





UpperCAmelCase_					:   str 							=						[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







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_					:   Optional[Any] 							=						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__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







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

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





class      lowercase__    (							__A  ):







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







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 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
            lowerCAmelCase_					:       Union[str, Any]    =		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
            lowerCAmelCase_					:       int    =		preprocessor.num_special_tokens_to_add(_lowercase						)
            lowerCAmelCase_					:       int    =		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
            lowerCAmelCase_					:       Optional[int]    =		[""" """.join(["""a"""]						) * seq_length] * batch_size
            lowerCAmelCase_					:       Optional[Any]    =		dict(preprocessor(_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Optional[Any]    =		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
            lowerCAmelCase_					:       Any    =		compute_effective_axis_dimension(_lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch						)
            lowerCAmelCase_					:       str    =		self._generate_dummy_images(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       List[str]    =		dict(preprocessor(images=_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Tuple    =		inputs.pop("""pixel_values"""						)
            return inputs
       else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""						)
