






'''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

UpperCamelCase     :   Optional[Any]	      =		logging.get_logger(__name__)




@add_end_docstrings(a      )
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		def __init__(   self ,*_lowerCAmelCase ,**_lowerCAmelCase							):
								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							):
								lowerCamelCase__								=	{}
								lowerCamelCase__								=	{}

								if prompt is not None:
														lowerCamelCase__								=	prompt

								if generate_kwargs is not None:
														lowerCamelCase__								=	generate_kwargs
								if max_new_tokens is not None:
														if "generate_kwargs" not in forward_kwargs:
																				lowerCamelCase__								=	{}
														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"""							)
														lowerCamelCase__								=	max_new_tokens
								return preprocess_params, forward_kwargs, {}






		def __call__(   self ,_lowerCAmelCase ,**_lowerCAmelCase							):
								return super().__call__(_lowerCAmelCase ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase=None							):
								lowerCamelCase__								=	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."""							)

														lowerCamelCase__								=	self.model.config.model_type

														if model_type == "git":
																				lowerCamelCase__								=	self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework							)
																				lowerCamelCase__								=	self.tokenizer(text=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							).input_ids
																				lowerCamelCase__								=	[self.tokenizer.cls_token_id] + input_ids
																				lowerCamelCase__								=	torch.tensor(_lowerCAmelCase							).unsqueeze(0							)
																				model_inputs.update({"""input_ids""": input_ids}							)

														elif model_type == "pix2struct":
																				lowerCamelCase__								=	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
																				lowerCamelCase__								=	self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework							)
																				lowerCamelCase__								=	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:
														lowerCamelCase__								=	self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework							)

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

								return model_inputs






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase=None							):
								# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
								# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
								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"""]							)
								):
														lowerCamelCase__								=	None

								if generate_kwargs is None:
														lowerCamelCase__								=	{}
								# 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.
								lowerCamelCase__								=	model_inputs.pop(self.model.main_input_name							)
								lowerCamelCase__								=	self.model.generate(_lowerCAmelCase ,**_lowerCAmelCase ,**_lowerCAmelCase							)
								return model_outputs






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	[]
								for output_ids in model_outputs:
														lowerCamelCase__								=	{
														    """generated_text""": self.tokenizer.decode(
														        _lowerCAmelCase ,skip_special_tokens=_lowerCAmelCase ,)
														}
														records.append(_lowerCAmelCase							)
								return records









from __future__ import annotations

from typing import Any







class 		_A						:

  def __init__(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	float = 0			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					row, column
     __UpperCamelCase	:			Tuple					=					[[default_value for c in range(lowerCamelCase__			)] for r in range(lowerCamelCase__			)]

  def __str__(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					f'Matrix consist of {self.row} rows and {self.column} columns\n'

     # Make string identifier
     __UpperCamelCase	:			Optional[int]					=					0
     for row_vector in self.array:
        for obj in row_vector:
           __UpperCamelCase	:			int					=					max(lowerCamelCase__   ,				len(str(lowerCamelCase__			)			)			)
     __UpperCamelCase	:			Union[str, Any]					=					f'%{max_element_length}s'

     # Make string and return
     def single_line(lowerCamelCase__						:	list[float]			) -> str:
        nonlocal string_format_identifier
        __UpperCamelCase	:			List[Any]					=					"""["""
        line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
        line += "]"
        return line

     s += "\n".join(single_line(lowerCamelCase__			) for row_vector in self.array			)
     return s

  def __repr__(    self						:	Any			):
     """simple docstring"""

     return str(self			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     if not (isinstance(lowerCamelCase__   ,				(list, tuple)			) and len(lowerCamelCase__			) == 2):
        return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
        return False
     else:
        return True

  def __getitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     return self.array[loc[0]][loc[1]]

  def __setitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]   ,				lowerCamelCase__						:	float			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					value

  def __add__(    self						:	int   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == another.row and self.column == another.column

     # Add
     __UpperCamelCase	:			Tuple					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Optional[int]					=					self[r, c] + another[r, c]
     return result

  def __neg__(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Any					=					-self[r, c]
     return result

  def __sub__(    self						:	Tuple   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     return self + (-another)

  def __mul__(    self						:	Tuple   ,				lowerCamelCase__						:	int | float | Matrix			):
     """simple docstring"""

     if isinstance(lowerCamelCase__   ,				(int, float)			):  # Scalar multiplication
        __UpperCamelCase	:			List[str]					=					Matrix(self.row   ,				self.column			)
        for r in range(self.row			):
           for c in range(self.column			):
              __UpperCamelCase	:			Tuple					=					self[r, c] * another
        return result
     elif isinstance(lowerCamelCase__   ,				lowerCamelCase__			):  # Matrix multiplication
        assert self.column == another.row
        __UpperCamelCase	:			List[Any]					=					Matrix(self.row   ,				another.column			)
        for r in range(self.row			):
           for c in range(another.column			):
              for i in range(self.column			):
                 result[r, c] += self[r, i] * another[i, c]
        return result
     else:
        __UpperCamelCase	:			Any					=					f'Unsupported type given for another ({type(lowerCamelCase__			)})'
        raise TypeError(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					Matrix(self.column   ,				self.row			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			str					=					self[r, c]
     return result

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Matrix   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     __UpperCamelCase	:			Optional[int]					=					v.transpose()
     __UpperCamelCase	:			Tuple					=					(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
        return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


       def 				__lowerCamelCase     (							)     ->  None:
          # a^(-1)
          __UpperCamelCase	:			str					=					Matrix(3					,							3					,							0							)
          for i in range(3							):
             __UpperCamelCase	:			List[str]					=					1
          print(f'a^(-1) is {ainv}'							)
          # u, v
          __UpperCamelCase	:			Any					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					1, 2, -3
          __UpperCamelCase	:			int					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					4, -2, 5
          print(f'u is {u}'							)
          print(f'v is {v}'							)
          print(f'uv^T is {u * v.transpose()}'							)
          # Sherman Morrison
          print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase					,							__lowerCAmelCase							)}'							)


       def 				__lowerCamelCase     (							)     ->  None:
          import doctest

          doctest.testmod()



       testa()



