





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


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    Dict  							= {
    '''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 						lowerCamelCase       (lowerCamelCase__      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "gptj"
							UpperCAmelCase_      						=   {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}






							def __init__( self     :					Any,			_UpperCAmelCase     :					Union[str, Any]=5_0_4_0_0,			_UpperCAmelCase     :					Any=2_0_4_8,			_UpperCAmelCase     :					Optional[Any]=4_0_9_6,			_UpperCAmelCase     :					Tuple=2_8,			_UpperCAmelCase     :					List[Any]=1_6,			_UpperCAmelCase     :					Any=6_4,			_UpperCAmelCase     :					str=None,			_UpperCAmelCase     :					Optional[Any]="gelu_new",			_UpperCAmelCase     :					Tuple=0.0,			_UpperCAmelCase     :					List[str]=0.0,			_UpperCAmelCase     :					Tuple=0.0,			_UpperCAmelCase     :					Optional[int]=1E-5,			_UpperCAmelCase     :					List[str]=0.02,			_UpperCAmelCase     :					int=True,			_UpperCAmelCase     :					Any=5_0_2_5_6,			_UpperCAmelCase     :					Optional[Any]=5_0_2_5_6,			_UpperCAmelCase     :					int=False,			**_UpperCAmelCase     :					Any,			)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						vocab_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						n_positions
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						n_embd
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						n_layer
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						n_head
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						n_inner
														SCREAMING_SNAKE_CASE__       :      Any     =						rotary_dim
														SCREAMING_SNAKE_CASE__       :      Any     =						activation_function
														SCREAMING_SNAKE_CASE__       :      Dict     =						resid_pdrop
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						embd_pdrop
														SCREAMING_SNAKE_CASE__       :      Dict     =						attn_pdrop
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						layer_norm_epsilon
														SCREAMING_SNAKE_CASE__       :      List[str]     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Tuple     =						use_cache

														SCREAMING_SNAKE_CASE__       :      Any     =						bos_token_id
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						eos_token_id

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







class 						lowerCamelCase       (lowerCamelCase__      ):







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					PretrainedConfig,			_UpperCAmelCase     :					str = "default",			_UpperCAmelCase     :					List[PatchingSpec] = None,			_UpperCAmelCase     :					bool = False,			)				->			int:





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






							@property
							def  A_     ( self     :					List[Any]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
														if self.use_past:
																					self.fill_with_past_key_values_(_UpperCAmelCase,			direction="inputs"						)
																					SCREAMING_SNAKE_CASE__       :      int     =						{0: "batch", 1: "past_sequence + sequence"}
														else:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						{0: "batch", 1: "sequence"}

														return common_inputs






							@property
							def  A_     ( self     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														return self._config.n_layer






							@property
							def  A_     ( self     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														return self._config.n_head






							def  A_     ( self     :					int,			_UpperCAmelCase     :					PreTrainedTokenizer,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					int = -1,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					Optional[TensorType] = None,			)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						super(_UpperCAmelCase,			self						).generate_dummy_inputs(
														    _UpperCAmelCase,			batch_size=_UpperCAmelCase,			seq_length=_UpperCAmelCase,			is_pair=_UpperCAmelCase,			framework=_UpperCAmelCase						)

														# We need to order the input in the way they appears in the forward()
														SCREAMING_SNAKE_CASE__       :      str     =						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

																												SCREAMING_SNAKE_CASE__       :      Dict     =						common_inputs["input_ids"].shape
																												# Not using the same length for past_key_values
																												SCREAMING_SNAKE_CASE__       :      Any     =						seqlen + 2
																												SCREAMING_SNAKE_CASE__       :      int     =						(
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												SCREAMING_SNAKE_CASE__       :      int     =						[
																												    (torch.zeros(_UpperCAmelCase						), torch.zeros(_UpperCAmelCase						)) for _ in range(self.num_layers						)
																												]

														SCREAMING_SNAKE_CASE__       :      Tuple     =						common_inputs["attention_mask"]
														if self.use_past:
																					SCREAMING_SNAKE_CASE__       :      int     =						ordered_inputs["attention_mask"].dtype
																					SCREAMING_SNAKE_CASE__       :      Any     =						torch.cat(
																					    [ordered_inputs["attention_mask"], torch.ones(_UpperCAmelCase,			_UpperCAmelCase,			dtype=_UpperCAmelCase						)],			dim=1						)

														return ordered_inputs






							@property
							def  A_     ( self     :					str						)				->			Dict:





														"""simple docstring"""
														return 1_3













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()






