






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

import cva
import numpy as np

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






def   a			(						):






				'''simple docstring'''
				lowercase__    =			get_dataset(_lowerCAmelCase		,   _lowerCAmelCase				)
				for index in range(_lowerCAmelCase				):
								lowercase__    =			random.sample(range(len(_lowerCAmelCase				)				)		,   4				)
								lowercase__    =			update_image_and_anno(
								    _lowerCAmelCase		,   _lowerCAmelCase		,   _lowerCAmelCase		,   _lowerCAmelCase		,   _lowerCAmelCase		,   filter_scale=_lowerCAmelCase		,   )

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






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






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

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

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






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ = 0.0		,   ):






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

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






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


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 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=7       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=99       ,							A_=24       ,							A_=2       ,							A_=6       ,							A_=37       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=16       ,							A_=2       ,							A_=0.02       ,							A_=3       ,							A_=None       ,							A_=1000       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   parent
       UpperCamelCase	:					List[Any]       	=   batch_size
       UpperCamelCase	:					Dict       	=   seq_length
       UpperCamelCase	:					Tuple       	=   is_training
       UpperCamelCase	:					Union[str, Any]       	=   use_input_mask
       UpperCamelCase	:					Tuple       	=   use_token_type_ids
       UpperCamelCase	:					Optional[Any]       	=   use_labels
       UpperCamelCase	:					str       	=   vocab_size
       UpperCamelCase	:					Optional[int]       	=   hidden_size
       UpperCamelCase	:					Any       	=   num_hidden_layers
       UpperCamelCase	:					Optional[Any]       	=   num_attention_heads
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Optional[Any]       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   hidden_dropout_prob
       UpperCamelCase	:					Union[str, Any]       	=   attention_probs_dropout_prob
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					str       	=   type_vocab_size
       UpperCamelCase	:					Optional[int]       	=   type_sequence_label_size
       UpperCamelCase	:					Dict       	=   initializer_range
       UpperCamelCase	:					int       	=   num_labels
       UpperCamelCase	:					Optional[int]       	=   scope
       UpperCamelCase	:					int       	=   range_bbox





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size   )

       UpperCamelCase	:					Any       	=   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	:					Union[str, Any]       	=   bbox[i, j, 3]
          UpperCamelCase	:					int       	=   bbox[i, j, 1]
          UpperCamelCase	:					int       	=   t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          UpperCamelCase	:					List[str]       	=   bbox[i, j, 2]
          UpperCamelCase	:					Optional[int]       	=   bbox[i, j, 0]
          UpperCamelCase	:					Optional[Any]       	=   t

       UpperCamelCase	:					Dict       	=   None
       if self.use_input_mask:
        UpperCamelCase	:					Optional[int]       	=   ids_tensor([self.batch_size, self.seq_length]       ,							vocab_size=2   )

       UpperCamelCase	:					str       	=   None
       if self.use_token_type_ids:
        UpperCamelCase	:					str       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.type_vocab_size   )

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

       UpperCamelCase	:					List[Any]       	=   self.get_config()

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   LiltModel(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					str       	=   model(A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_   )
       UpperCamelCase	:					Optional[int]       	=   model(A_       ,							bbox=A_       ,							token_type_ids=A_   )
       UpperCamelCase	:					Any       	=   model(A_       ,							bbox=A_   )

       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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.num_labels
       UpperCamelCase	:					Dict       	=   LiltForTokenClassification(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Dict       	=   model(
           A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							labels=A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   LiltForQuestionAnswering(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					List[str]       	=   model(
           A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							start_positions=A_       ,							end_positions=A_       ,							)
       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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    
       )	:					Tuple       	=   config_and_inputs
       UpperCamelCase	:					Tuple       	=   {
           "input_ids": input_ids,
           "bbox": bbox,
           "token_type_ids": token_type_ids,
           "attention_mask": input_mask,
       }
       return config, inputs_dict



@require_torch
class 					A__   (       __snake_case       ,   __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Union[str, Any]						    =   (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase	:Optional[Any]						    =   (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase	:Dict						    =   False
      _UpperCAmelCase	:Union[str, Any]						    =   False





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       return True





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   LiltModelTester(self   )
       UpperCamelCase	:					Optional[int]       	=   ConfigTester(self       ,							config_class=A_       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCamelCase	:					Union[str, Any]       	=   type
        self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase	:					Dict       	=   LiltModel.from_pretrained(A_   )
        self.assertIsNotNone(A_   )



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"   ).to(A_   )

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

       # forward pass
       with torch.no_grad():
        UpperCamelCase	:					Optional[int]       	=   model(input_ids=A_       ,							bbox=A_   )

       UpperCamelCase	:					List[str]       	=   torch.Size([1, 2, 768]   )
       UpperCamelCase	:					Any       	=   torch.tensor(
           [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]       ,							device=A_       ,							)

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