"""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

SCREAMING_SNAKE_CASE__:Optional[int]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        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 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None		):
        __a     				=  {}
        __a     				=  {}

        if prompt is not None:
               __a     				=  prompt

        if generate_kwargs is not None:
               __a     				=  generate_kwargs
        if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                      __a     				=  {}
               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     				=  max_new_tokens
        return preprocess_params, forward_kwargs, {}


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        __a     				=  load_image(lowerCamelCase		)

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

               __a     				=  self.model.config.model_type

               if model_type == "git":
                      __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
                      __a     				=  self.tokenizer(text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase		).input_ids
                      __a     				=  [self.tokenizer.cls_token_id] + input_ids
                      __a     				=  torch.tensor(lowerCamelCase		).unsqueeze(0		)
                      model_inputs.update({"input_ids": input_ids}		)

               elif model_type == "pix2struct":
                      __a     				=  self.image_processor(images=lowerCamelCase  ,						header_text=lowerCamelCase  ,						return_tensors=self.framework		)

               elif model_type != "vision-encoder-decoder":
                      # vision-encoder-decoder does not support conditional generation
                      __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
                      __a     				=  self.tokenizer(lowerCamelCase  ,						return_tensors=self.framework		)
                      model_inputs.update(lowerCamelCase		)

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

        else:
               __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)

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

        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=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"]  ,						lowerCamelCase		)
            and all(x is None for x in model_inputs["input_ids"]		)
        ):
               __a     				=  None

        if generate_kwargs is None:
               __a     				=  {}
        # 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     				=  model_inputs.pop(self.model.main_input_name		)
        __a     				=  self.model.generate(lowerCamelCase  ,						**lowerCamelCase  ,						**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for output_ids in model_outputs:
               __a     				=  {
                   "generated_text": self.tokenizer.decode(
                       lowerCamelCase  ,						skip_special_tokens=lowerCamelCase  ,						)
               }
               records.append(lowerCamelCase		)
        return records

"""simple docstring"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)






class 				snake_case__     :
 _snake_case   :			List[str]           =		None



@experimental
def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a ,  a ,  a ,  a ,  a ,  a ,  a							)

       return _map_with_joblib(a ,  a ,  a ,  a ,  a ,  a ,  a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       __a     				=  num_proc if num_proc <= len(a							) else len(a							)
       __a     				=  []  # We organize the splits ourselve (contiguous splits)
       for index in range(a							):
              __a     				=  len(a							) // num_proc
              __a     				=  len(a							) % num_proc
              __a     				=  div * index + min(a ,  a							)
              __a     				=  start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

       if len(a							) != sum(len(i[1]							) for i in split_kwds							):
              raise ValueError(
                  F"Error dividing inputs iterable among processes. "
                  F"Total number of objects {len(a							)}, "
                  F"length: {sum(len(i[1]							) for i in split_kwds							)}"							)

       logger.info(
           F"Spawning {num_proc} processes for {len(a							)} objects in slices of {[len(i[1]							) for i in split_kwds]}"							)
       __a				, __a     				=  None, None
       if not disable_tqdm:
              __a				, __a     				=  (RLock(),), tqdm.set_lock
       with Pool(a ,  initargs=a ,  initializer=a							) as pool:
              __a     				=  pool.map(a ,  a							)
       logger.info(F"Finished {num_proc} processes"							)
       __a     				=  [obj for proc_res in mapped for obj in proc_res]
       logger.info(F"Unpacked {len(a							)} objects"							)

       return mapped



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
       # and it requires monkey-patching joblib internal classes which is subject to change
       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name ,  n_jobs=a							):
              return joblib.Parallel()(
                  joblib.delayed(a							)((function, obj, types, None, True, None)							) for obj in iterable							)




@experimental
@contextlib.contextmanager
def 					_lowerCamelCase(					a							):
       __a     				=  backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              __a     				=  None
