


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

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           super().__init__(*lowerCamelCase,      **lowerCamelCase)
           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,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None)       ->   List[Any]:




           """simple docstring"""

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

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

           if generate_kwargs is not None:
               _lowercase       :							Optional[Any]      		=		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       :							Union[str, Any]      		=		max_new_tokens
           return preprocess_params, forward_kwargs, {}
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Any:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=None)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Any      		=		load_image(lowerCamelCase)

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

               _lowercase       :							int      		=		self.model.config.model_type

               if model_type == "git":
                   _lowercase       :							Optional[int]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
                   _lowercase       :							str      		=		self.tokenizer(text=lowerCamelCase,      add_special_tokens=lowerCamelCase).input_ids
                   _lowercase       :							Optional[int]      		=		[self.tokenizer.cls_token_id] + input_ids
                   _lowercase       :							int      		=		torch.tensor(lowerCamelCase).unsqueeze(0)
                   model_inputs.update({'input_ids': input_ids})

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

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

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

           else:
               _lowercase       :							int      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)

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

           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=None)       ->   Any:




           """simple docstring"""

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

           if generate_kwargs is None:
               _lowercase       :							int      		=		{}
           # 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       :							Optional[int]      		=		model_inputs.pop(self.model.main_input_name)
           _lowercase       :							Optional[Any]      		=		self.model.generate(lowerCamelCase,      **lowerCamelCase,      **lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

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






from __future__ import annotations

from math import ceil, floor, sqrt



def 				UpperCamelCase_(      lowerCamelCase_ = 200_0000 )    ->						int:
    _lowercase       :							list[int]      		=		[0]
    _lowercase       :							int

    for idx in range(1							, ceil(sqrt(target * 2 ) * 1.1 ) ):
        triangle_numbers.append(triangle_numbers[-1] + idx )

    # we want this to be as close as possible to target
    _lowercase       :							int      		=		0
    # the area corresponding to the grid that gives the product closest to target
    _lowercase       :							int      		=		0
    # an estimate of b, using the quadratic formula
    _lowercase       :							float
    # the largest integer less than b_estimate
    _lowercase       :							int
    # the largest integer less than b_estimate
    _lowercase       :							int
    # the triangle number corresponding to b_floor
    _lowercase       :							int
    # the triangle number corresponding to b_ceil
    _lowercase       :							int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:]							, 1 ):
        _lowercase       :							Optional[int]      		=		(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
        _lowercase       :							List[str]      		=		floor(lowerCamelCase_ )
        _lowercase       :							Dict      		=		ceil(lowerCamelCase_ )
        _lowercase       :							List[str]      		=		triangle_numbers[b_floor]
        _lowercase       :							List[str]      		=		triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a ) < abs(
            target - best_product ):
            _lowercase       :							Union[str, Any]      		=		triangle_b_first_guess * triangle_a
            _lowercase       :							Union[str, Any]      		=		idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a ) < abs(
            target - best_product ):
            _lowercase       :							Any      		=		triangle_b_second_guess * triangle_a
            _lowercase       :							Optional[Any]      		=		idx_a * b_ceil

    return area


if __name__ == "__main__":
 print(F"{solution() = }")


