


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     					=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Union[str, Any]       			=  """t5"""
		lowerCamelCase_	:			Union[str, Any]       			=  ["""past_key_values"""]
		lowerCamelCase_	:			int       			=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






		def __init__(						self				:     List[str]   ,  UpperCAmelCase				:     List[str]=3_21_28   ,  UpperCAmelCase				:     Any=5_12   ,  UpperCAmelCase				:     Optional[Any]=64   ,  UpperCAmelCase				:     Dict=20_48   ,  UpperCAmelCase				:     Optional[int]=6   ,  UpperCAmelCase				:     int=None   ,  UpperCAmelCase				:     Tuple=8   ,  UpperCAmelCase				:     Optional[Any]=32   ,  UpperCAmelCase				:     Tuple=1_28   ,  UpperCAmelCase				:     int=0.1   ,  UpperCAmelCase				:     str=1E-6   ,  UpperCAmelCase				:     Tuple=1.0   ,  UpperCAmelCase				:     Union[str, Any]="relu"   ,  UpperCAmelCase				:     List[str]=True   ,  UpperCAmelCase				:     List[Any]=True   ,  UpperCAmelCase				:     Optional[int]=0   ,  UpperCAmelCase				:     Tuple=1   ,  **UpperCAmelCase				:     List[str]   ,  ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						vocab_size
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						d_model
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						d_kv
				SCREAMING_SNAKE_CASE_				:Any				     =						d_ff
				SCREAMING_SNAKE_CASE_				:Dict				     =						num_layers
				SCREAMING_SNAKE_CASE_				:str				     =						(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						num_heads
				SCREAMING_SNAKE_CASE_				:str				     =						relative_attention_num_buckets
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						relative_attention_max_distance
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						dropout_rate
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						layer_norm_epsilon
				SCREAMING_SNAKE_CASE_				:str				     =						initializer_factor
				SCREAMING_SNAKE_CASE_				:List[str]				     =						feed_forward_proj
				SCREAMING_SNAKE_CASE_				:str				     =						use_cache

				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.feed_forward_proj.split("-")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						act_info[-1]
				SCREAMING_SNAKE_CASE_				:int				     =						act_info[0] == "gated"

				if len(UpperCAmelCase) > 1 and act_info[0] != "gated" or len(UpperCAmelCase) > 2:
						raise ValueError(
						    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
						    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
						    "'gated-gelu' or 'relu'")

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						"gelu_new"

				super().__init__(
				    pad_token_id=UpperCAmelCase   ,  eos_token_id=UpperCAmelCase   ,  is_encoder_decoder=UpperCAmelCase   ,  **UpperCAmelCase   ,  )




class 						_UpperCAmelCase      (     lowercase							):






		@property
		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
						SCREAMING_SNAKE_CASE_				:str				     =						"past_encoder_sequence + sequence"
						SCREAMING_SNAKE_CASE_				:List[str]				     =						{0: "batch"}
						SCREAMING_SNAKE_CASE_				:Any				     =						{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						{0: "batch", 1: "decoder_sequence"}
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
						self.fill_with_past_key_values_(UpperCAmelCase   ,  direction="inputs")

				return common_inputs







		@property
		def _snake_case		(						self				:     Any):
				return 13










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

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE__     					=			(720, 1_280)  # Height, Width
SCREAMING_SNAKE_CASE__     					=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__     					=			1 / 100
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			250


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						get_dataset(a     , a )
		for index in range(a ):
				SCREAMING_SNAKE_CASE_				:Any				     =						random.sample(range(len(a ) )     , 4 )
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						update_image_and_anno(
				    a     , a     , a     , a     , a     , filter_scale=a     , )

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


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		for label_file in glob.glob(os.path.join(a     , "*.txt" ) ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						label_file.split(os.sep )[-1].rsplit("."     , 1 )[0]
				with open(a ) as in_file:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						in_file.readlines()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , F"{label_name}.jpg" )

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

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


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



		'''simple docstring'''




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

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



		'''simple docstring'''




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


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






