





import copy
import os
from typing import Union

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


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    Union[str, Any]  							= {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}







class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "pix2struct_text_model"
							UpperCAmelCase_      						=   ["past_key_values"]
							UpperCAmelCase_      						=   {
							    "hidden_size": "hidden_size",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}






							def __init__( self     :					Tuple,			_UpperCAmelCase     :					int=5_0_2_4_4,			_UpperCAmelCase     :					str=7_6_8,			_UpperCAmelCase     :					Any=6_4,			_UpperCAmelCase     :					Tuple=2_0_4_8,			_UpperCAmelCase     :					List[Any]=1_2,			_UpperCAmelCase     :					List[Any]=1_2,			_UpperCAmelCase     :					Dict=3_2,			_UpperCAmelCase     :					Dict=1_2_8,			_UpperCAmelCase     :					Dict=0.1,			_UpperCAmelCase     :					List[str]=1E-6,			_UpperCAmelCase     :					Optional[int]=1.0,			_UpperCAmelCase     :					int="gelu_new",			_UpperCAmelCase     :					str=0,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Dict=0,			_UpperCAmelCase     :					List[str]=1,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					List[Any]=True,			**_UpperCAmelCase     :					Dict,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						vocab_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      int     =						d_kv
														SCREAMING_SNAKE_CASE__       :      Tuple     =						d_ff
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_layers
														SCREAMING_SNAKE_CASE__       :      Dict     =						num_heads
														SCREAMING_SNAKE_CASE__       :      Dict     =						relative_attention_num_buckets
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						relative_attention_max_distance
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						dropout_rate
														SCREAMING_SNAKE_CASE__       :      Dict     =						layer_norm_epsilon
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						initializer_factor
														SCREAMING_SNAKE_CASE__       :      Any     =						use_cache

														SCREAMING_SNAKE_CASE__       :      Dict     =						eos_token_id
														SCREAMING_SNAKE_CASE__       :      Any     =						decoder_start_token_id

														# for backwards compatibility
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						dense_act_fn

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






							@classmethod
							def  A_     ( cls     :					Optional[Any],			_UpperCAmelCase     :					Union[str, os.PathLike],			**_UpperCAmelCase     :					Optional[Any]						)				->			"PretrainedConfig":





														"""simple docstring"""
														cls._set_token_in_kwargs(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						cls.get_config_dict(_UpperCAmelCase,			**_UpperCAmelCase						)

														# get the text config dict if we are loading from Pix2StructConfig
														if config_dict.get("model_type"						) == "pix2struct":
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						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(_UpperCAmelCase,			**_UpperCAmelCase						)







class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "pix2struct_vision_model"






							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					Any=7_6_8,			_UpperCAmelCase     :					List[str]=7_6_8,			_UpperCAmelCase     :					Optional[int]=2_0_4_8,			_UpperCAmelCase     :					List[Any]=6_4,			_UpperCAmelCase     :					List[Any]=1_2,			_UpperCAmelCase     :					int=1_2,			_UpperCAmelCase     :					List[str]="gelu_new",			_UpperCAmelCase     :					Any=1E-6,			_UpperCAmelCase     :					str=0.0,			_UpperCAmelCase     :					List[str]=0.0,			_UpperCAmelCase     :					Dict=1E-10,			_UpperCAmelCase     :					int=1.0,			_UpperCAmelCase     :					Any=4_0_9_6,			_UpperCAmelCase     :					Any=3_2,			_UpperCAmelCase     :					List[str]=1_2_8,			**_UpperCAmelCase     :					str,			)				->			List[str]:





														"""simple docstring"""
														super().__init__(**_UpperCAmelCase						)

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






							@classmethod
							def  A_     ( cls     :					List[str],			_UpperCAmelCase     :					Union[str, os.PathLike],			**_UpperCAmelCase     :					Any						)				->			"PretrainedConfig":





														"""simple docstring"""
														cls._set_token_in_kwargs(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cls.get_config_dict(_UpperCAmelCase,			**_UpperCAmelCase						)

														# get the vision config dict if we are loading from Pix2StructConfig
														if config_dict.get("model_type"						) == "pix2struct":
																					SCREAMING_SNAKE_CASE__       :      str     =						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(_UpperCAmelCase,			**_UpperCAmelCase						)







class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "pix2struct"
							UpperCAmelCase_      						=   True






							def __init__( self     :					Any,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					List[Any]=None,			_UpperCAmelCase     :					List[Any]=1.0,			_UpperCAmelCase     :					Any=0.02,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					Any=False,			_UpperCAmelCase     :					Optional[Any]=True,			**_UpperCAmelCase     :					Union[str, Any],			)				->			Union[str, Any]:





														"""simple docstring"""
														super().__init__(tie_word_embeddings=_UpperCAmelCase,			is_encoder_decoder=_UpperCAmelCase,			**_UpperCAmelCase						)

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

														if vision_config is None:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}
																					logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						PixaStructTextConfig(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						PixaStructVisionConfig(**_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__       :      List[str]     =						initializer_factor
														SCREAMING_SNAKE_CASE__       :      int     =						initializer_range

														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.initializer_range
														SCREAMING_SNAKE_CASE__       :      str     =						self.initializer_range

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						is_vqa






							@classmethod
							def  A_     ( cls     :					str,			_UpperCAmelCase     :					PixaStructTextConfig,			_UpperCAmelCase     :					PixaStructVisionConfig,			**_UpperCAmelCase     :					Optional[int]						)				->			str:





														"""simple docstring"""
														return cls(text_config=text_config.to_dict(),			vision_config=vision_config.to_dict(),			**_UpperCAmelCase						)






							def  A_     ( self     :					Any						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						copy.deepcopy(self.__dict__						)
														SCREAMING_SNAKE_CASE__       :      int     =						self.text_config.to_dict()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.vision_config.to_dict()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.__class__.model_type
														return output













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

							# one length string
							SCREAMING_SNAKE_CASE__       :      str     =						0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						single_char_strings[ch]
																					SCREAMING_SNAKE_CASE__       :      Any     =						my_str / all_sum
																					my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )  # entropy formula.

    # print entropy
							print(f'''{round(-1 * my_fir_sum       ):.1f}'''       )

							# two len string
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sum(two_char_strings.values()       )
							SCREAMING_SNAKE_CASE__       :      int     =						0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						cha + cha
																					if sequence in two_char_strings:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						two_char_strings[sequence]
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						int(SCREAMING_SNAKE_CASE__       ) / all_sum
																												my_sec_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )

    # print second entropy
							print(f'''{round(-1 * my_sec_sum       ):.1f}'''       )

							# print the difference between them
							print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)       ):.1f}'''       )




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Counter()  # type: ignore
							SCREAMING_SNAKE_CASE__       :      Dict     =						Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0			,				len(SCREAMING_SNAKE_CASE__       ) - 1       ):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def _a (  )	->    str:

							'''simple docstring'''






							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()






