






"""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							=  logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Dict     ,		*__snake_case  :      Any     ,		**__snake_case  :      List[str]					)			->							Optional[int]:
											super().__init__(*__snake_case     ,		**__snake_case					)
											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   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      Dict=None     ,		__snake_case  :      Optional[Any]=None					)			->							Optional[Any]:
											__magic_name__:	Optional[Any]							=       {}
											__magic_name__:	List[Any]							=       {}

											if prompt is not None:
																	__magic_name__:	Tuple							=       prompt

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

					def __call__(			self  :      str     ,		__snake_case  :      Union[str, List[str], "Image.Image", List["Image.Image"]]     ,		**__snake_case  :      Dict					)			->							Optional[Any]:
											return super().__call__(__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Tuple     ,		__snake_case  :      Tuple=None					)			->							Optional[int]:
											__magic_name__:	Optional[int]							=       load_image(__snake_case					)

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

																	__magic_name__:	Union[str, Any]							=       self.model.config.model_type

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

																	elif model_type == "pix2struct":
																							__magic_name__:	Union[str, Any]							=       self.image_processor(images=__snake_case     ,		header_text=__snake_case     ,		return_tensors=self.framework					)

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							__magic_name__:	Union[str, Any]							=       self.image_processor(images=__snake_case     ,		return_tensors=self.framework					)
																							__magic_name__:	Tuple							=       self.tokenizer(__snake_case     ,		return_tensors=self.framework					)
																							model_inputs.update(__snake_case					)

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

											else:
																	__magic_name__:	Union[str, Any]							=       self.image_processor(images=__snake_case     ,		return_tensors=self.framework					)

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

											return model_inputs

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[Any]=None					)			->							List[Any]:
											# 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"""]     ,		__snake_case					)
											    and all(x is None for x in model_inputs["""input_ids"""]					)
											):
																	__magic_name__:	Optional[int]							=       None

											if generate_kwargs is None:
																	__magic_name__:	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.
											__magic_name__:	Dict							=       model_inputs.pop(self.model.main_input_name					)
											__magic_name__:	Optional[int]							=       self.model.generate(__snake_case     ,		**__snake_case     ,		**__snake_case					)
											return model_outputs






					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Any					)			->							Optional[Any]:
											__magic_name__:	Union[str, Any]							=       []
											for output_ids in model_outputs:
																	__magic_name__:	int							=       {
																	    """generated_text""": self.tokenizer.decode(
																	        __snake_case     ,		skip_special_tokens=__snake_case     ,		)
																	}
																	records.append(__snake_case					)
											return records







'''simple docstring'''





from ..utils import DummyObject, requires_backends




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	List[Any]			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	int			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )



