





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


lowercase    : List[Any]    	=  logging.get_logger(__name__)

lowercase    : int    	=  {
    """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(						__UpperCAmelCase ):
     __A	  =       "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-1_2,       A=True,       A=262,       A=2048,       A=56,       A=[368, 496],       A=16,       A=1920,       A=16,       A=[1, 16, 224, 224],       **A,       ):





            """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  __A(						__UpperCAmelCase ):

     @property
     def 	_UpperCamelCase     (      self      ):





            """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      ):





            """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,       ):





            """simple docstring"""

            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 __future__ import annotations

from collections.abc import Iterator

class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						value
            _UpperCamelCase              =						None
            _UpperCamelCase              =						None






class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						tree

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
            )



     def __iter__(      self      ):





            """simple docstring"""

            yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






