




"""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

_A													=      logging.get_logger(__name__)


@add_end_docstrings(a_						)
class _lowerCamelCase    (					a_						):






   def __init__(		self       :		str   ,     *UpperCamelCase       :		Optional[int]   ,     **UpperCamelCase       :		Optional[int]   )			->						List[Any]:

         """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 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Union[str, Any]=None   ,     UpperCamelCase       :		List[str]=None   ,     UpperCamelCase       :		Optional[Any]=None   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	{}
         lowerCAmelCase__      :			Any   =	{}

         if prompt is not None:
               lowerCAmelCase__      :			Any   =	prompt

         if generate_kwargs is not None:
               lowerCAmelCase__      :			str   =	generate_kwargs
         if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                     lowerCAmelCase__      :			Optional[int]   =	{}
               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__      :			Any   =	max_new_tokens
         return preprocess_params, forward_kwargs, {}






   def __call__(		self       :		Optional[int]   ,     UpperCamelCase       :		Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **UpperCamelCase       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         return super().__call__(UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Union[str, Any]=None   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	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."""   )

               lowerCAmelCase__      :			Optional[int]   =	self.model.config.model_type

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

               elif model_type == "pix2struct":
                     lowerCAmelCase__      :			Union[str, Any]   =	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
                     lowerCAmelCase__      :			Dict   =	self.image_processor(images=UpperCamelCase   ,     return_tensors=self.framework   )
                     lowerCAmelCase__      :			Tuple   =	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:
               lowerCAmelCase__      :			Dict   =	self.image_processor(images=UpperCamelCase   ,     return_tensors=self.framework   )

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

         return model_inputs






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		Dict=None   )			->						str:

         """simple docstring"""
         # 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"""]   ,     UpperCamelCase   )
             and all(x is None for x in model_inputs["""input_ids"""]   )
         ):
               lowerCAmelCase__      :			Optional[Any]   =	None

         if generate_kwargs is None:
               lowerCAmelCase__      :			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.
         lowerCAmelCase__      :			int   =	model_inputs.pop(self.model.main_input_name   )
         lowerCAmelCase__      :			Any   =	self.model.generate(UpperCamelCase   ,     **UpperCamelCase   ,     **UpperCamelCase   )
         return model_outputs






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Any   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	[]
         for output_ids in model_outputs:
               lowerCAmelCase__      :			Any   =	{
                   """generated_text""": self.tokenizer.decode(
                       UpperCamelCase   ,     skip_special_tokens=UpperCamelCase   ,     )
               }
               records.append(UpperCamelCase   )
         return records











"""simple docstring"""


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *





