




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import jax
  import jaxlib

_lowerCamelCase			:	Dict							 =     get_logger()

_lowerCamelCase			:	Optional[dict]							 =     None


class        UpperCamelCase_					(				TensorFormatter[Mapping, '''jax.Array''', Mapping]  ):

    '''simple docstring'''






    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[int]=None      ,				UpperCAmelCase__   :			Any=None      ,				**UpperCAmelCase__   :			str)		->Dict:





           '''simple docstring'''

           super().__init__(features=UpperCAmelCase__)
           import jax
           from jaxlib.xla_client import Device

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  raise ValueError(
                      f"""Expected {device} to be a `str` not {type(UpperCAmelCase__)}, as `jaxlib.xla_extension.Device` """
                      '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
                      '''the device with `str()` to get its string identifier that will be internally mapped '''
                      '''to the actual `jaxlib.xla_extension.Device`.''')
           A__   				=					device if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) else str(jax.devices()[0])
           # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
           # with `pickle` nor with `dill`, so we need to use a global variable instead
           global DEVICE_MAPPING
           if DEVICE_MAPPING is None:
                  A__   				=					self._map_devices_to_str()
           if self.device not in list(DEVICE_MAPPING.keys()):
                  logger.warning(
                      f"""Device with string identifier {self.device} not listed among the available """
                      f"""devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default """
                      f"""device: {str(jax.devices()[0])}.""")
                  A__   				=					str(jax.devices()[0])
           A__   				=					jnp_array_kwargs


    @staticmethod
    def  SCREAMING_SNAKE_CASE							(    )		->Dict[str, "jaxlib.xla_extension.Device"]:





           '''simple docstring'''

           import jax

           return {str(UpperCAmelCase__): device for device in jax.devices()}


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			int)		->Union[str, Any]:





           '''simple docstring'''

           import jax
           import jax.numpy as jnp

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) and column:
                  if all(
                      isinstance(UpperCAmelCase__      ,				jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
                         return jnp.stack(UpperCAmelCase__      ,				axis=0)
           return column


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[Any])		->List[Any]:





           '''simple docstring'''

           import jax
           import jax.numpy as jnp

           if isinstance(UpperCAmelCase__      ,				(str, bytes, type(UpperCAmelCase__))):
                  return value
           elif isinstance(UpperCAmelCase__      ,				(np.character, np.ndarray)) and np.issubdtype(value.dtype      ,				np.character):
                  return value.tolist()

           A__   				=					{}

           if isinstance(UpperCAmelCase__      ,				(np.number, np.ndarray)) and np.issubdtype(value.dtype      ,				np.integer):
                  # the default int precision depends on the jax config
                  # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
                  if jax.config.jax_enable_xaa:
                         A__   				=					{'''dtype''': jnp.intaa}
                  else:
                         A__   				=					{'''dtype''': jnp.intaa}
           elif isinstance(UpperCAmelCase__      ,				(np.number, np.ndarray)) and np.issubdtype(value.dtype      ,				np.floating):
                  A__   				=					{'''dtype''': jnp.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                  import PIL.Image

                  if isinstance(UpperCAmelCase__      ,				PIL.Image.Image):
                         A__   				=					np.asarray(UpperCAmelCase__)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
           global DEVICE_MAPPING
           if DEVICE_MAPPING is None:
                  A__   				=					self._map_devices_to_str()

           with jax.default_device(DEVICE_MAPPING[self.device]):
                  # calling jnp.array on a np.ndarray does copy the data
                  # see https://github.com/google/jax/issues/4486
                  return jnp.array(UpperCAmelCase__      ,				**{**default_dtype, **self.jnp_array_kwargs})


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Any)		->Union[str, Any]:





           '''simple docstring'''

           import jax

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                  import torch

                  if isinstance(UpperCAmelCase__      ,				torch.Tensor):
                         return self._tensorize(data_struct.detach().cpu().numpy()[()])
           if hasattr(UpperCAmelCase__      ,				'''__array__''') and not isinstance(UpperCAmelCase__      ,				jax.Array):
                  A__   				=					data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(UpperCAmelCase__      ,				np.ndarray):
                  if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(UpperCAmelCase__) for substruct in data_struct])
           elif isinstance(UpperCAmelCase__      ,				(list, tuple)):
                  return self._consolidate([self.recursive_tensorize(UpperCAmelCase__) for substruct in data_struct])
           return self._tensorize(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			dict)		->Optional[int]:





           '''simple docstring'''

           return map_nested(self._recursive_tensorize      ,				UpperCAmelCase__      ,				map_list=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			pa.Table)		->Mapping:





           '''simple docstring'''

           A__   				=					self.numpy_arrow_extractor().extract_row(UpperCAmelCase__)
           A__   				=					self.python_features_decoder.decode_row(UpperCAmelCase__)
           return self.recursive_tensorize(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			pa.Table)		->"jax.Array":





           '''simple docstring'''

           A__   				=					self.numpy_arrow_extractor().extract_column(UpperCAmelCase__)
           A__   				=					self.python_features_decoder.decode_column(UpperCAmelCase__      ,				pa_table.column_names[0])
           A__   				=					self.recursive_tensorize(UpperCAmelCase__)
           A__   				=					self._consolidate(UpperCAmelCase__)
           return column





    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			pa.Table)		->Mapping:





           '''simple docstring'''

           A__   				=					self.numpy_arrow_extractor().extract_batch(UpperCAmelCase__)
           A__   				=					self.python_features_decoder.decode_batch(UpperCAmelCase__)
           A__   				=					self.recursive_tensorize(UpperCAmelCase__)
           for column_name in batch:
                  A__   				=					self._consolidate(batch[column_name])
           return batch








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

import cva
import numpy as np

# Parrameters
_lowerCamelCase			:	List[str]							 =     (720, 1280)  # Height, Width
_lowerCamelCase			:	Optional[Any]							 =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase			:	List[Any]							 =     1 / 100
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	Union[str, Any]							 =     250







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

              # 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"""				,       lowercase_				,       [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(lowercase_		)
              with open(f"""{file_root}.txt"""				,       '''w'''		) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					[]
       for label_file in glob.glob(os.path.join(lowercase_				,       '''*.txt'''		)		):
              A__   				=					label_file.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              with open(lowercase_		) as in_file:
                     A__   				=					in_file.readlines()
              A__   				=					os.path.join(lowercase_				,       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(lowercase_		)
              labels.append(lowercase_		)
       return img_paths, labels







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       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(lowercase_		):
              A__   				=					all_img_list[index]
              path_list.append(lowercase_		)
              A__   				=					all_annos[index]
              A__   				=					cva.imread(lowercase_		)
              if i == 0:  # top-left
                     A__   				=					cva.resize(lowercase_				,       (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(lowercase_				,       (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(lowercase_				,       (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(
                         lowercase_				,       (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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

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


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


