



'''simple docstring'''





from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a_			=					logging.get_logger(__name__)



@add_end_docstrings(lowercase__   )
class  __lowerCAmelCase     (	lowercase__   ):




 def __init__(						self	,				*__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    super().__init__(*__lowercase	,				**__lowercase				)
    requires_backends(self	,				'''vision'''				)
    self.check_model_type(__lowercase				)




 def __call__(						self	,				__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return super().__call__(__lowercase	,				**__lowercase				)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return {}, {}, {}




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(__lowercase				)
    __lowerCamelCase													=    image.size
    __lowerCamelCase													=    self.image_processor(images=__lowercase	,				return_tensors=self.framework				)
    return model_inputs




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model(**__lowercase				)
    return model_outputs




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    model_outputs.predicted_depth
    __lowerCamelCase													=    torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1				)	,				size=self.image_size[::-1]	,				mode='''bicubic'''	,				align_corners=__lowercase				)
    __lowerCamelCase													=    prediction.squeeze().cpu().numpy()
    __lowerCamelCase													=    (output * 255 / np.max(__lowercase				)).astype('''uint8'''				)
    __lowerCamelCase													=    Image.fromarray(__lowercase				)
    __lowerCamelCase													=    {}
    __lowerCamelCase													=    predicted_depth
    __lowerCamelCase													=    depth
    return output_dict











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 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				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

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






