


'''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

__snake_case											=					logging.get_logger(__name__)
@add_end_docstrings(__lowercase )
class        lowercase     (							__lowercase ):

		"""simple docstring"""

		def __init__(		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(*snake_case_   ,       **snake_case_							)
							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    lowerCAmelCase__    (		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			{}
							UpperCamelCase__       :Union[str, Any]							 =			{}

							if prompt is not None:
												UpperCamelCase__       :List[Any]							 =			prompt

							if generate_kwargs is not None:
												UpperCamelCase__       :Optional[int]							 =			generate_kwargs
							if max_new_tokens is not None:
												if "generate_kwargs" not in forward_kwargs:
																	UpperCamelCase__       :Union[str, Any]							 =			{}
												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'''							)
												UpperCamelCase__       :List[str]							 =			max_new_tokens
							return preprocess_params, forward_kwargs, {}




		def __call__(		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			load_image(snake_case_							)

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

												UpperCamelCase__       :Optional[int]							 =			self.model.config.model_type

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

												elif model_type == "pix2struct":
																	UpperCamelCase__       :Optional[int]							 =			self.image_processor(images=snake_case_   ,       header_text=snake_case_   ,       return_tensors=self.framework							)

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

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

							else:
												UpperCamelCase__       :Dict							 =			self.image_processor(images=snake_case_   ,       return_tensors=self.framework							)

							if self.model.config.model_type == "git" and prompt is None:
												UpperCamelCase__       :List[str]							 =			None

							return model_inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs['''input_ids''']   ,       snake_case_							)
							    and all(x is None for x in model_inputs['''input_ids''']							)
							):
												UpperCamelCase__       :str							 =			None

							if generate_kwargs is None:
												UpperCamelCase__       :List[Any]							 =			{}
							# 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.
							UpperCamelCase__       :str							 =			model_inputs.pop(self.model.main_input_name							)
							UpperCamelCase__       :Dict							 =			self.model.generate(snake_case_   ,       **snake_case_   ,       **snake_case_							)
							return model_outputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


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



'''simple docstring'''
from collections import Counter
from timeit import timeit


def       a				(      __a = ""			, )  ->     bool:



					'''simple docstring'''






					return sum(c % 2 for c in Counter(input_str.replace(''' '''			, ''''''       ).lower()       ).values()       ) < 2


def       a				(      __a = ""       )  ->     bool:



					'''simple docstring'''






					if len(__a       ) == 0:
										return True
					UpperCamelCase__       :List[Any]							 =			input_str.replace(''' '''			, ''''''       ).lower()
					# character_freq_dict: Stores the frequency of every character in the input string
					UpperCamelCase__       :dict[str, int]							 =			{}

					for character in lower_case_input_str:
										UpperCamelCase__       :Optional[int]							 =			character_freq_dict.get(__a			, 0       ) + 1
					UpperCamelCase__       :List[str]							 =			0

					for character_count in character_freq_dict.values():
										if character_count % 2:
															odd_char += 1
					if odd_char > 1:
										return False
					return True


def       a				(      __a = ""       )  ->     None:



					'''simple docstring'''






					print('''\nFor string = '''			, __a			, ''':'''       )
					print(
					    '''> can_string_be_rearranged_as_palindrome_counter()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome_counter(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )
					print(
					    '''> can_string_be_rearranged_as_palindrome()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )


if __name__ == "__main__":
		__snake_case											=					input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__snake_case											=					can_string_be_rearranged_as_palindrome_counter(check_str)
		print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")