

import itertools
import string
from collections.abc import Generator, Iterable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Iterable[str]  ,      lowerCAmelCase:  int )   ->       Generator[tuple[str, ...], None, None]:
				_UpperCAmelCase       :		List[str] 		=    iter(lowerCAmelCase )
				while True:
								_UpperCAmelCase       :		Optional[Any] 		=    tuple(itertools.islice(lowerCAmelCase  ,      lowerCAmelCase ) )
								if not chunk:
												return
								yield chunk

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       str:
				_UpperCAmelCase       :		Optional[int] 		=    "".join([c.upper() for c in dirty if c in string.ascii_letters] )
				_UpperCAmelCase       :		Any 		=    ""

				if len(lowerCAmelCase ) < 2:
								return dirty

				for i in range(len(lowerCAmelCase ) - 1 ):
								clean += dirty[i]

								if dirty[i] == dirty[i + 1]:
												clean += "X"

				clean += dirty[-1]

				if len(lowerCAmelCase ) & 1:
								clean += "X"

				return clean

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       list[str]:
				# I and J are used interchangeably to allow
				# us to use a 5x5 table (25 letters)
				_UpperCAmelCase       :		int 		=    "ABCDEFGHIKLMNOPQRSTUVWXYZ"
				# we're using a list instead of a '2d' array because it makes the math
				# for setting up the table and doing the actual encoding/decoding simpler
				_UpperCAmelCase       :		Optional[Any] 		=    []

				# copy key chars into the table if they are in `alphabet` ignoring duplicates
				for char in key.upper():
								if char not in table and char in alphabet:
												table.append(lowerCAmelCase )

    # fill the rest of the table in with the remaining alphabet chars
				for char in alphabet:
								if char not in table:
												table.append(lowerCAmelCase )

				return table

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       str:
				_UpperCAmelCase       :		int 		=    generate_table(lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    prepare_input(lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    ""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase  ,      2 ):
								_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    divmod(table.index(lowerCAmelCase )  ,      5 )
								_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    divmod(table.index(lowerCAmelCase )  ,      5 )

								if rowa == rowa:
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
								elif cola == cola:
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
								else:  # rectangle
												ciphertext += table[rowa * 5 + cola]
												ciphertext += table[rowa * 5 + cola]

				return ciphertext





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       str:
				_UpperCAmelCase       :		Any 		=    generate_table(lowerCAmelCase )
				_UpperCAmelCase       :		Union[str, Any] 		=    ""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase  ,      2 ):
								_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    divmod(table.index(lowerCAmelCase )  ,      5 )
								_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    divmod(table.index(lowerCAmelCase )  ,      5 )

								if rowa == rowa:
												plaintext += table[rowa * 5 + (cola - 1) % 5]
												plaintext += table[rowa * 5 + (cola - 1) % 5]
								elif cola == cola:
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
								else:  # rectangle
												plaintext += table[rowa * 5 + cola]
												plaintext += table[rowa * 5 + cola]

				return plaintext





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    '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 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bloom"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "num_hidden_layers": "n_layer",
						    "num_attention_heads": "n_head",
						}


						def __init__(       self						,					A_=250880						,					A_=64						,					A_=2						,					A_=8						,					A_=1e-5						,					A_=0.02						,					A_=True						,					A_=1						,					A_=2						,					A_=False						,					A_=0.0						,					A_=0.0						,					A_=1						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab_size
										# Backward compatibility with n_embed kwarg
										_UpperCAmelCase       :		str 		=    kwargs.pop("n_embed"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    hidden_size if n_embed is None else n_embed
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Optional[Any] 		=    n_head
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		List[str] 		=    pretraining_tp
										_UpperCAmelCase       :		List[Any] 		=    apply_residual_connection_post_layernorm
										_UpperCAmelCase       :		int 		=    hidden_dropout
										_UpperCAmelCase       :		List[str] 		=    attention_dropout

										_UpperCAmelCase       :		int 		=    bos_token_id
										_UpperCAmelCase       :		str 		=    eos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    slow_but_exact

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







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.12"				)


						def __init__(       self						,					A_						,					A_ = "default"						,					A_ = None						,					A_ = False						,					):







										'''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       :		str 		=    0


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    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_(A_						,					direction="inputs"						,					inverted_values_shape=A_	)
														_UpperCAmelCase       :		Optional[int] 		=    {0: "batch", 1: "past_sequence + sequence"}
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    {0: "batch", 1: "sequence"}

										return common_inputs


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_layer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_head


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-3


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = -1						,					A_ = -1						,					A_ = False						,					A_ = None						,					):







										'''simple docstring'''


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

										_UpperCAmelCase       :		int 		=    common_inputs["attention_mask"]
										if self.use_past:
														_UpperCAmelCase       :		str 		=    ordered_inputs["attention_mask"].dtype
														_UpperCAmelCase       :		Optional[int] 		=    torch.cat(
														    [ordered_inputs["attention_mask"], torch.ones(A_						,					A_						,					dtype=A_	)]						,					dim=1	)

										return ordered_inputs





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 13


