'''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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """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    (		__a      ):


  """simple docstring"""

  A_    							=						'''perceiver'''




  def __init__(		self      :       List[Any]   ,				lowercase_      :       List[str]=256   ,				lowercase_      :       Union[str, Any]=1_280   ,				lowercase_      :       int=768   ,				lowercase_      :       Any=1   ,				lowercase_      :       Dict=26   ,				lowercase_      :       List[Any]=8   ,				lowercase_      :       int=8   ,				lowercase_      :       Tuple=None   ,				lowercase_      :       Any=None   ,				lowercase_      :       Tuple="kv"   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       str="gelu"   ,				lowercase_      :       Dict=0.1   ,				lowercase_      :       Union[str, Any]=0.0_2   ,				lowercase_      :       Any=1e-12   ,				lowercase_      :       Dict=True   ,				lowercase_      :       Tuple=262   ,				lowercase_      :       List[Any]=2_048   ,				lowercase_      :       Tuple=56   ,				lowercase_      :       Union[str, Any]=[368, 496]   ,				lowercase_      :       Union[str, Any]=16   ,				lowercase_      :       Optional[int]=1_920   ,				lowercase_      :       Dict=16   ,				lowercase_      :       Optional[int]=[1, 16, 224, 224]   ,				**lowercase_      :       Optional[int]   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )

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






class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


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




  @property
  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    return 1e-4





  def 		lowerCamelCase__  (		self      :       Any   ,				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   ,				):







    '''simple docstring'''


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

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__snake_case						  =		{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXJapaneseForCausalLM""",
           """GPTNeoXJapaneseLayer""",
           """GPTNeoXJapaneseModel""",
           """GPTNeoXJapanesePreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
       from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox_japanese import (
                  GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXJapaneseForCausalLM,
                  GPTNeoXJapaneseLayer,
                  GPTNeoXJapaneseModel,
                  GPTNeoXJapanesePreTrainedModel,
              )


else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

