


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			: int				   =					logging.get_logger(__name__)






@add_end_docstrings(_a  )
class _lowerCamelCase(		_a  ):
       def __init__(   self, *lowerCamelCase, **lowerCamelCase)			->  Optional[int]:




           """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)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						{}
           _lowercase			:				Union[str, Any] 						=						{}

           if prompt is not None:
               _lowercase			:				Optional[int] 						=						prompt

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




           """simple docstring"""

           return super().__call__(lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Union[str, 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			:				Optional[int] 						=						self.model.config.model_type

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

               elif model_type == "pix2struct":
                   _lowercase			:				Optional[int] 						=						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			:				Tuple 						=						self.image_processor(images=lowerCamelCase, return_tensors=self.framework)
                   _lowercase			:				List[str] 						=						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			:				str 						=						self.image_processor(images=lowerCamelCase, return_tensors=self.framework)

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

           return model_inputs
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Optional[int]:




           """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			:				Optional[Any] 						=						None

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




       def      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

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





from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()