





"""simple docstring"""


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        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Tuple:




      """simple docstring"""

      if isinstance(lowercase_		,lowercase_      ):
            _UpperCamelCase   :						Dict    =							np.full((len(lowercase_      ), sequence_length, 2)		,lowercase_      )
      else:
            _UpperCamelCase   :						Optional[Any]    =							np.full((len(lowercase_      ), sequence_length)		,lowercase_      )

      for i, tensor in enumerate(lowercase_      ):
            if padding_side == "right":
                  if isinstance(lowercase_		,lowercase_      ):
                        _UpperCamelCase   :						int    =							tensor[:sequence_length]
                  else:
                        _UpperCamelCase   :						List[str]    =							tensor[:sequence_length]
            else:
                  if isinstance(lowercase_		,lowercase_      ):
                        _UpperCamelCase   :						Union[str, Any]    =							tensor[:sequence_length]
                  else:
                        _UpperCamelCase   :						Dict    =							tensor[:sequence_length]

      return out_tensor.tolist()




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							ord(lowercase_      )
      if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
            return True
      _UpperCamelCase   :						Union[str, Any]    =							unicodedata.category(lowercase_      )
      if cat.startswith("P"      ):
            return True
      return False



@dataclass
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  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  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							int						)		->					Dict:
        import torch

        _UpperCamelCase   :						Optional[Any]    =							"label" if "label" in features[0].keys() else "labels"
        _UpperCamelCase   :						Tuple    =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
        _UpperCamelCase   :						Dict    =							self.tokenizer.pad(
            __a       , 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

        _UpperCamelCase   :						Optional[int]    =							torch.tensor(batch["entity_ids"]						).shape[1]
        _UpperCamelCase   :						List[Any]    =							self.tokenizer.padding_side
        if padding_side == "right":
              _UpperCamelCase   :						Optional[Any]    =							[
                  list(__a						) + [self.label_pad_token_id] * (sequence_length - len(__a						)) for label in labels
              ]
        else:
              _UpperCamelCase   :						Any    =							[
                  [self.label_pad_token_id] * (sequence_length - len(__a						)) + list(__a						) for label in labels
              ]

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

        return batch







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
