

"""simple docstring"""




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

import cva
import numpy as np

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





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

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

		__UpperCAmelCase      		= []
		for obj_list in obj_lists:
			__UpperCAmelCase      		= obj_list.rstrip('''\n'''    ).split(''' '''    )
			__UpperCAmelCase      		= float(obj[1]    ) - float(obj[3]    ) / 2
			__UpperCAmelCase      		= float(obj[2]    ) - float(obj[4]    ) / 2
			__UpperCAmelCase      		= float(obj[1]    ) + float(obj[3]    ) / 2
			__UpperCAmelCase      		= 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       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :str			,		snake_case_   :Union[str, Any]			,		snake_case_   :Optional[Any]			,		snake_case_   :str			,		snake_case_   :Optional[int] = 0.0			,		):
	__UpperCAmelCase      		= np.zeros([output_size[0], output_size[1], 3]			,		dtype=np.uinta    )
	__UpperCAmelCase      		= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	__UpperCAmelCase      		= scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	__UpperCAmelCase      		= int(scale_x * output_size[1]    )
	__UpperCAmelCase      		= int(scale_y * output_size[0]    )

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



