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

_UpperCAmelCase      :				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        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       "perceiver"



		def __init__(				self       ,   A_=256       ,   A_=1_280       ,   A_=768       ,   A_=1       ,   A_=26       ,   A_=8       ,   A_=8       ,   A_=None       ,   A_=None       ,   A_="kv"       ,   A_=1       ,   A_=1       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.02       ,   A_=1e-12       ,   A_=True       ,   A_=262       ,   A_=2_048       ,   A_=56       ,   A_=[368, 496]       ,   A_=16       ,   A_=1_920       ,   A_=16       ,   A_=[1, 16, 224, 224]       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(**A_ )

				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        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self )						->      float:
				"""simple docstring"""
				return 1e-4



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = -1       ,   A_ = -1       ,   A_ = -1       ,   A_ = False       ,   A_ = None       ,   A_ = 3       ,   A_ = 40       ,   A_ = 40       ,   )						->      Mapping[str, Any]:
				"""simple docstring"""
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(A_       ,   A_ ):
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						UpperCamelCase						=  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
						UpperCamelCase						=  preprocessor.num_special_tokens_to_add(A_ )
						UpperCamelCase						=  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
						UpperCamelCase						=  [' '.join(['a'] ) * seq_length] * batch_size
						UpperCamelCase						=  dict(preprocessor(A_       ,   return_tensors=A_ ) )
						UpperCamelCase						=  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
						UpperCamelCase						=  compute_effective_axis_dimension(A_       ,   fixed_dimension=OnnxConfig.default_fixed_batch )
						UpperCamelCase						=  self._generate_dummy_images(A_       ,   A_       ,   A_       ,   A_ )
						UpperCamelCase						=  dict(preprocessor(images=A_       ,   return_tensors=A_ ) )
						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.' )


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				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						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
