
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

lowercase__          =logging.get_logger(__name__)



@add_end_docstrings(__lowercase   )
class 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: str ,    *snake_case_			: Any ,    **snake_case_			: Tuple       ):
    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			: Tuple ,    snake_case_			: List[str]=None ,    snake_case_			: Union[str, Any]=None ,    snake_case_			: Any=None       ):
    __a			:  Tuple		    =  {}
    __a			:  Union[str, Any]		    =  {}

    if prompt is not None:
     __a			:  List[Any]		    =  prompt

    if generate_kwargs is not None:
     __a			:  Optional[int]		    =  generate_kwargs
    if max_new_tokens is not None:
     if "generate_kwargs" not in forward_kwargs:
      __a			:  Union[str, 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'''       )
     __a			:  List[str]		    =  max_new_tokens
    return preprocess_params, forward_kwargs, {}
   def __call__(self			: Tuple ,    snake_case_			: Union[str, List[str], "Image.Image", List["Image.Image"]] ,    **snake_case_			: List[str]       ):
    return super().__call__(snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Any ,    snake_case_			: List[str]=None       ):
    __a			:  Optional[Any]		    =  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.'''       )

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

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

     elif model_type == "pix2struct":
      __a			:  Optional[int]		    =  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
      __a			:  Union[str, Any]		    =  self.image_processor(images=snake_case_ ,    return_tensors=self.framework       )
      __a			:  str		    =  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:
     __a			:  Dict		    =  self.image_processor(images=snake_case_ ,    return_tensors=self.framework       )

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

    return model_inputs
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Optional[Any] ,    snake_case_			: 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'''] ,    snake_case_       )
        and all(x is None for x in model_inputs['''input_ids''']       )
    ):
     __a			:  str		    =  None

    if generate_kwargs is None:
     __a			:  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.
    __a			:  str		    =  model_inputs.pop(self.model.main_input_name       )
    __a			:  Dict		    =  self.model.generate(snake_case_ ,    **snake_case_ ,    **snake_case_       )
    return model_outputs







   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Optional[Any]       ):
    __a			:  str		    =  []
    for output_ids in model_outputs:
     __a			:  Optional[Any]		    =  {
         '''generated_text''': self.tokenizer.decode(
             snake_case_ ,    skip_special_tokens=snake_case_ ,    )
     }
     records.append(snake_case_       )
    return records


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
