
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(_UpperCAmelCase       )
class 	_lowerCAmelCase					(	_UpperCAmelCase       ):




				def __init__(					self				,				*_UpperCamelCase				,				**_UpperCamelCase     )				->      Union[str, Any]:
							super().__init__(*_UpperCAmelCase				,				**_UpperCAmelCase     )
							requires_backends(self				,				"vision"     )
							self.check_model_type(_UpperCAmelCase     )




				def __call__(					self				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      Any:
							return super().__call__(_UpperCAmelCase				,				**_UpperCAmelCase     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Tuple:
							return {}, {}, {}




				def      __a						(					self				,				_UpperCamelCase     )				->      Dict:
							lowerCAmelCase_						=	load_image(_UpperCAmelCase     )
							lowerCAmelCase_						=	image.size
							lowerCAmelCase_						=	self.image_processor(images=_UpperCAmelCase				,				return_tensors=self.framework     )
							return model_inputs




				def      __a						(					self				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	self.model(**_UpperCAmelCase     )
							return model_outputs




				def      __a						(					self				,				_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	model_outputs.predicted_depth
							lowerCAmelCase_						=	torch.nn.functional.interpolate(
							    predicted_depth.unsqueeze(1     )				,				size=self.image_size[::-1]				,				mode="bicubic"				,				align_corners=_UpperCAmelCase     )
							lowerCAmelCase_						=	prediction.squeeze().cpu().numpy()
							lowerCAmelCase_						=	(output * 255 / np.max(_UpperCAmelCase     )).astype("uint8"     )
							lowerCAmelCase_						=	Image.fromarray(_UpperCAmelCase     )
							lowerCAmelCase_						=	{}
							lowerCAmelCase_						=	predicted_depth
							lowerCAmelCase_						=	depth
							return output_dict




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A			   = logging.get_logger(__name__)

__A			   = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A			   = {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, Any]					=      [self.cls_token_id]
       if token_ids_a is None:
          return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
