


'''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_ :     List[Any]								=      logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase				)
class 						_lowerCamelCase					(    __lowerCamelCase				):




 '''simple docstring'''


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

  """simple docstring"""







  super().__init__(*UpperCamelCase_      ,      **UpperCamelCase_						)
  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  snake_case__   (      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		int       =			{}
  __A  :		str       =			{}

  if prompt is not None:
   __A  :		Union[str, Any]       =			prompt

  if generate_kwargs is not None:
   __A  :		int       =			generate_kwargs
  if max_new_tokens is not None:
   if "generate_kwargs" not in forward_kwargs:
    __A  :		Dict       =			{}
   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'						)
   __A  :		str       =			max_new_tokens
  return preprocess_params, forward_kwargs, {}


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

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		List[str]       =			load_image(UpperCamelCase_						)

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

   __A  :		Dict       =			self.model.config.model_type

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

   elif model_type == "pix2struct":
    __A  :		Dict       =			self.image_processor(images=UpperCamelCase_      ,      header_text=UpperCamelCase_      ,      return_tensors=self.framework						)

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

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

  else:
   __A  :		Optional[Any]       =			self.image_processor(images=UpperCamelCase_      ,      return_tensors=self.framework						)

  if self.model.config.model_type == "git" and prompt is None:
   __A  :		Union[str, Any]       =			None

  return model_inputs


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







  if (
      "input_ids" in model_inputs
      and isinstance(model_inputs['input_ids']      ,      UpperCamelCase_						)
      and all(x is None for x in model_inputs['input_ids']						)
  ):
   __A  :		Tuple       =			None

  if generate_kwargs is None:
   __A  :		Optional[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.
  __A  :		Any       =			model_inputs.pop(self.model.main_input_name						)
  __A  :		List[Any]       =			self.model.generate(UpperCamelCase_      ,      **UpperCamelCase_      ,      **UpperCamelCase_						)
  return model_outputs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







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










'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = None						):

  """simple docstring"""







  if components is None:
   __A  :		Optional[int]       =			[]
  __A  :		Dict       =			list(__lowercase						)


 def __len__(      self						):

  """simple docstring"""







  return len(self.__components						)


 def __str__(      self						):

  """simple docstring"""







  return "(" + ",".join(map(__lowercase      ,      self.__components						)						) + ")"


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			len(self						)
  if size == len(__lowercase						):
   __A  :		Dict       =			[self.__components[i] + other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:
   raise Exception('must have the same size'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			len(self						)
  if size == len(__lowercase						):
   __A  :		str       =			[self.__components[i] - other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:  # error case
   raise Exception('must have the same size'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      (float, int)						):
   __A  :		Dict       =			[c * other for c in self.__components]
   return Vector(__lowercase						)
  elif isinstance(__lowercase      ,      __lowercase						) and len(self						) == len(__lowercase						):
   __A  :		Optional[Any]       =			len(self						)
   __A  :		Optional[int]       =			[self.__components[i] * other.component(__lowercase						) for i in range(__lowercase						)]
   return sum(__lowercase						)
  else:  # error case
   raise Exception('invalid operand!'						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return Vector(self.__components						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						) and -len(self.__components						) <= i < len(self.__components						):
   return self.__components[i]
  else:
   raise Exception('index out of range'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  assert -len(self.__components						) <= pos < len(self.__components						)
  __A  :		str       =			value


 def  snake_case__   (      self						):

  """simple docstring"""







  if len(self.__components						) == 0:
   raise Exception('Vector is empty'						)
  __A  :		List[Any]       =			[c**2 for c in self.__components]
  return math.sqrt(sum(__lowercase						)						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		List[Any]       =			self * other
  __A  :		Any       =			self.euclidean_length() * other.euclidean_length()
  if deg:
   return math.degrees(math.acos(num / den						)						)
  else:
   return math.acos(num / den						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			)
 return Vector([0] * dimension			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			) and (isinstance(UpperCamelCase__,				UpperCamelCase__			))
 __A  :		List[str]       =			[0] * dimension
 __A  :		Tuple       =			1
 return Vector(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					Vector,				UpperCamelCase__       :					Vector			):
 assert (
     isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and (isinstance(UpperCamelCase__,				(int, float)			))
 )
 return x * scalar + y


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		Any       =			[random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)]
 return Vector(UpperCamelCase__			)

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			matrix
  __A  :		str       =			w
  __A  :		Dict       =			h


 def __str__(      self						):

  """simple docstring"""







  __A  :		Tuple       =			''
  for i in range(self.__height						):
   ans += "|"
   for j in range(self.__width						):
    if j < self.__width - 1:
     ans += str(self.__matrix[i][j]						) + ","
    else:
     ans += str(self.__matrix[i][j]						) + "|\n"
  return ans


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Union[str, Any]       =			[]
   for i in range(self.__height						):
    __A  :		List[Any]       =			[
        self.__matrix[i][j] + other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrix must have the same dimension!'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Dict       =			[]
   for i in range(self.__height						):
    __A  :		str       =			[
        self.__matrix[i][j] - other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrices must have the same dimension!'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						):  # matrix-vector
   if len(__lowercase						) == self.__width:
    __A  :		Tuple       =			zero_vector(self.__height						)
    for i in range(self.__height						):
     __A  :		Any       =			[
         self.__matrix[i][j] * other.component(__lowercase						)
         for j in range(self.__width						)
     ]
     ans.change_component(__lowercase      ,      sum(__lowercase						)						)
    return ans
   else:
    raise Exception(
        'vector must have the same size as the '
        'number of columns of the matrix!'						)
  elif isinstance(__lowercase      ,      (int, float)						):  # matrix-scalar
   __A  :		Optional[Any]       =			[
       [self.__matrix[i][j] * other for j in range(self.__width						)]
       for i in range(self.__height						)
   ]
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  return None


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__height


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__width


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   return self.__matrix[x][y]
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   __A  :		Dict       =			value
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  __A  :		Union[str, Any]       =			self.__matrix[:x] + self.__matrix[x + 1 :]
  for i in range(len(__lowercase						)						):
   __A  :		Dict       =			minor[i][:y] + minor[i][y + 1 :]
  return Matrix(__lowercase      ,      self.__width - 1      ,      self.__height - 1						).determinant()


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if 0 <= x < self.__height and 0 <= y < self.__width:
   return (-1) ** (x + y) * self.minor(__lowercase      ,      __lowercase						)
  else:
   raise Exception('Indices out of bounds'						)







 def  snake_case__   (      self						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if self.__height < 1:
   raise Exception('Matrix has no element'						)
  elif self.__height == 1:
   return self.__matrix[0][0]
  elif self.__height == 2:
   return (
       self.__matrix[0][0] * self.__matrix[1][1]
       - self.__matrix[0][1] * self.__matrix[1][0]
   )
  else:
   __A  :		List[str]       =			[
       self.__matrix[0][y] * self.cofactor(0      ,      __lowercase						) for y in range(self.__width						)
   ]
   return sum(__lowercase						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		list[list[float]]       =			[[0] * n for _ in range(UpperCamelCase__			)]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		list[list[float]]       =			[
     [random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)] for _ in range(UpperCamelCase__			)
 ]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)






