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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''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     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	int						="gptj"
  SCREAMING_SNAKE_CASE_	:	List[str]						={
      "max_position_embeddings": "n_positions",
      "hidden_size": "n_embd",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=5_0400   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=4096   ,				SCREAMING_SNAKE_CASE_=28   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=64   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_="gelu_new"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=1E-5   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=5_0256   ,				SCREAMING_SNAKE_CASE_=5_0256   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[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=SCREAMING_SNAKE_CASE_   ,				eos_token_id=SCREAMING_SNAKE_CASE_   ,				tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)



class     _A   (    __UpperCamelCase							):






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





       '''simple docstring'''





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






  @property
  def 		_a					(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_(SCREAMING_SNAKE_CASE_   ,				direction='''inputs'''	)
            UpperCamelCase__										=      {0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
            UpperCamelCase__										=      {0: '''batch''', 1: '''sequence'''}

       return common_inputs






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return self._config.n_layer






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return self._config.n_head






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





       '''simple docstring'''





       UpperCamelCase__										=      super(SCREAMING_SNAKE_CASE_   ,				self	).generate_dummy_inputs(
           SCREAMING_SNAKE_CASE_   ,				batch_size=SCREAMING_SNAKE_CASE_   ,				seq_length=SCREAMING_SNAKE_CASE_   ,				is_pair=SCREAMING_SNAKE_CASE_   ,				framework=SCREAMING_SNAKE_CASE_	)

       # 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(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) 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(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				dtype=SCREAMING_SNAKE_CASE_	)]   ,				dim=1	)

       return ordered_inputs






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return 13




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     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(A							)  # 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(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # 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  __UpperCamelCase    (						A							):
     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(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     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()



