




'''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


a  :				Tuple									=  logging.get_logger(__name__)

a  :				List[str]									=  {
    '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				(   _lowerCamelCase     ):
				snake_case_      =		"perceiver"


				def __init__(		self    :					Dict       ,				lowercase_    :					str=256       ,				lowercase_    :					List[Any]=1280       ,				lowercase_    :					Dict=768       ,				lowercase_    :					str=1       ,				lowercase_    :					Optional[int]=26       ,				lowercase_    :					Any=8       ,				lowercase_    :					Tuple=8       ,				lowercase_    :					int=None       ,				lowercase_    :					List[str]=None       ,				lowercase_    :					Any="kv"       ,				lowercase_    :					str=1       ,				lowercase_    :					int=1       ,				lowercase_    :					List[str]="gelu"       ,				lowercase_    :					List[str]=0.1       ,				lowercase_    :					Union[str, Any]=0.02       ,				lowercase_    :					Optional[Any]=1e-12       ,				lowercase_    :					Any=True       ,				lowercase_    :					List[Any]=262       ,				lowercase_    :					List[str]=2048       ,				lowercase_    :					str=56       ,				lowercase_    :					int=[368, 496]       ,				lowercase_    :					Any=16       ,				lowercase_    :					Optional[int]=1920       ,				lowercase_    :					Optional[int]=16       ,				lowercase_    :					Union[str, Any]=[1, 16, 224, 224]       ,				**lowercase_    :					List[str]       ,				):
									super().__init__(**lowercase_					)

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







class 						a				(   _lowerCamelCase     ):


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


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


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

									if isinstance(lowercase_       ,				lowercase_					):
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_									=				compute_effective_axis_dimension(
														    lowercase_       ,				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
														snake_case_									=				preprocessor.num_special_tokens_to_add(lowercase_					)
														snake_case_									=				compute_effective_axis_dimension(
														    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowercase_					)
														# Generate dummy inputs according to compute batch and sequence
														snake_case_									=				[''' '''.join(['''a''']					) * seq_length] * batch_size
														snake_case_									=				dict(preprocessor(lowercase_       ,				return_tensors=lowercase_					)					)
														snake_case_									=				inputs.pop('''input_ids'''					)
														return inputs
									elif isinstance(lowercase_       ,				lowercase_					) 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
														snake_case_									=				compute_effective_axis_dimension(lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_batch					)
														snake_case_									=				self._generate_dummy_images(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
														snake_case_									=				dict(preprocessor(images=lowercase_       ,				return_tensors=lowercase_					)					)
														snake_case_									=				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 ..utils import DummyObject, requires_backends






class 						a				(   metaclass=_lowerCamelCase     ):
				snake_case_      =		["transformers", "torch", "note_seq"]


				def __init__(		self    :					Union[str, Any]       ,				*lowercase_    :					Optional[int]       ,				**lowercase_    :					int					):
									requires_backends(self       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					List[Any]       ,				*lowercase_    :					Optional[Any]       ,				**lowercase_    :					str					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					Tuple       ,				*lowercase_    :					Union[str, Any]       ,				**lowercase_    :					List[Any]					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)

