



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

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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str       						="""gpt_neo"""
				UpperCamelCase__			:   Dict       						=["""past_key_values"""]
				UpperCamelCase__			:   Tuple       						={"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




				def __init__(  self   :  str,   __lowercase   :  Tuple=5_0257,   __lowercase   :  List[str]=2048,   __lowercase   :  Tuple=2048,   __lowercase   :  List[str]=24,   __lowercase   :  Optional[int]=[[["global", "local"], 12]],   __lowercase   :  List[Any]=16,   __lowercase   :  Tuple=None,   __lowercase   :  Optional[int]=256,   __lowercase   :  List[Any]="gelu_new",   __lowercase   :  Any=0.0,   __lowercase   :  Optional[int]=0.0,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Tuple=0.1,   __lowercase   :  Tuple=1e-5,   __lowercase   :  List[str]=0.02,   __lowercase   :  Any=True,   __lowercase   :  List[Any]=5_0256,   __lowercase   :  int=5_0256,   **__lowercase   :  Any,   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			max_position_embeddings
							lowercase__   					=			hidden_size
							lowercase__   					=			num_layers
							lowercase__   					=			num_heads
							lowercase__   					=			intermediate_size
							lowercase__   					=			window_size
							lowercase__   					=			activation_function
							lowercase__   					=			resid_dropout
							lowercase__   					=			embed_dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			classifier_dropout
							lowercase__   					=			layer_norm_epsilon
							lowercase__   					=			initializer_range
							lowercase__   					=			use_cache

							lowercase__   					=			bos_token_id
							lowercase__   					=			eos_token_id

							lowercase__   					=			attention_types
							lowercase__   					=			self.expand_attention_types_params(__lowercase  )

							if len(self.attention_layers  ) != self.num_layers:
										raise ValueError(
										    "Configuration for convolutional module is incorrect. "
										    "It is required that `len(config.attention_layers)` == `config.num_layers` "
										    F'''but is `len(config.attention_layers) = {len(self.attention_layers  )}`, '''
										    F'''`config.num_layers = {self.num_layers}`. '''
										    "`config.attention_layers` is prepared using `config.attention_types`. "
										    "Please verify the value of `config.attention_types` argument."  )

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




				@staticmethod
				def 							A__ (  __lowercase   :  Tuple  ):
							lowercase__   					=			[]
							for item in attention_types:
										for _ in range(item[1]  ):
													attentions.extend(item[0]  )
							return attentions



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			import torch

			lowercase__   					=			input.size()
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			shape[dimension]

			lowercase__   					=			torch.arange(0  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			torch.div(sizedim - size  ,					SCREAMING_SNAKE_CASE_  ,					rounding_mode="floor"					) + 1
			lowercase__   					=			torch.arange(SCREAMING_SNAKE_CASE_					) + low_indices[:min_length][:, None]

			lowercase__   					=			[slice(SCREAMING_SNAKE_CASE_					)] * rank
			lowercase__   					=			indices
			lowercase__   					=			input[s]

			lowercase__   					=			list(range(0  ,					rank + 1					)					)
			perm.append(perm.pop(dimension + 1					)					)

			return sliced.permute(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			import torch

			lowercase__   					=			torch.arange(1  ,					SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			torch.remainder(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			remainders == 0
			lowercase__   					=			candidates[divisor_indices]
			lowercase__   					=			torch.max(SCREAMING_SNAKE_CASE_					)
			return largest_divisor, torch.div(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					rounding_mode="floor"					)




class 			_snake_case     (   lowercase__):




				@property
				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}  )
							if self.use_past:
										self.fill_with_past_key_values_(__lowercase,   direction="inputs"  )
										lowercase__   					=			{0: "batch", 1: "past_sequence + sequence"}
							else:
										lowercase__   					=			{0: "batch", 1: "sequence"}

							return common_inputs




				@property
				def 							A__ (  self   :  Union[str, Any]  ):
							return self._config.num_heads




				def 							A__ (  self   :  str,   __lowercase   :  PreTrainedTokenizer,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   ):
							lowercase__   					=			super(__lowercase,   self  ).generate_dummy_inputs(
							    __lowercase,   batch_size=__lowercase,   seq_length=__lowercase,   is_pair=__lowercase,   framework=__lowercase  )

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

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

							lowercase__   					=			common_inputs["attention_mask"]
							if self.use_past:
										lowercase__   					=			ordered_inputs["attention_mask"].dtype
										lowercase__   					=			torch.cat(
										    [ordered_inputs["attention_mask"], torch.ones(__lowercase,   __lowercase,   dtype=__lowercase  )],   dim=1  )

							return ordered_inputs




				@property
				def 							A__ (  self   :  Any  ):
							return 13









from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

			return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_					)					)]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

			lowercase__   					=			all_rotations(SCREAMING_SNAKE_CASE_					)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			lowercase__   					=			{
			    "bwt_string": "".join([word[-1] for word in rotations]					),
			    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_					),
			}
			return response



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter bwt_string type must be str."					)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."					)
			try:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."					)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."					)
			if idx_original_string >= len(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
							    F'we get original string \'{original_string}\''
							)




