





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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    '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     __SCREAMING_SNAKE_CASE   (       _UpperCamelCase    ):
							snake_case       : Dict         =    'perceiver'






							def __init__(  self					,	__lowerCAmelCase=256					,	__lowerCAmelCase=1280					,	__lowerCAmelCase=768					,	__lowerCAmelCase=1					,	__lowerCAmelCase=26					,	__lowerCAmelCase=8					,	__lowerCAmelCase=8					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					,	__lowerCAmelCase="kv"					,	__lowerCAmelCase=1					,	__lowerCAmelCase=1					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1E-12					,	__lowerCAmelCase=True					,	__lowerCAmelCase=262					,	__lowerCAmelCase=2048					,	__lowerCAmelCase=56					,	__lowerCAmelCase=[368, 496]					,	__lowerCAmelCase=16					,	__lowerCAmelCase=1920					,	__lowerCAmelCase=16					,	__lowerCAmelCase=[1, 16, 224, 224]					,	**__lowerCAmelCase					,	):
														super().__init__(**_SCREAMING_SNAKE_CASE					)

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






class     __SCREAMING_SNAKE_CASE   (       _UpperCamelCase    ):






							@property
							def 			_lowerCamelCase						(  self					):
														if self.task == "multiple-choice":
																					UpperCamelCase__			       =     {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					UpperCamelCase__			       =     {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""inputs""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														    ]					)






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







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = -1					,	__lowerCAmelCase = -1					,	__lowerCAmelCase = -1					,	__lowerCAmelCase = False					,	__lowerCAmelCase = None					,	__lowerCAmelCase = 3					,	__lowerCAmelCase = 40					,	__lowerCAmelCase = 40					,	):
														# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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











import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase__     =	logging.getLogger(__name__)

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Optional[Any]         =    """masked_bert"""






							def __init__(  self					,	__lowerCAmelCase=30522					,	__lowerCAmelCase=768					,	__lowerCAmelCase=12					,	__lowerCAmelCase=12					,	__lowerCAmelCase=3072					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1E-12					,	__lowerCAmelCase=0					,	__lowerCAmelCase="topK"					,	__lowerCAmelCase="constant"					,	__lowerCAmelCase=0.0					,	**__lowerCAmelCase					,	):
														super().__init__(pad_token_id=__lowerCAmelCase					,	**__lowerCAmelCase					)

														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     num_hidden_layers
														UpperCamelCase__			       =     num_attention_heads
														UpperCamelCase__			       =     hidden_act
														UpperCamelCase__			       =     intermediate_size
														UpperCamelCase__			       =     hidden_dropout_prob
														UpperCamelCase__			       =     attention_probs_dropout_prob
														UpperCamelCase__			       =     max_position_embeddings
														UpperCamelCase__			       =     type_vocab_size
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     layer_norm_eps
														UpperCamelCase__			       =     pruning_method
														UpperCamelCase__			       =     mask_init
														UpperCamelCase__			       =     mask_scale




