




from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__  =		logging.get_logger(__name__)

UpperCamelCase__  =		{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class        __SCREAMING_SNAKE_CASE							(	__UpperCAmelCase						):
					_lowerCamelCase							=				SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
					_lowerCamelCase							=				"""squeezebert"""

					def __init__(       self				, __lowercase=30_522				, __lowercase=768				, __lowercase=12				, __lowercase=12				, __lowercase=3_072				, __lowercase="gelu"				, __lowercase=0.1				, __lowercase=0.1				, __lowercase=512				, __lowercase=2				, __lowercase=0.02				, __lowercase=1E-12				, __lowercase=0				, __lowercase=768				, __lowercase=4				, __lowercase=4				, __lowercase=4				, __lowercase=1				, __lowercase=4				, __lowercase=4				, **__lowercase				, ):







						'''simple docstring'''




						super().__init__(pad_token_id=lowercase_				, **lowercase_      )

						lowerCamelCase_							:	Optional[Any]							     =							vocab_size
						lowerCamelCase_							:	Union[str, Any]							     =							hidden_size
						lowerCamelCase_							:	List[str]							     =							num_hidden_layers
						lowerCamelCase_							:	Dict							     =							num_attention_heads
						lowerCamelCase_							:	List[Any]							     =							hidden_act
						lowerCamelCase_							:	str							     =							intermediate_size
						lowerCamelCase_							:	List[str]							     =							hidden_dropout_prob
						lowerCamelCase_							:	Optional[Any]							     =							attention_probs_dropout_prob
						lowerCamelCase_							:	Dict							     =							max_position_embeddings
						lowerCamelCase_							:	Tuple							     =							type_vocab_size
						lowerCamelCase_							:	Optional[Any]							     =							initializer_range
						lowerCamelCase_							:	str							     =							layer_norm_eps
						lowerCamelCase_							:	Optional[Any]							     =							embedding_size
						lowerCamelCase_							:	Optional[Any]							     =							q_groups
						lowerCamelCase_							:	Optional[Any]							     =							k_groups
						lowerCamelCase_							:	Dict							     =							v_groups
						lowerCamelCase_							:	List[Any]							     =							post_attention_groups
						lowerCamelCase_							:	List[Any]							     =							intermediate_groups
						lowerCamelCase_							:	Optional[int]							     =							output_groups


class        __SCREAMING_SNAKE_CASE							(	__UpperCAmelCase						):

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_							:	List[str]							     =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
							lowerCamelCase_							:	Optional[Any]							     =							{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]      )






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .image_encoder import PaintByExampleImageEncoder
							from .pipeline_paint_by_example import PaintByExamplePipeline
