




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

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

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


__A								=       logging.get_logger(__name__)

__A								=       {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''bloom'''
			snake_case_  						=			['''past_key_values''']
			snake_case_  						=			{
			    '''num_hidden_layers''': '''n_layer''',
			    '''num_attention_heads''': '''n_head''',
			}


			def __init__( self     ,		lowerCamelCase__=250_880     ,		lowerCamelCase__=64     ,		lowerCamelCase__=2     ,		lowerCamelCase__=8     ,		lowerCamelCase__=1e-5     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=True     ,		lowerCamelCase__=1     ,		lowerCamelCase__=2     ,		lowerCamelCase__=False     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=1     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       vocab_size
							# Backward compatibility with n_embed kwarg
							__lowerCamelCase									=       kwargs.pop('n_embed'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       hidden_size if n_embed is None else n_embed
							__lowerCamelCase									=       n_layer
							__lowerCamelCase									=       n_head
							__lowerCamelCase									=       layer_norm_epsilon
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       pretraining_tp
							__lowerCamelCase									=       apply_residual_connection_post_layernorm
							__lowerCamelCase									=       hidden_dropout
							__lowerCamelCase									=       attention_dropout

							__lowerCamelCase									=       bos_token_id
							__lowerCamelCase									=       eos_token_id
							__lowerCamelCase									=       slow_but_exact

							super().__init__(bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		**lowerCamelCase__				)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			version.parse('''1.12'''					)


			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = "default"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = False     ,		)       -> Union[str, Any]:



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


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



							'''simple docstring'''
							__lowerCamelCase									=       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
							if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(lowerCamelCase__     ,		direction='inputs'     ,		inverted_values_shape=lowerCamelCase__				)
											__lowerCamelCase									=       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
											__lowerCamelCase									=       {0: 'batch', 1: 'sequence'}

							return common_inputs


			@property
			def  lowercase_       ( self				)       -> int:



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


			@property
			def  lowercase_       ( self				)       -> int:



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


			@property
			def  lowercase_       ( self				)       -> float:



							'''simple docstring'''
							return 1e-3


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



							'''simple docstring'''
							__lowerCamelCase									=       super(lowerCamelCase__     ,		self				).generate_dummy_inputs(
							    lowerCamelCase__     ,		batch_size=lowerCamelCase__     ,		seq_length=lowerCamelCase__     ,		is_pair=lowerCamelCase__     ,		framework=lowerCamelCase__				)

							# We need to order the input in the way they appears in the forward()
							__lowerCamelCase									=       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

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

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

							return ordered_inputs






			@property
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							return 13








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


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

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

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

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # 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_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       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(UpperCamelCase__ ) - 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()


