
"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'gptj'
		lowercase__      =				{
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}



		def __init__( self    ,		__a=5_04_00    ,		__a=20_48    ,		__a=40_96    ,		__a=28    ,		__a=16    ,		__a=64    ,		__a=None    ,		__a="gelu_new"    ,		__a=0.0    ,		__a=0.0    ,		__a=0.0    ,		__a=1e-5    ,		__a=0.02    ,		__a=True    ,		__a=5_02_56    ,		__a=5_02_56    ,		__a=False    ,		**__a    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  n_positions
							_UpperCamelCase										=  n_embd
							_UpperCamelCase										=  n_layer
							_UpperCamelCase										=  n_head
							_UpperCamelCase										=  n_inner
							_UpperCamelCase										=  rotary_dim
							_UpperCamelCase										=  activation_function
							_UpperCamelCase										=  resid_pdrop
							_UpperCamelCase										=  embd_pdrop
							_UpperCamelCase										=  attn_pdrop
							_UpperCamelCase										=  layer_norm_epsilon
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  use_cache

							_UpperCamelCase										=  bos_token_id
							_UpperCamelCase										=  eos_token_id

							super().__init__(
							    bos_token_id=__a    ,		eos_token_id=__a    ,		tie_word_embeddings=__a    ,		**__a)






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a    ,		__a = "default"    ,		__a = None    ,		__a = False    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(__a    ,		task=__a    ,		patching_specs=__a    ,		use_past=__a)
							if not getattr(self._config    ,		'''pad_token_id'''    ,		__a):
												# TODO: how to do that better?
												_UpperCamelCase										=  0



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							_UpperCamelCase										=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
							if self.use_past:
												self.fill_with_past_key_values_(__a    ,		direction='''inputs''')
												_UpperCamelCase										=  {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}

							return common_inputs



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self._config.n_layer



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self._config.n_head



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = -1    ,		__a = -1    ,		__a = False    ,		__a = None    ,		)	->	Mapping[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  super(__a    ,		self).generate_dummy_inputs(
							    __a    ,		batch_size=__a    ,		seq_length=__a    ,		is_pair=__a    ,		framework=__a)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase										=  OrderedDict({'''input_ids''': common_inputs['''input_ids''']})

							# Need to add the past_keys
							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
												else:
																	import torch

																	_UpperCamelCase			,  _UpperCamelCase										=  common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	_UpperCamelCase										=  seqlen + 2
																	_UpperCamelCase										=  (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	_UpperCamelCase										=  [
																	    (torch.zeros(__a), torch.zeros(__a)) for _ in range(self.num_layers)
																	]

							_UpperCamelCase										=  common_inputs['''attention_mask''']
							if self.use_past:
												_UpperCamelCase										=  ordered_inputs['''attention_mask'''].dtype
												_UpperCamelCase										=  torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(__a    ,		__a    ,		dtype=__a)]    ,		dim=1)

							return ordered_inputs




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 13








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 				lowerCamelCase__		(      __snake_case     )							->		None:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  analyze_text(__snake_case     )
					_UpperCamelCase										=  list(''' ''' + ascii_lowercase     )
					# what is our total sum of probabilities.
					_UpperCamelCase										=  sum(single_char_strings.values()     )

					# one length string
					_UpperCamelCase										=  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:
															_UpperCamelCase										=  single_char_strings[ch]
															_UpperCamelCase										=  my_str / all_sum
															my_fir_sum += prob * math.loga(__snake_case     )  # entropy formula.

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

					# two len string
					_UpperCamelCase										=  sum(two_char_strings.values()     )
					_UpperCamelCase										=  0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															_UpperCamelCase										=  cha + cha
															if sequence in two_char_strings:
																				_UpperCamelCase										=  two_char_strings[sequence]
																				_UpperCamelCase										=  int(__snake_case     ) / all_sum
																				my_sec_sum += prob * math.loga(__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 				lowerCamelCase__		(      __snake_case     )							->		tuple[dict, dict]:


					"""simple docstring"""





					_UpperCamelCase										=  Counter()  # type: ignore
					_UpperCamelCase										=  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(__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 				lowerCamelCase__		(      )							->		Dict:


					"""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()






