




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=["""image_processor""", """tokenizer"""]
				__a							: List[str]										="""OwlViTImageProcessor"""
				__a							: List[Any]										=("""CLIPTokenizer""", """CLIPTokenizerFast""")



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
											lowerCAmelCase   =		None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																		    ''' instead.'''       , UpperCAmelCase_       , )
																		lowerCAmelCase   =		kwargs.pop('''feature_extractor'''						)

											lowerCAmelCase   =		image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('''You need to specify an `image_processor`.'''						)
											if tokenizer is None:
																		raise ValueError('''You need to specify a `tokenizer`.'''						)

											super().__init__(UpperCAmelCase_       , UpperCAmelCase_						)



				def __call__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_="max_length"       , UpperCAmelCase_="np"       , **UpperCAmelCase_						):

											if text is None and query_images is None and images is None:
																		raise ValueError(
																		    '''You have to specify at least one text or query image or image. All three cannot be none.'''						)

											if text is not None:
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						) or (isinstance(UpperCAmelCase_       , UpperCAmelCase_						) and not isinstance(text[0]       , UpperCAmelCase_						)):
																									lowerCAmelCase   =		[self.tokenizer(UpperCAmelCase_       , padding=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)]

																		elif isinstance(UpperCAmelCase_       , UpperCAmelCase_						) and isinstance(text[0]       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[]

																									# Maximum number of queries across batch
																									lowerCAmelCase   =		max([len(UpperCAmelCase_						) for t in text]						)

																									# Pad all batch samples to max number of text queries
																									for t in text:
																																if len(UpperCAmelCase_						) != max_num_queries:
																																							lowerCAmelCase   =		t + [''' '''] * (max_num_queries - len(UpperCAmelCase_						))

																																lowerCAmelCase   =		self.tokenizer(UpperCAmelCase_       , padding=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)
																																encodings.append(UpperCAmelCase_						)
																		else:
																									raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''						)

																		if return_tensors == "np":
																									lowerCAmelCase   =		np.concatenate([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		np.concatenate([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		elif return_tensors == "jax" and is_flax_available():
																									import jax.numpy as jnp

																									lowerCAmelCase   =		jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		elif return_tensors == "pt" and is_torch_available():
																									import torch

																									lowerCAmelCase   =		torch.cat([encoding['''input_ids'''] for encoding in encodings]       , dim=0						)
																									lowerCAmelCase   =		torch.cat([encoding['''attention_mask'''] for encoding in encodings]       , dim=0						)

																		elif return_tensors == "tf" and is_tf_available():
																									import tensorflow as tf

																									lowerCAmelCase   =		tf.stack([encoding['''input_ids'''] for encoding in encodings]       , axis=0						)
																									lowerCAmelCase   =		tf.stack([encoding['''attention_mask'''] for encoding in encodings]       , axis=0						)

																		else:
																									raise ValueError('''Target return tensor type could not be returned'''						)

																		lowerCAmelCase   =		BatchEncoding()
																		lowerCAmelCase   =		input_ids
																		lowerCAmelCase   =		attention_mask

											if query_images is not None:
																		lowerCAmelCase   =		BatchEncoding()
																		lowerCAmelCase   =		self.image_processor(
																		    UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						).pixel_values
																		lowerCAmelCase   =		query_pixel_values

											if images is not None:
																		lowerCAmelCase   =		self.image_processor(UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_						)

											if text is not None and images is not None:
																		lowerCAmelCase   =		image_features.pixel_values
																		return encoding
											elif query_images is not None and images is not None:
																		lowerCAmelCase   =		image_features.pixel_values
																		return encoding
											elif text is not None or query_images is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**UpperCAmelCase_						)       , tensor_type=UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process_object_detection(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.batch_decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor_class






				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							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(_snake_case							)  # 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(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # 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       UpperCAmelCase       (     _snake_case							):
							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(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							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()



