
"""simple docstring"""







from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 				A__ (			__snake_case):
		A_		: int            =			'perceiver'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=12_80 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=26 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="kv" ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=2_62 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=56 ,	_SCREAMING_SNAKE_CASE=[3_68, 4_96] ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=19_20 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=[1, 16, 2_24, 2_24] ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**A_				)

			__lowerCAmelCase    :		List[Any]		    =   num_latents
			__lowerCAmelCase    :		int		    =   d_latents
			__lowerCAmelCase    :		List[Any]		    =   d_model
			__lowerCAmelCase    :		Union[str, Any]		    =   num_blocks
			__lowerCAmelCase    :		List[Any]		    =   num_self_attends_per_block
			__lowerCAmelCase    :		List[str]		    =   num_self_attention_heads
			__lowerCAmelCase    :		str		    =   num_cross_attention_heads
			__lowerCAmelCase    :		Optional[Any]		    =   qk_channels
			__lowerCAmelCase    :		List[Any]		    =   v_channels
			__lowerCAmelCase    :		Tuple		    =   cross_attention_shape_for_attention
			__lowerCAmelCase    :		Optional[int]		    =   self_attention_widening_factor
			__lowerCAmelCase    :		List[str]		    =   cross_attention_widening_factor
			__lowerCAmelCase    :		List[str]		    =   hidden_act
			__lowerCAmelCase    :		List[str]		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[int]		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   layer_norm_eps
			__lowerCAmelCase    :		Optional[Any]		    =   use_query_residual
			# masked language modeling attributes
			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Optional[int]		    =   max_position_embeddings
			# image classification attributes
			__lowerCAmelCase    :		Dict		    =   image_size
			# flow attributes
			__lowerCAmelCase    :		str		    =   train_size
			# multimodal autoencoding attributes
			__lowerCAmelCase    :		Optional[int]		    =   num_frames
			__lowerCAmelCase    :		List[Any]		    =   audio_samples_per_frame
			__lowerCAmelCase    :		int		    =   samples_per_patch
			__lowerCAmelCase    :		str		    =   output_shape




class 				A__ (			__snake_case):

		@property
		def 							__lowerCamelCase							(   self				):




			if self.task == "multiple-choice":
				__lowerCAmelCase    :		Any		    =   {0: "batch", 1: "choice", 2: "sequence"}
			else:
				__lowerCAmelCase    :		Any		    =   {0: "batch", 1: "sequence"}
			return OrderedDict(
			    [
			        ('inputs', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ]				)

		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = -1 ,	_SCREAMING_SNAKE_CASE = -1 ,	_SCREAMING_SNAKE_CASE = -1 ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 3 ,	_SCREAMING_SNAKE_CASE = 40 ,	_SCREAMING_SNAKE_CASE = 40 ,	):




			if isinstance(A_ ,	A_				):
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				__lowerCAmelCase    :		Union[str, Any]		    =   compute_effective_axis_dimension(
				    A_ ,	fixed_dimension=OnnxConfig.default_fixed_batch ,	num_token_to_add=0				)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				__lowerCAmelCase    :		Optional[int]		    =   preprocessor.num_special_tokens_to_add(A_				)
				__lowerCAmelCase    :		List[Any]		    =   compute_effective_axis_dimension(
				    A_ ,	fixed_dimension=OnnxConfig.default_fixed_sequence ,	num_token_to_add=A_				)
				# Generate dummy inputs according to compute batch and sequence
				__lowerCAmelCase    :		str		    =   [" ".join(['a']				) * seq_length] * batch_size
				__lowerCAmelCase    :		Tuple		    =   dict(preprocessor(A_ ,	return_tensors=A_				)				)
				__lowerCAmelCase    :		int		    =   inputs.pop('input_ids'				)
				return inputs
			elif isinstance(A_ ,	A_				) and preprocessor.model_input_names[0] == "pixel_values":
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				__lowerCAmelCase    :		List[str]		    =   compute_effective_axis_dimension(A_ ,	fixed_dimension=OnnxConfig.default_fixed_batch				)
				__lowerCAmelCase    :		List[Any]		    =   self._generate_dummy_images(A_ ,	A_ ,	A_ ,	A_				)
				__lowerCAmelCase    :		Tuple		    =   dict(preprocessor(images=A_ ,	return_tensors=A_				)				)
				__lowerCAmelCase    :		Any		    =   inputs.pop('pixel_values'				)
				return inputs
			else:
				raise ValueError(
				    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'				)

"""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__           =	{
    """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 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'beit'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=81_92 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=2_24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[3, 5, 7, 11] ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 6] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		Dict		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
			__lowerCAmelCase    :		List[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   initializer_range
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm_eps

			__lowerCAmelCase    :		Any		    =   image_size
			__lowerCAmelCase    :		Union[str, Any]		    =   patch_size
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   use_mask_token
			__lowerCAmelCase    :		Any		    =   use_absolute_position_embeddings
			__lowerCAmelCase    :		int		    =   use_relative_position_bias
			__lowerCAmelCase    :		List[str]		    =   use_shared_relative_position_bias
			__lowerCAmelCase    :		Tuple		    =   layer_scale_init_value
			__lowerCAmelCase    :		int		    =   drop_path_rate
			__lowerCAmelCase    :		Dict		    =   use_mean_pooling
			# decode head attributes (semantic segmentation)
			__lowerCAmelCase    :		Optional[Any]		    =   out_indices
			__lowerCAmelCase    :		Optional[Any]		    =   pool_scales
			# auxiliary head attributes (semantic segmentation)
			__lowerCAmelCase    :		Union[str, Any]		    =   use_auxiliary_head
			__lowerCAmelCase    :		Optional[int]		    =   auxiliary_loss_weight
			__lowerCAmelCase    :		Optional[Any]		    =   auxiliary_channels
			__lowerCAmelCase    :		List[str]		    =   auxiliary_num_convs
			__lowerCAmelCase    :		List[str]		    =   auxiliary_concat_input
			__lowerCAmelCase    :		Optional[int]		    =   semantic_loss_ignore_index




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

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







		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4