


'''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

__snake_case    						=logging.get_logger(__name__)

@add_end_docstrings(__lowercase		)
class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Tuple    ,      *UpperCAmelCase__       :      int    ,      **UpperCAmelCase__       :      Optional[int]    )				->  int:
        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 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      List[str]=None    ,      UpperCAmelCase__       :      Tuple=None    ,      UpperCAmelCase__       :      Dict=None    )				->  Tuple:
        lowerCAmelCase     =			{}
        lowerCAmelCase     =			{}

        if prompt is not None:
           lowerCAmelCase     =			prompt

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





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





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[Any]=None    )				->  Dict:
        lowerCAmelCase     =			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     =			self.model.config.model_type

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

           elif model_type == "pix2struct":
              lowerCAmelCase     =			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     =			self.image_processor(images=UpperCAmelCase__    ,      return_tensors=self.framework    )
              lowerCAmelCase     =			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     =			self.image_processor(images=UpperCAmelCase__    ,      return_tensors=self.framework    )

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

        return model_inputs





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[Any]=None    )				->  str:
        # 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     =			None

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





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Tuple    )				->  Optional[int]:
        lowerCAmelCase     =			[]
        for output_ids in model_outputs:
           lowerCAmelCase     =			{
               'generated_text': self.tokenizer.decode(
                   UpperCAmelCase__    ,      skip_special_tokens=UpperCAmelCase__    ,      )
           }
           records.append(UpperCAmelCase__    )
        return records







class      A__							:






				def __init__(						self					:      Optional[Any]    ,     a					:      list   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		set_counts
							lowerCAmelCase__      :      str         =		max(a   )
							lowerCAmelCase__      :      Any         =		len(a   )
							lowerCAmelCase__      :      List[str]         =		[1] * num_sets
							lowerCAmelCase__      :      Dict         =		list(range(a   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.get_parent(a   )
							lowerCAmelCase__      :      Tuple         =		self.get_parent(a   )

							if src_parent == dst_parent:
										return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
										self.set_counts[dst_parent] += self.set_counts[src_parent]
										lowerCAmelCase__      :      Tuple         =		0
										lowerCAmelCase__      :      str         =		dst_parent
										if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
										lowerCAmelCase__      :      List[Any]         =		self.set_counts[dst_parent]
							else:
										self.set_counts[src_parent] += self.set_counts[dst_parent]
										lowerCAmelCase__      :      Optional[int]         =		0
										lowerCAmelCase__      :      Tuple         =		src_parent
										lowerCAmelCase__      :      Optional[int]         =		self.set_counts[src_parent]

							lowerCAmelCase__      :      Optional[Any]         =		max(self.max_set    ,     a   )
							return True






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      int   ):






							'''simple docstring'''







							if self.parents[disj_set] == disj_set:
										return disj_set
							lowerCAmelCase__      :      Tuple         =		self.get_parent(self.parents[disj_set]   )
							return self.parents[disj_set]