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

import cva
SCREAMING_SNAKE_CASE__		:       List[Any]       						=       ""
SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       ""
SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       ""
SCREAMING_SNAKE_CASE__		:       int       						=       1  # (0 is vertical, 1 is horizontal)







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		get_dataset(lowercase__  ,	lowercase__			)
     print('Processing...'			)
     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		update_image_and_anno(lowercase__  ,	lowercase__  ,	lowercase__			)

     for index, image in enumerate(lowercase__			):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		random_chars(32			)
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		paths[index].split(os.sep			)[-1].rsplit('.'  ,	1			)[0]
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
          cva.imwrite(f'''/{file_root}.jpg'''  ,	lowercase__  ,	[cva.IMWRITE_JPEG_QUALITY, 85]			)
          print(f'''Success {index+1}/{len(lowercase__			)} with {file_name}'''			)
          SCREAMING_SNAKE_CASE__					:					str      =		[]
          for anno in new_annos[index]:
               SCREAMING_SNAKE_CASE__					:					Optional[int]      =		f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
               annos_list.append(lowercase__			)
          with open(f'''/{file_root}.txt'''  ,	'w'			) as outfile:
               outfile.write('\n'.join(line for line in annos_list			)			)







def      _a					(   lowercase__	:      str  ,	lowercase__	:      str			):







     '''simple docstring'''


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

          SCREAMING_SNAKE_CASE__					:					str      =		[]
          for obj_list in obj_lists:
               SCREAMING_SNAKE_CASE__					:					List[Any]      =		obj_list.rstrip('\n'			).split(' '			)
               boxes.append(
                   [
                       int(obj[0]			),
                       float(obj[1]			),
                       float(obj[2]			),
                       float(obj[3]			),
                       float(obj[4]			),
                   ]			)
          if not boxes:
               continue
          img_paths.append(lowercase__			)
          labels.append(lowercase__			)
     return img_paths, labels







def      _a					(   lowercase__	:      list  ,	lowercase__	:      list  ,	lowercase__	:      int = 1			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		[]
     SCREAMING_SNAKE_CASE__					:					str      =		[]
     SCREAMING_SNAKE_CASE__					:					int      =		[]
     for idx in range(len(lowercase__			)			):
          SCREAMING_SNAKE_CASE__					:					int      =		[]
          SCREAMING_SNAKE_CASE__					:					Tuple      =		img_list[idx]
          path_list.append(lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Any      =		anno_list[idx]
          SCREAMING_SNAKE_CASE__					:					List[str]      =		cva.imread(lowercase__			)
          if flip_type == 1:
               SCREAMING_SNAKE_CASE__					:					int      =		cva.flip(lowercase__  ,	lowercase__			)
               for bbox in img_annos:
                    SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
          elif flip_type == 0:
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		cva.flip(lowercase__  ,	lowercase__			)
               for bbox in img_annos:
                    SCREAMING_SNAKE_CASE__					:					Dict      =		1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
          new_annos_lists.append(lowercase__			)
          new_imgs_list.append(lowercase__			)
     return new_imgs_list, new_annos_lists, path_list







def      _a					(   lowercase__	:      int = 32			):







     '''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(lowercase__			) for _ in range(lowercase__			)			)


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










import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      int         =    {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Optional[int]  		=					"""pix2struct_text_model"""
   _UpperCamelCase			:    Dict  		=					["""past_key_values"""]
   _UpperCamelCase			:    Union[str, Any]  		=					{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }





   def __init__(			self       ,      snake_case=50_244       ,      snake_case=768       ,      snake_case=64       ,      snake_case=2_048       ,      snake_case=12       ,      snake_case=12       ,      snake_case=32       ,      snake_case=128       ,      snake_case=0.1       ,      snake_case=1E-6       ,      snake_case=1.0       ,      snake_case="gelu_new"       ,      snake_case=0       ,      snake_case=False       ,      snake_case=0       ,      snake_case=1       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      a__						:    Tuple   		=	vocab_size
      a__						:    List[str]   		=	hidden_size
      a__						:    Optional[Any]   		=	d_kv
      a__						:    List[str]   		=	d_ff
      a__						:    Optional[int]   		=	num_layers
      a__						:    Tuple   		=	num_heads
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    Optional[int]   		=	relative_attention_max_distance
      a__						:    Optional[int]   		=	dropout_rate
      a__						:    List[Any]   		=	layer_norm_epsilon
      a__						:    List[str]   		=	initializer_factor
      a__						:    Any   		=	use_cache

      a__						:    Any   		=	eos_token_id
      a__						:    List[Any]   		=	decoder_start_token_id

      # for backwards compatibility
      a__						:    Tuple   		=	dense_act_fn

      super().__init__(
          pad_token_id=snake_case       ,      eos_token_id=snake_case       ,      decoder_start_token_id=snake_case       ,      tie_word_embeddings=snake_case       ,      is_decoder=snake_case       ,      **snake_case       ,      )





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    Optional[Any]   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

      # get the text config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    Optional[int]   		=	config_dict["text_config"]

      if "model_type" in config_dict and hasattr(cls       ,      "model_type"   ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

      return cls.from_dict(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					"""pix2struct_vision_model"""





   def __init__(			self       ,      snake_case=768       ,      snake_case=768       ,      snake_case=2_048       ,      snake_case=64       ,      snake_case=12       ,      snake_case=12       ,      snake_case="gelu_new"       ,      snake_case=1E-6       ,      snake_case=0.0       ,      snake_case=0.0       ,      snake_case=1E-10       ,      snake_case=1.0       ,      snake_case=4_096       ,      snake_case=32       ,      snake_case=128       ,      **snake_case       ,      )					->	Optional[Any]:
      """simple docstring"""





      super().__init__(**snake_case   )

      a__						:    Optional[Any]   		=	hidden_size
      a__						:    Tuple   		=	patch_embed_hidden_size
      a__						:    Tuple   		=	d_ff
      a__						:    List[Any]   		=	dropout_rate
      a__						:    Optional[Any]   		=	num_hidden_layers
      a__						:    Optional[Any]   		=	num_attention_heads
      a__						:    List[str]   		=	initializer_range
      a__						:    List[str]   		=	initializer_factor
      a__						:    List[Any]   		=	attention_dropout
      a__						:    Optional[int]   		=	layer_norm_eps
      a__						:    int   		=	dense_act_fn
      a__						:    Optional[Any]   		=	seq_len
      a__						:    List[Any]   		=	relative_attention_num_buckets
      a__						:    int   		=	relative_attention_max_distance
      a__						:    int   		=	d_kv





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      **snake_case   )					->	"PretrainedConfig":
      """simple docstring"""





      cls._set_token_in_kwargs(snake_case   )

      a__		,  a__						:    int   		=	cls.get_config_dict(snake_case       ,      **snake_case   )

      # get the vision config dict if we are loading from Pix2StructConfig
      if config_dict.get("model_type"   ) == "pix2struct":
         a__						:    int   		=	config_dict["vision_config"]

      if "model_type" in config_dict and hasattr(cls       ,      "model_type"   ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

      return cls.from_dict(snake_case       ,      **snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					"""pix2struct"""
   _UpperCamelCase			:    Dict  		=					True





   def __init__(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=1.0       ,      snake_case=0.02       ,      snake_case=False       ,      snake_case=False       ,      snake_case=True       ,      **snake_case       ,      )					->	Optional[int]:
      """simple docstring"""





      super().__init__(tie_word_embeddings=snake_case       ,      is_encoder_decoder=snake_case       ,      **snake_case   )

      if text_config is None:
         a__						:    Optional[Any]   		=	{}
         logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."   )

      if vision_config is None:
         a__						:    List[Any]   		=	{}
         logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."   )

      a__						:    List[str]   		=	PixaStructTextConfig(**snake_case   )
      a__						:    List[str]   		=	PixaStructVisionConfig(**snake_case   )

      a__						:    Tuple   		=	self.text_config.decoder_start_token_id
      a__						:    int   		=	self.text_config.pad_token_id
      a__						:    List[Any]   		=	self.text_config.eos_token_id

      a__						:    str   		=	initializer_factor
      a__						:    Dict   		=	initializer_range

      a__						:    Optional[int]   		=	self.initializer_range
      a__						:    Union[str, Any]   		=	self.initializer_range

      a__						:    Union[str, Any]   		=	is_vqa





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case       ,      snake_case       ,      **snake_case   )					->	Dict:
      """simple docstring"""





      return cls(text_config=text_config.to_dict()       ,      vision_config=vision_config.to_dict()       ,      **snake_case   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	copy.deepcopy(self.__dict__   )
      a__						:    List[str]   		=	self.text_config.to_dict()
      a__						:    Union[str, Any]   		=	self.vision_config.to_dict()
      a__						:    List[Any]   		=	self.__class__.model_type
      return output

