




from __future__ import annotations







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





				"""simple docstring"""


				__lowerCamelCase		       =    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)
								__lowerCamelCase		       =    {
								    'a': 0.0_84_97,
								    'b': 0.0_14_92,
								    'c': 0.0_22_02,
								    'd': 0.0_42_53,
								    'e': 0.1_11_62,
								    'f': 0.0_22_28,
								    'g': 0.0_20_15,
								    'h': 0.0_60_94,
								    'i': 0.0_75_46,
								    'j': 0.0_01_53,
								    'k': 0.0_12_92,
								    'l': 0.0_40_25,
								    'm': 0.0_24_06,
								    'n': 0.0_67_49,
								    'o': 0.0_75_07,
								    'p': 0.0_19_29,
								    'q': 0.0_00_95,
								    'r': 0.0_75_87,
								    's': 0.0_63_27,
								    't': 0.0_93_56,
								    'u': 0.0_27_58,
								    'v': 0.0_09_78,
								    'w': 0.0_25_60,
								    'x': 0.0_01_50,
								    'y': 0.0_19_94,
								    'z': 0.0_00_77,
								}
				else:
								# Custom frequencies dictionary
								__lowerCamelCase		       =    frequencies_dict

				if not case_sensitive:
								__lowerCamelCase		       =    ciphertext.lower()

				# Chi squared statistic values
				__lowerCamelCase		       =    {}

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

								# decrypt the message with the shift
								for letter in ciphertext:
												try:
																# Try to index the letter in the alphabet
																__lowerCamelCase		       =    (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

								__lowerCamelCase		       =    0.0

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

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

																				# Complete the chi squared statistic formula
																				__lowerCamelCase		       =    ((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
																				__lowerCamelCase		       =    decrypted_with_shift.count(UpperCamelCase__      )

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

																				# Complete the chi squared statistic formula
																				__lowerCamelCase		       =    ((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
								__lowerCamelCase		       =    (
								    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]

				__lowerCamelCase		       =    min(
				    UpperCamelCase__ ,     key=UpperCamelCase__ ,     )

				# Get all the data from the most likely cipher (key, decoded message)
				(
				    (
				    __lowerCamelCase
				)       ,						(
				    __lowerCamelCase
				)       ,						
				)		       =    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,
				)








from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowercase__				:     Dict 		=						logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__			)
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     Optional[Any]       ,	**__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							super().__init__(**__lowercase						)
							requires_backends(self       ,	"vision"						)
							requires_backends(self       ,	"torch"						)

							if self.framework != "pt":
								raise ValueError(f"The {self.__class__} is only available in PyTorch."						)

							self.check_model_type(__lowercase						)






						def        snake_case__						(						self							:     Optional[int]       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     {}
							snake_case_		    =     {}
							snake_case_		    =     {}
							# preprocess args
							if "points_per_batch" in kwargs:
								snake_case_		    =     kwargs["points_per_batch"]
							if "points_per_crop" in kwargs:
								snake_case_		    =     kwargs["points_per_crop"]
							if "crops_n_layers" in kwargs:
								snake_case_		    =     kwargs["crops_n_layers"]
							if "crop_overlap_ratio" in kwargs:
								snake_case_		    =     kwargs["crop_overlap_ratio"]
							if "crop_n_points_downscale_factor" in kwargs:
								snake_case_		    =     kwargs["crop_n_points_downscale_factor"]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
								snake_case_		    =     kwargs["pred_iou_thresh"]
							if "stability_score_offset" in kwargs:
								snake_case_		    =     kwargs["stability_score_offset"]
							if "mask_threshold" in kwargs:
								snake_case_		    =     kwargs["mask_threshold"]
							if "stability_score_thresh" in kwargs:
								snake_case_		    =     kwargs["stability_score_thresh"]
							if "crops_nms_thresh" in kwargs:
								snake_case_		    =     kwargs["crops_nms_thresh"]
							if "output_rle_mask" in kwargs:
								snake_case_		    =     kwargs["output_rle_mask"]
							if "output_bboxes_mask" in kwargs:
								snake_case_		    =     kwargs["output_bboxes_mask"]
							return preprocess_kwargs, forward_params, postprocess_kwargs






						def __call__(						self							:     Optional[int]       ,	__lowercase							:     List[str]       ,	*__lowercase							:     Optional[Any]       ,	__lowercase							:     Dict=None       ,	__lowercase							:     List[str]=None       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							return super().__call__(__lowercase       ,	*__lowercase       ,	num_workers=__lowercase       ,	batch_size=__lowercase       ,	**__lowercase						)






						def        snake_case__						(						self							:     str       ,	__lowercase							:     int       ,	__lowercase							:     List[str]=64       ,	__lowercase							:     int = 0       ,	__lowercase							:     float = 5_12 / 15_00       ,	__lowercase							:     Optional[int] = 32       ,	__lowercase							:     Optional[int] = 1       ,	):






							"""simple docstring"""






							snake_case_		    =     load_image(__lowercase						)
							snake_case_		    =     self.image_processor.size["longest_edge"]
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.generate_crop_boxes(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)
							snake_case_		    =     self.image_processor(images=__lowercase       ,	return_tensors="pt"						)

							with self.device_placement():
								if self.framework == "pt":
									snake_case_		    =     self.get_inference_context()
									with inference_context():
										snake_case_		    =     self._ensure_tensor_on_device(__lowercase       ,	device=self.device						)
										snake_case_		    =     self.model.get_image_embeddings(model_inputs.pop("pixel_values"						)						)
										snake_case_		    =     image_embeddings

							snake_case_		    =     grid_points.shape[1]
							snake_case_		    =     points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
								raise ValueError(
								    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
								    "To return all points at once, set points_per_batch to None"						)

							for i in range(0       ,	__lowercase       ,	__lowercase						):
								snake_case_		    =     grid_points[:, i : i + points_per_batch, :, :]
								snake_case_		    =     input_labels[:, i : i + points_per_batch]
								snake_case_		    =     i == n_points - points_per_batch
								yield {
								    "input_points": batched_points,
								    "input_labels": labels,
								    "input_boxes": crop_boxes,
								    "is_last": is_last,
								    **model_inputs,
								}






						def        snake_case__						(						self							:     Tuple       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     Union[str, Any]=0.88       ,	__lowercase							:     Union[str, Any]=0.95       ,	__lowercase							:     int=0       ,	__lowercase							:     int=1       ,	):






							"""simple docstring"""






							snake_case_		    =     model_inputs.pop("input_boxes"						)
							snake_case_		    =     model_inputs.pop("is_last"						)
							snake_case_		    =     model_inputs.pop("original_sizes"						).tolist()
							snake_case_		    =     model_inputs.pop("reshaped_input_sizes"						).tolist()

							snake_case_		    =     self.model(**__lowercase						)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							snake_case_		    =     model_outputs["pred_masks"]
							snake_case_		    =     self.image_processor.post_process_masks(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	binarize=__lowercase						)
							snake_case_		    =     model_outputs["iou_scores"]
							snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.filter_masks(
							    masks[0]       ,	iou_scores[0]       ,	original_sizes[0]       ,	input_boxes[0]       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}






						def        snake_case__						(						self							:     str       ,	__lowercase							:     Any       ,	__lowercase							:     Optional[int]=False       ,	__lowercase							:     int=False       ,	__lowercase							:     List[str]=0.7       ,	):






							"""simple docstring"""






							snake_case_		    =     []
							snake_case_		    =     []
							snake_case_		    =     []
							for model_output in model_outputs:
								all_scores.append(model_output.pop("iou_scores"						)						)
								all_masks.extend(model_output.pop("masks"						)						)
								all_boxes.append(model_output.pop("boxes"						)						)

							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.post_process_for_mask_generation(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)

							snake_case_		    =     defaultdict(__lowercase						)
							for output in model_outputs:
								for k, v in output.items():
									extra[k].append(__lowercase						)

							snake_case_		    =     {}
							if output_rle_mask:
								snake_case_		    =     rle_mask

							if output_bboxes_mask:
								snake_case_		    =     bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
