






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


a		:       List[str]			     = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a		:       List[Any]			     = {
    'vocab_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
        ),
        'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-generator': (
            'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
        ),
    },
}

a		:       Optional[Any]			     = {
    'google/electra-small-generator': 512,
    'google/electra-base-generator': 512,
    'google/electra-large-generator': 512,
    'google/electra-small-discriminator': 512,
    'google/electra-base-discriminator': 512,
    'google/electra-large-discriminator': 512,
}

a		:       Optional[Any]			     = {
    'google/electra-small-generator': {'do_lower_case': True},
    'google/electra-base-generator': {'do_lower_case': True},
    'google/electra-large-generator': {'do_lower_case': True},
    'google/electra-small-discriminator': {'do_lower_case': True},
    'google/electra-base-discriminator': {'do_lower_case': True},
    'google/electra-large-discriminator': {'do_lower_case': True},
}
class     a    ( a__   ):



      """simple docstring"""


      a  : List[str]							       =	VOCAB_FILES_NAMES
      a  : Tuple							       =	PRETRAINED_VOCAB_FILES_MAP
      a  : Union[str, Any]							       =	PRETRAINED_INIT_CONFIGURATION
      a  : Union[str, Any]							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : Dict							       =	ElectraTokenizer
      def __init__(      self					:			Any		,      __lowercase					:			int=None		,      __lowercase					:			List[str]=None		,      __lowercase					:			int=True		,      __lowercase					:			List[str]="[UNK]"		,      __lowercase					:			str="[SEP]"		,      __lowercase					:			Dict="[PAD]"		,      __lowercase					:			Any="[CLS]"		,      __lowercase					:			str="[MASK]"		,      __lowercase					:			Union[str, Any]=True		,      __lowercase					:			int=None		,      **__lowercase					:			Any		,      )							->      List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE__		,      tokenizer_file=SCREAMING_SNAKE_CASE__		,      do_lower_case=SCREAMING_SNAKE_CASE__		,      unk_token=SCREAMING_SNAKE_CASE__		,      sep_token=SCREAMING_SNAKE_CASE__		,      pad_token=SCREAMING_SNAKE_CASE__		,      cls_token=SCREAMING_SNAKE_CASE__		,      mask_token=SCREAMING_SNAKE_CASE__		,      tokenize_chinese_chars=SCREAMING_SNAKE_CASE__		,      strip_accents=SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__		,      )

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

        __UpperCAmelCase							:	int           =  do_lower_case
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			Optional[int]		,      __lowercase					:			Tuple=None					)							->      List[str]:
        __UpperCAmelCase							:	List[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 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Dict		,      __lowercase					:			Union[str, Any] = None					)							->      List[int]:
        __UpperCAmelCase							:	Optional[Any]           =  [self.sep_token_id]
        __UpperCAmelCase							:	int           =  [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 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Optional[int] = None					)							->      Tuple[str]:
        __UpperCAmelCase							:	str           =  self._tokenizer.model.save(SCREAMING_SNAKE_CASE__		,      name=SCREAMING_SNAKE_CASE__					)
        return tuple(SCREAMING_SNAKE_CASE__					)




"""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

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




