



"""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


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						List[str]    							=      {
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= 'beit'

					def __init__(    self				,       lowerCamelCase__=8_1_9_2				,       lowerCamelCase__=7_6_8				,       lowerCamelCase__=1_2				,       lowerCamelCase__=1_2				,       lowerCamelCase__=3_0_7_2				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=1e-12				,       lowerCamelCase__=2_2_4				,       lowerCamelCase__=1_6				,       lowerCamelCase__=3				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=True				,       lowerCamelCase__=[3, 5, 7, 1_1]				,       lowerCamelCase__=[1, 2, 3, 6]				,       lowerCamelCase__=True				,       lowerCamelCase__=0.4				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=1				,       lowerCamelCase__=False				,       lowerCamelCase__=2_5_5				,       **lowerCamelCase__				,       ):
									super().__init__(**lowerCamelCase__   )

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				layer_norm_eps

									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				use_mask_token
									_lowerCamelCase        =				use_absolute_position_embeddings
									_lowerCamelCase        =				use_relative_position_bias
									_lowerCamelCase        =				use_shared_relative_position_bias
									_lowerCamelCase        =				layer_scale_init_value
									_lowerCamelCase        =				drop_path_rate
									_lowerCamelCase        =				use_mean_pooling
									# decode head attributes (semantic segmentation)
									_lowerCamelCase        =				out_indices
									_lowerCamelCase        =				pool_scales
									# auxiliary head attributes (semantic segmentation)
									_lowerCamelCase        =				use_auxiliary_head
									_lowerCamelCase        =				auxiliary_loss_weight
									_lowerCamelCase        =				auxiliary_channels
									_lowerCamelCase        =				auxiliary_num_convs
									_lowerCamelCase        =				auxiliary_concat_input
									_lowerCamelCase        =				semantic_loss_ignore_index


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Dict										= version.parse('1.11'    )

					@property
					def 			snake_case__      (    self   ):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									    ]   )





					@property
					def 			snake_case__      (    self   ):
									return 1e-4





"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
			from .pipeline_kandinsky import KandinskyPipeline
			from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
			from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
			from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
			from .text_encoder import MultilingualCLIP
