   , __snake_case  :Any  )   -> tuple[list, list]:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				[]
   __SCREAMING_SNAKE_CASE       =				[]
   for label_file in glob.glob(os.path.join(lowercase_   , "*.txt"  )  ):
      __SCREAMING_SNAKE_CASE       =				label_file.split(os.sep  )[-1].rsplit("."   , 1  )[0]
      with open(lowercase_  ) as in_file:
         __SCREAMING_SNAKE_CASE       =				in_file.readlines()
      __SCREAMING_SNAKE_CASE       =				os.path.join(lowercase_   , f'''{label_name}.jpg'''  )

      __SCREAMING_SNAKE_CASE       =				[]
      for obj_list in obj_lists:
         __SCREAMING_SNAKE_CASE       =				obj_list.rstrip("\n"  ).split(" "  )
         __SCREAMING_SNAKE_CASE       =				float(obj[1]  ) - float(obj[3]  ) / 2
         __SCREAMING_SNAKE_CASE       =				float(obj[2]  ) - float(obj[4]  ) / 2
         __SCREAMING_SNAKE_CASE       =				float(obj[1]  ) + float(obj[3]  ) / 2
         __SCREAMING_SNAKE_CASE       =				float(obj[2]  ) + float(obj[4]  ) / 2

         boxes.append([int(obj[0]  ), xmin, ymin, xmax, ymax]  )
      if not boxes:
         continue
      img_paths.append(lowercase_  )
      labels.append(lowercase_  )
   return img_paths, labels





def    _A							(						__snake_case  :Any   , __snake_case  :Dict   , __snake_case  :str   , __snake_case  :Any   , __snake_case  :Union[str, Any]   , __snake_case  :Optional[Any] = 0.0   , )   -> tuple[list, list, str]:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				np.zeros([output_size[0], output_size[1], 3]   , dtype=np.uinta  )
   __SCREAMING_SNAKE_CASE       =				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __SCREAMING_SNAKE_CASE       =				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __SCREAMING_SNAKE_CASE       =				int(scale_x * output_size[1]  )
   __SCREAMING_SNAKE_CASE       =				int(scale_y * output_size[0]  )

   __SCREAMING_SNAKE_CASE       =				[]
   __SCREAMING_SNAKE_CASE       =				[]
   for i, index in enumerate(lowercase_  ):
      __SCREAMING_SNAKE_CASE       =				all_img_list[index]
      path_list.append(lowercase_  )
      __SCREAMING_SNAKE_CASE       =				all_annos[index]
      __SCREAMING_SNAKE_CASE       =				cva.imread(lowercase_  )
      if i == 0:  # top-left
         __SCREAMING_SNAKE_CASE       =				cva.resize(lowercase_   , (divid_point_x, divid_point_y)  )
         __SCREAMING_SNAKE_CASE       =				img
         for bbox in img_annos:
            __SCREAMING_SNAKE_CASE       =				bbox[1] * scale_x
            __SCREAMING_SNAKE_CASE       =				bbox[2] * scale_y
            __SCREAMING_SNAKE_CASE       =				bbox[3] * scale_x
            __SCREAMING_SNAKE_CASE       =				bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
      elif i == 1:  # top-right
         __SCREAMING_SNAKE_CASE       =				cva.resize(lowercase_   , (output_size[1] - divid_point_x, divid_point_y)  )
         __SCREAMING_SNAKE_CASE       =				img
         for bbox in img_annos:
            __SCREAMING_SNAKE_CASE       =				scale_x + bbox[1] * (1 - scale_x)
            __SCREAMING_SNAKE_CASE       =				bbox[2] * scale_y
            __SCREAMING_SNAKE_CASE       =				scale_x + bbox[3] * (1 - scale_x)
            __SCREAMING_SNAKE_CASE       =				bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
      elif i == 2:  # bottom-left
         __SCREAMING_SNAKE_CASE       =				cva.resize(lowercase_   , (divid_point_x, output_size[0] - divid_point_y)  )
         __SCREAMING_SNAKE_CASE       =				img
         for bbox in img_annos:
            __SCREAMING_SNAKE_CASE       =				bbox[1] * scale_x
            __SCREAMING_SNAKE_CASE       =				scale_y + bbox[2] * (1 - scale_y)
            __SCREAMING_SNAKE_CASE       =				bbox[3] * scale_x
            __SCREAMING_SNAKE_CASE       =				scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
      else:  # bottom-right
         __SCREAMING_SNAKE_CASE       =				cva.resize(
             lowercase_   , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)  )
         __SCREAMING_SNAKE_CASE       =				img
         for bbox in img_annos:
            __SCREAMING_SNAKE_CASE       =				scale_x + bbox[1] * (1 - scale_x)
            __SCREAMING_SNAKE_CASE       =				scale_y + bbox[2] * (1 - scale_y)
            __SCREAMING_SNAKE_CASE       =				scale_x + bbox[3] * (1 - scale_x)
            __SCREAMING_SNAKE_CASE       =				scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )

    # Remove bounding box small than scale of filter
   if filter_scale > 0:
      __SCREAMING_SNAKE_CASE       =				[
          anno
          for anno in new_anno
          if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
      ]

   return output_img, new_anno, path_list[0]





def    _A							(						__snake_case  :List[Any]  )   -> str:



   """simple docstring"""


   assert number_char > 1, "The number of character should greater than 1"
   __SCREAMING_SNAKE_CASE       =				ascii_lowercase + digits
   return "".join(random.choice(lowercase_  ) for _ in range(lowercase_  )  )


if __name__ == "__main__":
     main()
     print('DONE ✅')





import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=1_3				,     lowercase__=7				,     lowercase__=True				,     lowercase__=True				,     lowercase__=True				,     lowercase__=True				,     lowercase__=9_9				,     lowercase__=2_4				,     lowercase__=2				,     lowercase__=6				,     lowercase__=3_7				,     lowercase__="gelu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=5_1_2				,     lowercase__=1_6				,     lowercase__=2				,     lowercase__=0.0_2				,     lowercase__=3				,     lowercase__=None				,     lowercase__=1_0_0_0				,     ):
							__UpperCAmelCase			:							int	    =	parent
							__UpperCAmelCase			:							Optional[int]	    =	batch_size
							__UpperCAmelCase			:							Any	    =	seq_length
							__UpperCAmelCase			:							Dict	    =	is_training
							__UpperCAmelCase			:							str	    =	use_input_mask
							__UpperCAmelCase			:							Dict	    =	use_token_type_ids
							__UpperCAmelCase			:							Tuple	    =	use_labels
							__UpperCAmelCase			:							Union[str, Any]	    =	vocab_size
							__UpperCAmelCase			:							Tuple	    =	hidden_size
							__UpperCAmelCase			:							Dict	    =	num_hidden_layers
							__UpperCAmelCase			:							Optional[Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Union[str, Any]	    =	intermediate_size
							__UpperCAmelCase			:							Optional[int]	    =	hidden_act
							__UpperCAmelCase			:							int	    =	hidden_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	max_position_embeddings
							__UpperCAmelCase			:							Optional[Any]	    =	type_vocab_size
							__UpperCAmelCase			:							Optional[Any]	    =	type_sequence_label_size
							__UpperCAmelCase			:							str	    =	initializer_range
							__UpperCAmelCase			:							str	    =	num_labels
							__UpperCAmelCase			:							str	    =	scope
							__UpperCAmelCase			:							Union[str, Any]	    =	range_bbox


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size)

							__UpperCAmelCase			:							Optional[int]	    =	ids_tensor([self.batch_size, self.seq_length, 4]				,     self.range_bbox)
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]):
													for j in range(bbox.shape[1]):
																			if bbox[i, j, 3] < bbox[i, j, 1]:
																									__UpperCAmelCase			:							Optional[int]	    =	bbox[i, j, 3]
																									__UpperCAmelCase			:							str	    =	bbox[i, j, 1]
																									__UpperCAmelCase			:							Union[str, Any]	    =	t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																									__UpperCAmelCase			:							Optional[int]	    =	bbox[i, j, 2]
																									__UpperCAmelCase			:							int	    =	bbox[i, j, 0]
																									__UpperCAmelCase			:							Dict	    =	t

							__UpperCAmelCase			:							Tuple	    =	None
							if self.use_input_mask:
													__UpperCAmelCase			:							Any	    =	ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2)

							__UpperCAmelCase			:							List[Any]	    =	None
							if self.use_token_type_ids:
													__UpperCAmelCase			:							Tuple	    =	ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size)

							__UpperCAmelCase			:							List[str]	    =	None
							__UpperCAmelCase			:							Union[str, Any]	    =	None
							if self.use_labels:
													__UpperCAmelCase			:							Dict	    =	ids_tensor([self.batch_size]				,     self.type_sequence_label_size)
													__UpperCAmelCase			:							Optional[int]	    =	ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels)

							__UpperCAmelCase			:							int	    =	self.get_config()

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


	def  A(    self):
							return LiltConfig(
							    vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     )


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     ):
							__UpperCAmelCase			:							List[Any]	    =	LiltModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							List[Any]	    =	model(lowercase__				,     bbox=lowercase__				,     attention_mask=lowercase__				,     token_type_ids=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	model(lowercase__				,     bbox=lowercase__				,     token_type_ids=lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	model(lowercase__				,     bbox=lowercase__)

							self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape				,     (self.batch_size, self.hidden_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	self.num_labels
							__UpperCAmelCase			:							Dict	    =	LiltForTokenClassification(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Dict	    =	model(
							    lowercase__				,     bbox=lowercase__				,     attention_mask=lowercase__				,     token_type_ids=lowercase__				,     labels=lowercase__)
							self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     ):
							__UpperCAmelCase			:							Dict	    =	LiltForQuestionAnswering(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							List[Any]	    =	model(
							    lowercase__				,     bbox=lowercase__				,     attention_mask=lowercase__				,     token_type_ids=lowercase__				,     start_positions=lowercase__				,     end_positions=lowercase__				,     )
							self.parent.assertEqual(result.start_logits.shape				,     (self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape				,     (self.batch_size, self.seq_length))


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	self.prepare_config_and_inputs()
							(
							    (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      (
							    __UpperCAmelCase
							)    ,      
							)			:							Any	    =	config_and_inputs
							__UpperCAmelCase			:							int	    =	{
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		str										=						(
	    (
	        LiltModel,
	        LiltForSequenceClassification,
	        LiltForTokenClassification,
	        LiltForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase     :		str										=						(
	    {
	        '''feature-extraction''': LiltModel,
	        '''question-answering''': LiltForQuestionAnswering,
	        '''text-classification''': LiltForSequenceClassification,
	        '''token-classification''': LiltForTokenClassification,
	        '''zero-shot''': LiltForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase     :		Any										=						False
	_lowerCAmelCase     :		List[Any]										=						False


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
							return True


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	LiltModelTester(self)
							__UpperCAmelCase			:							List[Any]	    =	ConfigTester(self				,     config_class=lowercase__				,     hidden_size=3_7)


	def  A(    self):
							self.config_tester.run_common_tests()


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													__UpperCAmelCase			:							Any	    =	type
													self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowercase__)


	@slow
	def  A(    self):
							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__UpperCAmelCase			:							Optional[Any]	    =	LiltModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)






@require_torch
@slow
class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(lowercase__)

							__UpperCAmelCase			:							str	    =	torch.tensor([[1, 2]]				,     device=lowercase__)
							__UpperCAmelCase			:							List[str]	    =	torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,     device=lowercase__)

							# forward pass
							with torch.no_grad():
													__UpperCAmelCase			:							Dict	    =	model(input_ids=lowercase__				,     bbox=lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	torch.Size([1, 2, 7_6_8])
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.tensor(
							    [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,     device=lowercase__				,     )

							self.assertTrue(outputs.last_hidden_state.shape				,     lowercase__)
							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,     lowercase__				,     atol=1e-3))

