


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

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE							    =							(7_2_0, 1_2_8_0)  # Height, Width
SCREAMING_SNAKE_CASE							    =							(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE							    =							1 / 1_0_0
SCREAMING_SNAKE_CASE							    =							''
SCREAMING_SNAKE_CASE							    =							''
SCREAMING_SNAKE_CASE							    =							''
SCREAMING_SNAKE_CASE							    =							2_5_0




def  a   ():
   __a	,    __a					=  get_dataset(lowerCAmelCase__		,				lowerCAmelCase__   )
   for index in range(lowerCAmelCase__   ):
      __a					=  random.sample(range(len(lowerCAmelCase__   )   )		,				4   )
      __a	,    __a	,    __a					=  update_image_and_anno(
          lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				filter_scale=lowerCAmelCase__		,				)

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      __a					=  random_chars(32   )
      __a					=  path.split(os.sep   )[-1].rsplit("""."""		,				1   )[0]
      __a					=  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}'''   )
      __a					=  []
      for anno in new_annos:
         __a					=  anno[3] - anno[1]
         __a					=  anno[4] - anno[2]
         __a					=  anno[1] + width / 2
         __a					=  anno[2] + height / 2
         __a					=  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__   ):
   __a					=  []
   __a					=  []
   for label_file in glob.glob(os.path.join(lowerCAmelCase__		,				"""*.txt"""   )   ):
      __a					=  label_file.split(os.sep   )[-1].rsplit("""."""		,				1   )[0]
      with open(lowerCAmelCase__   ) as in_file:
         __a					=  in_file.readlines()
      __a					=  os.path.join(lowerCAmelCase__		,				f'''{label_name}.jpg'''   )

      __a					=  []
      for obj_list in obj_lists:
         __a					=  obj_list.rstrip("""\n"""   ).split(""" """   )
         __a					=  float(obj[1]   ) - float(obj[3]   ) / 2
         __a					=  float(obj[2]   ) - float(obj[4]   ) / 2
         __a					=  float(obj[1]   ) + float(obj[3]   ) / 2
         __a					=  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		,				):
   __a					=  np.zeros([output_size[0], output_size[1], 3]		,				dtype=np.uinta   )
   __a					=  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __a					=  scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __a					=  int(scale_x * output_size[1]   )
   __a					=  int(scale_y * output_size[0]   )

   __a					=  []
   __a					=  []
   for i, index in enumerate(lowerCAmelCase__   ):
      __a					=  all_img_list[index]
      path_list.append(lowerCAmelCase__   )
      __a					=  all_annos[index]
      __a					=  cva.imread(lowerCAmelCase__   )
      if i == 0:  # top-left
         __a					=  cva.resize(lowerCAmelCase__		,				(divid_point_x, divid_point_y)   )
         __a					=  img
         for bbox in img_annos:
            __a					=  bbox[1] * scale_x
            __a					=  bbox[2] * scale_y
            __a					=  bbox[3] * scale_x
            __a					=  bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
      elif i == 1:  # top-right
         __a					=  cva.resize(lowerCAmelCase__		,				(output_size[1] - divid_point_x, divid_point_y)   )
         __a					=  img
         for bbox in img_annos:
            __a					=  scale_x + bbox[1] * (1 - scale_x)
            __a					=  bbox[2] * scale_y
            __a					=  scale_x + bbox[3] * (1 - scale_x)
            __a					=  bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
      elif i == 2:  # bottom-left
         __a					=  cva.resize(lowerCAmelCase__		,				(divid_point_x, output_size[0] - divid_point_y)   )
         __a					=  img
         for bbox in img_annos:
            __a					=  bbox[1] * scale_x
            __a					=  scale_y + bbox[2] * (1 - scale_y)
            __a					=  bbox[3] * scale_x
            __a					=  scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
      else:  # bottom-right
         __a					=  cva.resize(
             lowerCAmelCase__		,				(output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
         __a					=  img
         for bbox in img_annos:
            __a					=  scale_x + bbox[1] * (1 - scale_x)
            __a					=  scale_y + bbox[2] * (1 - scale_y)
            __a					=  scale_x + bbox[3] * (1 - scale_x)
            __a					=  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:
      __a					=  [
          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__   ):
   assert number_char > 1, "The number of character should greater than 1"
   __a					=  ascii_lowercase + digits
   return "".join(random.choice(lowerCAmelCase__   ) for _ in range(lowerCAmelCase__   )   )


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







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
  from PIL import Image


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
         """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
         """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
     )
     _lowerCamelCase   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


     def __init__(	self  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



