


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 __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0)			->  None:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						row, column
           _lowercase			:				Any 						=						[[default_value for c in range(lowerCamelCase)] for r in range(lowerCamelCase)]
       def __str__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

           # Make string identifier
           _lowercase			:				str 						=						0
           for row_vector in self.array:
               for obj in row_vector:
                   _lowercase			:				Optional[int] 						=						max(lowerCamelCase, len(str(lowerCamelCase)))
           _lowercase			:				List[str] 						=						F'''%{max_element_length}s'''

           # Make string and return
           def single_line(lowerCamelCase) -> str:
               nonlocal string_format_identifier
               _lowercase			:				Union[str, 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)			->  str:




           """simple docstring"""

           return str(self)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  bool:




           """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, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           return self.array[loc[0]][loc[1]]
       def __setitem__(   self, lowerCamelCase, lowerCamelCase)			->  None:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						value
       def __add__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

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

           # Add
           _lowercase			:				Any 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				int 						=						self[r, c] + another[r, c]
           return result
       def __neg__(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				List[str] 						=						-self[r, c]
           return result
       def __sub__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           return self + (-another)
       def __mul__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           if isinstance(lowerCamelCase, (int, float)):  # Scalar multiplication
               _lowercase			:				Dict 						=						Matrix(self.row, self.column)
               for r in range(self.row):
                   for c in range(self.column):
                       _lowercase			:				Union[str, Any] 						=						self[r, c] * another
               return result
           elif isinstance(lowerCamelCase, lowerCamelCase):  # Matrix multiplication
               assert self.column == another.row
               _lowercase			:				str 						=						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:
               _lowercase			:				Tuple 						=						F'''Unsupported type given for another ({type(lowerCamelCase)})'''
               raise TypeError(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Matrix:




           """simple docstring"""

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """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
           _lowercase			:				Dict 						=						v.transpose()
           _lowercase			:				Any 						=						(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 							UpperCamelCase_( )       ->		None:
     # a^(-1)
     _lowercase			:				Optional[int] 						=						Matrix(3				,					3				,					0					)
     for i in range(3					):
         _lowercase			:				int 						=						1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     _lowercase			:				Dict 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						1, 2, -3
     _lowercase			:				List[Any] 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				int 						=						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 							UpperCamelCase_( )       ->		None:
     import doctest

     doctest.testmod()

 testa()


