



'''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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "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'''







 __lowerCamelCase       :			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					,  )       ->	Union[str, Any]:


      """simple docstring"""



      super().__init__(**A			)

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






class __A     (    A		):



 '''simple docstring'''







 @property
 def    a__    (self			)       ->	Mapping[str, Mapping[int, str]]:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	float:


      """simple docstring"""



      return 1E-4



 def    a__    (self					,  A					,  A = -1					,  A = -1					,  A = -1					,  A = False					,  A = None					,  A = 3					,  A = 40					,  A = 40					,  )       ->	Mapping[str, Any]:


      """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
           _a           =    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
           _a           =    preprocessor.num_special_tokens_to_add(A			)
           _a           =    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
           _a           =    [''' '''.join(['''a''']			) * seq_length] * batch_size
           _a           =    dict(preprocessor(A					,  return_tensors=A			)			)
           _a           =    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
           _a           =    compute_effective_axis_dimension(A					,  fixed_dimension=OnnxConfig.default_fixed_batch			)
           _a           =    self._generate_dummy_images(A					,  A					,  A					,  A			)
           _a           =    dict(preprocessor(images=A					,  return_tensors=A			)			)
           _a           =    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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_									=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ["BlipImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BlipModel",
      "BlipPreTrainedModel",
      "BlipForConditionalGeneration",
      "BlipForQuestionAnswering",
      "BlipVisionModel",
      "BlipTextModel",
      "BlipForImageTextRetrieval",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFBlipModel",
      "TFBlipPreTrainedModel",
      "TFBlipForConditionalGeneration",
      "TFBlipForQuestionAnswering",
      "TFBlipVisionModel",
      "TFBlipTextModel",
      "TFBlipForImageTextRetrieval",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

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



