




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

__lowerCAmelCase	:       Optional[Any]     =	{
    '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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      """perceiver"""





				def __init__(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple=2_56      ,   __lowerCamelCase       :  int=12_80      ,   __lowerCamelCase       :  Union[str, Any]=7_68      ,   __lowerCamelCase       :  Any=1      ,   __lowerCamelCase       :  Optional[Any]=26      ,   __lowerCamelCase       :  Optional[int]=8      ,   __lowerCamelCase       :  Union[str, Any]=8      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  List[Any]="kv"      ,   __lowerCamelCase       :  Any=1      ,   __lowerCamelCase       :  Tuple=1      ,   __lowerCamelCase       :  Any="gelu"      ,   __lowerCamelCase       :  str=0.1      ,   __lowerCamelCase       :  Union[str, Any]=0.02      ,   __lowerCamelCase       :  Any=1e-12      ,   __lowerCamelCase       :  Any=True      ,   __lowerCamelCase       :  Optional[Any]=2_62      ,   __lowerCamelCase       :  Union[str, Any]=20_48      ,   __lowerCamelCase       :  Tuple=56      ,   __lowerCamelCase       :  Optional[Any]=[3_68, 4_96]      ,   __lowerCamelCase       :  str=16      ,   __lowerCamelCase       :  int=19_20      ,   __lowerCamelCase       :  int=16      ,   __lowerCamelCase       :  Optional[Any]=[1, 16, 2_24, 2_24]      ,   **__lowerCamelCase       :  Union[str, Any]      ,   )      ->  Optional[Any]:
							super().__init__(**__lowerCamelCase							)

							a					=    num_latents
							a					=    d_latents
							a					=    d_model
							a					=    num_blocks
							a					=    num_self_attends_per_block
							a					=    num_self_attention_heads
							a					=    num_cross_attention_heads
							a					=    qk_channels
							a					=    v_channels
							a					=    cross_attention_shape_for_attention
							a					=    self_attention_widening_factor
							a					=    cross_attention_widening_factor
							a					=    hidden_act
							a					=    attention_probs_dropout_prob
							a					=    initializer_range
							a					=    layer_norm_eps
							a					=    use_query_residual
							# masked language modeling attributes
							a					=    vocab_size
							a					=    max_position_embeddings
							# image classification attributes
							a					=    image_size
							# flow attributes
							a					=    train_size
							# multimodal autoencoding attributes
							a					=    num_frames
							a					=    audio_samples_per_frame
							a					=    samples_per_patch
							a					=    output_shape




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										a					=    {0: "batch", 1: "choice", 2: "sequence"}
							else:
										a					=    {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]							)





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





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,   __lowerCamelCase       :  int = -1      ,   __lowerCamelCase       :  int = -1      ,   __lowerCamelCase       :  int = -1      ,   __lowerCamelCase       :  bool = False      ,   __lowerCamelCase       :  Optional[TensorType] = None      ,   __lowerCamelCase       :  int = 3      ,   __lowerCamelCase       :  int = 40      ,   __lowerCamelCase       :  int = 40      ,   )      ->  Mapping[str, Any]:
							# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCAmelCase	:       Union[str, Any]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






