



from __future__ import annotations





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       list[str] | None = None							,       _UpperCAmelCase    :       dict[str, float] | None = None							,       _UpperCAmelCase    :       bool = False							,       )    ->			tuple[int, float, str]:







			'''simple docstring'''
			_UpperCAmelCase			=       cipher_alphabet or [chr(_UpperCAmelCase   ) for i in range(97							,       123   )]

			# If the argument is None or the user provided an empty dictionary
			if not frequencies_dict:
						# Frequencies of letters in the english language (how much they show up)
						_UpperCAmelCase			=       {
						    'a': 0.08497,
						    'b': 0.01492,
						    'c': 0.02202,
						    'd': 0.04253,
						    'e': 0.11162,
						    'f': 0.02228,
						    'g': 0.02015,
						    'h': 0.06094,
						    'i': 0.07546,
						    'j': 0.00153,
						    'k': 0.01292,
						    'l': 0.04025,
						    'm': 0.02406,
						    'n': 0.06749,
						    'o': 0.07507,
						    'p': 0.01929,
						    'q': 0.00095,
						    'r': 0.07587,
						    's': 0.06327,
						    't': 0.09356,
						    'u': 0.02758,
						    'v': 0.00978,
						    'w': 0.02560,
						    'x': 0.00150,
						    'y': 0.01994,
						    'z': 0.00077,
						}
			else:
						# Custom frequencies dictionary
						_UpperCAmelCase			=       frequencies_dict

			if not case_sensitive:
						_UpperCAmelCase			=       ciphertext.lower()

			# Chi squared statistic values
			_UpperCAmelCase			=       {}

			# cycle through all of the shifts
			for shift in range(len(_UpperCAmelCase   )   ):
						_UpperCAmelCase			=       ''

						# decrypt the message with the shift
						for letter in ciphertext:
									try:
												# Try to index the letter in the alphabet
												_UpperCAmelCase			=       (alphabet_letters.index(letter.lower()   ) - shift) % len(
												    _UpperCAmelCase   )
												decrypted_with_shift += (
												    alphabet_letters[new_key].upper()
												    if case_sensitive and letter.isupper()
												    else alphabet_letters[new_key]
												)
									except ValueError:
												# Append the character if it isn't in the alphabet
												decrypted_with_shift += letter

						_UpperCAmelCase			=       0.0

						# Loop through each letter in the decoded message with the shift
						for letter in decrypted_with_shift:
									if case_sensitive:
												_UpperCAmelCase			=       letter.lower()
												if letter in frequencies:
															# Get the amount of times the letter occurs in the message
															_UpperCAmelCase			=       decrypted_with_shift.lower().count(_UpperCAmelCase   )

															# Get the excepcted amount of times the letter should appear based
															# on letter frequencies
															_UpperCAmelCase			=       frequencies[letter] * occurrences

															# Complete the chi squared statistic formula
															_UpperCAmelCase			=       ((occurrences - expected) ** 2) / expected

															# Add the margin of error to the total chi squared statistic
															chi_squared_statistic += chi_letter_value
									else:
												if letter.lower() in frequencies:
															# Get the amount of times the letter occurs in the message
															_UpperCAmelCase			=       decrypted_with_shift.count(_UpperCAmelCase   )

															# Get the excepcted amount of times the letter should appear based
															# on letter frequencies
															_UpperCAmelCase			=       frequencies[letter] * occurrences

															# Complete the chi squared statistic formula
															_UpperCAmelCase			=       ((occurrences - expected) ** 2) / expected

															# Add the margin of error to the total chi squared statistic
															chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
						_UpperCAmelCase			=       (
						    chi_squared_statistic,
						    decrypted_with_shift,
						)

			# Get the most likely cipher by finding the cipher with the smallest chi squared
			# statistic
			def chi_squared_statistic_values_sorting_key(_UpperCAmelCase    :       int   ) -> tuple[float, str]:
						return chi_squared_statistic_values[key]

			_UpperCAmelCase			=       min(
			    _UpperCAmelCase							,       key=_UpperCAmelCase							,       )

			# Get all the data from the most likely cipher (key, decoded message)
			(
			    (
			    _UpperCAmelCase					
			)	,					(
			    _UpperCAmelCase					
			)	,										
			)			=       chi_squared_statistic_values[most_likely_cipher]

			# Return the data on the most likely shift
			return (
			    most_likely_cipher,
			    most_likely_cipher_chi_squared_value,
			    decoded_most_likely_cipher,
			)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCAmelCase__       =	r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''rag'''
			UpperCamelCase        =					True



			def __init__(						self       :  str			,		A       :  int=None			,		A       :  Dict=True			,		A       :  int=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  int=None			,		A       :  str=" / "			,		A       :  int=" // "			,		A       :  Any=5			,		A       :  Optional[Any]=3_00			,		A       :  Optional[Any]=7_68			,		A       :  Optional[int]=8			,		A       :  Union[str, Any]="wiki_dpr"			,		A       :  int="train"			,		A       :  Tuple="compressed"			,		A       :  str=None			,		A       :  Any=None			,		A       :  Tuple=False			,		A       :  List[str]=False			,		A       :  Tuple=0.0			,		A       :  Any=True			,		A       :  int=False			,		A       :  Dict=False			,		A       :  List[str]=False			,		A       :  Optional[Any]=True			,		A       :  Tuple=None			,		**A       :  Optional[Any]			,		)       ->   Optional[Any]:



						"""simple docstring"""






						super().__init__(
						    bos_token_id=A			,		pad_token_id=A			,		eos_token_id=A			,		decoder_start_token_id=A			,		forced_eos_token_id=A			,		is_encoder_decoder=A			,		prefix=A			,		vocab_size=A			,		**A			,		)
						assert (
						    "question_encoder" in kwargs and "generator" in kwargs
						), "Config has to be initialized with question_encoder and generator config"
						_UpperCAmelCase			=       kwargs.pop('question_encoder')
						_UpperCAmelCase			=       question_encoder_config.pop('model_type')
						_UpperCAmelCase			=       kwargs.pop('generator')
						_UpperCAmelCase			=       decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)
						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)

						_UpperCAmelCase			=       reduce_loss
						_UpperCAmelCase			=       label_smoothing
						_UpperCAmelCase			=       exclude_bos_score
						_UpperCAmelCase			=       do_marginalize

						_UpperCAmelCase			=       title_sep
						_UpperCAmelCase			=       doc_sep
						_UpperCAmelCase			=       n_docs
						_UpperCAmelCase			=       max_combined_length

						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       dataset_split
						_UpperCAmelCase			=       index_name

						_UpperCAmelCase			=       retrieval_vector_size
						_UpperCAmelCase			=       retrieval_batch_size
						_UpperCAmelCase			=       passages_path
						_UpperCAmelCase			=       index_path
						_UpperCAmelCase			=       use_dummy_dataset

						_UpperCAmelCase			=       output_retrieved

						_UpperCAmelCase			=       do_deduplication

						_UpperCAmelCase			=       use_cache

						if self.forced_eos_token_id is None:
									_UpperCAmelCase			=       getattr(self.generator			,		'forced_eos_token_id'			,		A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  PretrainedConfig			,		A       :  PretrainedConfig			,		**A       :  Optional[Any])       ->   PretrainedConfig:



						"""simple docstring"""






						return cls(question_encoder=question_encoder_config.to_dict()			,		generator=generator_config.to_dict()			,		**A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       copy.deepcopy(self.__dict__)
						_UpperCAmelCase			=       self.question_encoder.to_dict()
						_UpperCAmelCase			=       self.generator.to_dict()
						_UpperCAmelCase			=       self.__class__.model_type
						return output
