



"""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

__lowerCAmelCase							:       List[str]          =       logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__		)
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     *_lowercase				,     **_lowercase )			->    Optional[Any]:







								'''simple docstring'''

								super().__init__(*_lowercase				,     **_lowercase )
								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				,     _lowercase=None				,     _lowercase=None				,     _lowercase=None )			->    int:







								'''simple docstring'''

								snake_case_ :			Any            =      {}
								snake_case_ :			Optional[int]            =      {}

								if prompt is not None:
														snake_case_ :			Tuple            =      prompt

								if generate_kwargs is not None:
														snake_case_ :			List[str]            =      generate_kwargs
								if max_new_tokens is not None:
														if "generate_kwargs" not in forward_kwargs:
																				snake_case_ :			Tuple            =      {}
														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""" )
														snake_case_ :			Optional[int]            =      max_new_tokens
								return preprocess_params, forward_kwargs, {}





		def __call__(       self				,     _lowercase				,     **_lowercase )			->    str:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=None )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			Any            =      load_image(_lowercase )

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

														snake_case_ :			Dict            =      self.model.config.model_type

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

														elif model_type == "pix2struct":
																				snake_case_ :			int            =      self.image_processor(images=_lowercase				,     header_text=_lowercase				,     return_tensors=self.framework )

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

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

								else:
														snake_case_ :			List[str]            =      self.image_processor(images=_lowercase				,     return_tensors=self.framework )

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

								return model_inputs





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=None )			->    List[str]:







								'''simple docstring'''

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

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





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			str            =      []
								for output_ids in model_outputs:
														snake_case_ :			Dict            =      {
														    """generated_text""": self.tokenizer.decode(
														        _lowercase				,     skip_special_tokens=_lowercase				,     )
														}
														records.append(_lowercase )
								return records



import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
