






'''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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_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 UpperCAmelCase_					(    snake_case_				):




      """simple docstring"""




      UpperCamelCase_								=			"""perceiver"""






      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		List[str]=256     ,      UpperCAmelCase			:		Dict=1280     ,      UpperCAmelCase			:		List[str]=768     ,      UpperCAmelCase			:		Tuple=1     ,      UpperCAmelCase			:		str=26     ,      UpperCAmelCase			:		str=8     ,      UpperCAmelCase			:		Dict=8     ,      UpperCAmelCase			:		Optional[int]=None     ,      UpperCAmelCase			:		List[str]=None     ,      UpperCAmelCase			:		Any="kv"     ,      UpperCAmelCase			:		int=1     ,      UpperCAmelCase			:		Optional[int]=1     ,      UpperCAmelCase			:		Optional[int]="gelu"     ,      UpperCAmelCase			:		str=0.1     ,      UpperCAmelCase			:		str=0.0_2     ,      UpperCAmelCase			:		List[str]=1e-12     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		Any=262     ,      UpperCAmelCase			:		Tuple=2048     ,      UpperCAmelCase			:		Any=56     ,      UpperCAmelCase			:		int=[368, 496]     ,      UpperCAmelCase			:		Optional[int]=16     ,      UpperCAmelCase			:		Optional[int]=1920     ,      UpperCAmelCase			:		int=16     ,      UpperCAmelCase			:		int=[1, 16, 224, 224]     ,      **UpperCAmelCase			:		Any     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)

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





class UpperCAmelCase_					(    snake_case_				):




      """simple docstring"""




      @property
      def 				A__      ( self			:		Optional[int]		)  ->  str:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       return 1e-4






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Union[str, Any] = -1     ,      UpperCAmelCase			:		List[str] = -1     ,      UpperCAmelCase			:		Optional[Any] = -1     ,      UpperCAmelCase			:		List[Any] = False     ,      UpperCAmelCase			:		str = None     ,      UpperCAmelCase			:		Tuple = 3     ,      UpperCAmelCase			:		str = 40     ,      UpperCAmelCase			:		List[str] = 40     ,      )  ->  int:


       '''simple docstring'''

       if isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        lowercase					:   Any				  =compute_effective_axis_dimension(
            UpperCAmelCase     ,      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					:   int				  =preprocessor.num_special_tokens_to_add(UpperCAmelCase		)
        lowercase					:   Union[str, Any]				  =compute_effective_axis_dimension(
            UpperCAmelCase     ,      fixed_dimension=OnnxConfig.default_fixed_sequence     ,      num_token_to_add=UpperCAmelCase		)
        # Generate dummy inputs according to compute batch and sequence
        lowercase					:   Dict				  =[''' '''.join(['''a''']		) * seq_length] * batch_size
        lowercase					:   List[Any]				  =dict(preprocessor(UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)		)
        lowercase					:   str				  =inputs.pop('''input_ids'''		)
        return inputs
       elif isinstance(UpperCAmelCase     ,      UpperCAmelCase		) 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					:   Union[str, Any]				  =compute_effective_axis_dimension(UpperCAmelCase     ,      fixed_dimension=OnnxConfig.default_fixed_batch		)
        lowercase					:   Optional[Any]				  =self._generate_dummy_images(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
        lowercase					:   int				  =dict(preprocessor(images=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)		)
        lowercase					:   Dict				  =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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



