






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

a_		:						Tuple             =    {
    """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  (  __snake_case					):
   _lowercase		:    int			     =							'perceiver'






   def __init__(       self		,					a=256		,					a=1280		,					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=2048		,					a=56		,					a=[368, 496]		,					a=16		,					a=1920		,					a=16		,					a=[1, 16, 224, 224]		,					**a		,					)      ->       List[Any]:
      super().__init__(**A_)

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



class 		_snake_case  (  __snake_case					):






   @property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:




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






   @property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      return 1E-4







   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = -1		,					a = -1		,					a = -1		,					a = False		,					a = None		,					a = 3		,					a = 40		,					a = 40		,					)      ->       Any:




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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




