



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

A__  :  Any								=					logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				*A_     :       Dict				,				**A_     :       Optional[Any]):
									super().__init__(*A_				,				**A_)
									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     :       int				,				A_     :       Any=None				,				A_     :       Dict=None				,				A_     :       List[Any]=None):
									lowerCAmelCase_			:     int									= {}
									lowerCAmelCase_			:     Dict									= {}

									if prompt is not None:
											lowerCAmelCase_			:     Any									= prompt

									if generate_kwargs is not None:
											lowerCAmelCase_			:     Union[str, Any]									= generate_kwargs
									if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
													lowerCAmelCase_			:     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''')
											lowerCAmelCase_			:     int									= max_new_tokens
									return preprocess_params, forward_kwargs, {}




							def __call__(       self     :       str				,				A_     :       Union[str, List[str], "Image.Image", List["Image.Image"]]				,				**A_     :       Union[str, Any]):
									return super().__call__(A_				,				**A_)




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Any				,				A_     :       str=None):
									lowerCAmelCase_			:     Tuple									= load_image(A_)

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

											lowerCAmelCase_			:     Tuple									= self.model.config.model_type

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

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

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

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

									else:
											lowerCAmelCase_			:     str									= self.image_processor(images=A_				,				return_tensors=self.framework)

									if self.model.config.model_type == "git" and prompt is None:
											lowerCAmelCase_			:     List[Any]									= None

									return model_inputs




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Union[str, Any]				,				A_     :       Optional[int]=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''']				,				A_)
									    and all(x is None for x in model_inputs['''input_ids'''])
									):
											lowerCAmelCase_			:     Optional[Any]									= None

									if generate_kwargs is None:
											lowerCAmelCase_			:     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.
									lowerCAmelCase_			:     Any									= model_inputs.pop(self.model.main_input_name)
									lowerCAmelCase_			:     Tuple									= self.model.generate(A_				,				**A_				,				**A_)
									return model_outputs







							def    UpperCAmelCase__			(       self     :       int				,				A_     :       int):
									lowerCAmelCase_			:     Tuple									= []
									for output_ids in model_outputs:
											lowerCAmelCase_			:     Optional[int]									= {
											    '''generated_text''': self.tokenizer.decode(
											        A_				,				skip_special_tokens=A_				,				)
											}
											records.append(A_)
									return records










A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





