



'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_A				:    List[Any]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  *UpperCamelCase_				:	Any     ,  **UpperCamelCase_				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(*UpperCamelCase_     ,  **UpperCamelCase_					)
						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	str=None					)  ->       int:



						'''simple docstring'''





						_lowercase					:       List[str]       =		{}
						_lowercase					:       Any       =		{}

						if prompt is not None:
							_lowercase					:       List[str]       =		prompt

						if generate_kwargs is not None:
							_lowercase					:       Tuple       =		generate_kwargs
						if max_new_tokens is not None:
							if "generate_kwargs" not in forward_kwargs:
								_lowercase					:       str       =		{}
							if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
								raise ValueError(
								    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
								    ' please use only one'					)
							_lowercase					:       Optional[Any]       =		max_new_tokens
						return preprocess_params, forward_kwargs, {}




					def __call__(  self				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, List[str], "Image.Image", List["Image.Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       str:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=None					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)

						if prompt is not None:
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								raise ValueError(
								    F'''Received an invalid text input, got - {type(UpperCamelCase_					)} - but expected a single string. '''
								    'Note also that one single text can be provided for conditional image to text generation.'					)

							_lowercase					:       Tuple       =		self.model.config.model_type

							if model_type == "git":
								_lowercase					:       Tuple       =		self.image_processor(images=UpperCamelCase_     ,  return_tensors=self.framework					)
								_lowercase					:       List[Any]       =		self.tokenizer(text=UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					).input_ids
								_lowercase					:       Dict       =		[self.tokenizer.cls_token_id] + input_ids
								_lowercase					:       Any       =		torch.tensor(UpperCamelCase_					).unsqueeze(0					)
								model_inputs.update({'input_ids': input_ids}					)

							elif model_type == "pix2struct":
								_lowercase					:       List[str]       =		self.image_processor(images=UpperCamelCase_     ,  header_text=UpperCamelCase_     ,  return_tensors=self.framework					)

							elif model_type != "vision-encoder-decoder":
								# vision-encoder-decoder does not support conditional generation
								_lowercase					:       Optional[Any]       =		self.image_processor(images=UpperCamelCase_     ,  return_tensors=self.framework					)
								_lowercase					:       str       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework					)
								model_inputs.update(UpperCamelCase_					)

							else:
								raise ValueError(F'''Model type {model_type} does not support conditional text generation'''					)

						else:
							_lowercase					:       Any       =		self.image_processor(images=UpperCamelCase_     ,  return_tensors=self.framework					)

						if self.model.config.model_type == "git" and prompt is None:
							_lowercase					:       Any       =		None

						return model_inputs




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Optional[int]=None					)  ->       str:



						'''simple docstring'''





						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs['input_ids']     ,  UpperCamelCase_					)
						    and all(x is None for x in model_inputs['input_ids']					)
						):
							_lowercase					:       List[Any]       =		None

						if generate_kwargs is None:
							_lowercase					:       str       =		{}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						_lowercase					:       Any       =		model_inputs.pop(self.model.main_input_name					)
						_lowercase					:       Tuple       =		self.model.generate(UpperCamelCase_     ,  **UpperCamelCase_     ,  **UpperCamelCase_					)
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		[]
						for output_ids in model_outputs:
							_lowercase					:       List[Any]       =		{
							    'generated_text': self.tokenizer.decode(
							        UpperCamelCase_     ,  skip_special_tokens=UpperCamelCase_     ,  )
							}
							records.append(UpperCamelCase_					)
						return records





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, Any]       =		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:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

    # print entropy
	print(f'''{round(-1 * my_fir_sum					):.1f}'''					)

	# two len string
	_lowercase					:       str       =		sum(two_char_strings.values()					)
	_lowercase					:       str       =		0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			_lowercase					:       Optional[Any]       =		cha + cha
			if sequence in two_char_strings:
				_lowercase					:       int       =		two_char_strings[sequence]
				_lowercase					:       Optional[int]       =		int(_lowercase					) / all_sum
				my_sec_sum += prob * math.loga(_lowercase					)

    # 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				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		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(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	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()
