






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

import cva
import numpy as np

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

def    SCREAMING_SNAKE_CASE_							(							)		->			None:






	"""simple docstring"""



	UpperCamelCase     ,      UpperCamelCase     :int          =	get_dataset(__magic_name__	,			__magic_name__					)
	for index in range(__magic_name__					):
		UpperCamelCase     :int          =	random.sample(range(len(__magic_name__					)					)	,			4					)
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	update_image_and_anno(
		    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			filter_scale=__magic_name__	,			)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  str					)		->			tuple[list, list]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	[]
	UpperCamelCase     :str          =	[]
	for label_file in glob.glob(os.path.join(__magic_name__	,			"""*.txt"""					)					):
		UpperCamelCase     :Optional[Any]          =	label_file.split(os.sep					)[-1].rsplit("""."""	,			1					)[0]
		with open(__magic_name__					) as in_file:
			UpperCamelCase     :Union[str, Any]          =	in_file.readlines()
		UpperCamelCase     :Union[str, Any]          =	os.path.join(__magic_name__	,			f"""{label_name}.jpg"""					)

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list	,			__magic_name__					:  list	,			__magic_name__					:  list[int]	,			__magic_name__					:  tuple[int, int]	,			__magic_name__					:  tuple[float, float]	,			__magic_name__					:  float = 0.0	,			)		->			tuple[list, list, str]:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	np.zeros([output_size[0], output_size[1], 3]	,			dtype=np.uinta					)
	UpperCamelCase     :Tuple          =	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCamelCase     :Optional[Any]          =	scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	UpperCamelCase     :Dict          =	int(scale_x * output_size[1]					)
	UpperCamelCase     :List[Any]          =	int(scale_y * output_size[0]					)

	UpperCamelCase     :Any          =	[]
	UpperCamelCase     :Optional[int]          =	[]
	for i, index in enumerate(__magic_name__					):
		UpperCamelCase     :Optional[int]          =	all_img_list[index]
		path_list.append(__magic_name__					)
		UpperCamelCase     :str          =	all_annos[index]
		UpperCamelCase     :List[Any]          =	cva.imread(__magic_name__					)
		if i == 0:  # top-left
			UpperCamelCase     :Union[str, Any]          =	cva.resize(__magic_name__	,			(divid_point_x, divid_point_y)					)
			UpperCamelCase     :Tuple          =	img
			for bbox in img_annos:
				UpperCamelCase     :str          =	bbox[1] * scale_x
				UpperCamelCase     :List[str]          =	bbox[2] * scale_y
				UpperCamelCase     :Dict          =	bbox[3] * scale_x
				UpperCamelCase     :List[Any]          =	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		elif i == 1:  # top-right
			UpperCamelCase     :int          =	cva.resize(__magic_name__	,			(output_size[1] - divid_point_x, divid_point_y)					)
			UpperCamelCase     :Tuple          =	img
			for bbox in img_annos:
				UpperCamelCase     :List[str]          =	scale_x + bbox[1] * (1 - scale_x)
				UpperCamelCase     :str          =	bbox[2] * scale_y
				UpperCamelCase     :Tuple          =	scale_x + bbox[3] * (1 - scale_x)
				UpperCamelCase     :Tuple          =	bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		elif i == 2:  # bottom-left
			UpperCamelCase     :List[str]          =	cva.resize(__magic_name__	,			(divid_point_x, output_size[0] - divid_point_y)					)
			UpperCamelCase     :Optional[Any]          =	img
			for bbox in img_annos:
				UpperCamelCase     :Tuple          =	bbox[1] * scale_x
				UpperCamelCase     :Dict          =	scale_y + bbox[2] * (1 - scale_y)
				UpperCamelCase     :List[Any]          =	bbox[3] * scale_x
				UpperCamelCase     :int          =	scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]					)
		else:  # bottom-right
			UpperCamelCase     :List[str]          =	cva.resize(
			    __magic_name__	,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)					)
			UpperCamelCase     :Any          =	img
			for bbox in img_annos:
				UpperCamelCase     :Any          =	scale_x + bbox[1] * (1 - scale_x)
				UpperCamelCase     :List[Any]          =	scale_y + bbox[2] * (1 - scale_y)
				UpperCamelCase     :List[Any]          =	scale_x + bbox[3] * (1 - scale_x)
				UpperCamelCase     :List[str]          =	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     :List[str]          =	[
		    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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			str:






	"""simple docstring"""



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


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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """openai/whisper-base"""
	snake_case__							:     Optional[int]     	=  (
	    """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
	    """transcribed text."""
	)
	snake_case__							:     Any     	=  """transcriber"""
	snake_case__							:     Optional[int]     	=  WhisperProcessor
	snake_case__							:     str     	=  WhisperForConditionalGeneration

	snake_case__							:     Optional[Any]     	=  ["""audio"""]
	snake_case__							:     Any     	=  ["""text"""]





	def 						_A (		self				:					str ,  __lowerCamelCase				:					Dict	):
		return self.pre_processor(__lowerCamelCase ,  return_tensors="""pt"""	).input_features





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[Any]	):
		return self.model.generate(inputs=__lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Optional[Any]	):
		return self.pre_processor.batch_decode(__lowerCamelCase ,  skip_special_tokens=__lowerCamelCase	)[0]
