


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__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,__lowerCAmelCase    ):
										snake_case					=    np.full((len(__lowerCAmelCase    ), sequence_length, 2)							,__lowerCAmelCase    )
					else:
										snake_case					=    np.full((len(__lowerCAmelCase    ), sequence_length)							,__lowerCAmelCase    )

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

					return out_tensor.tolist()
def       UpperCAmelCase__						(UpperCamelCase_    ):



					"""simple docstring"""






					snake_case					=    ord(__lowerCAmelCase    )
					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
					snake_case					=    unicodedata.category(__lowerCAmelCase    )
					if cat.startswith('''P'''    ):
										return True
					return False

@dataclass
class   A__ (   UpperCamelCase_     ):







				"""simple docstring"""

				__magic_name__        =		42
				__magic_name__        =		True
				__magic_name__        =		None
				__magic_name__        =		None
				__magic_name__        =		-1_00
				__magic_name__        =		"pt"
				def 		a_      (	self      , __snake_case   ):
									import torch

									snake_case					=    """label""" if """label""" in features[0].keys() else """labels"""
									snake_case					=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
									snake_case					=    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

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

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

									return batch








from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


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

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




    @property
    def 		a_      (	self   ):
         return 1E-5




