





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_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			str  			=	'''perceiver'''







       def __init__(     self						:						Optional[Any]    ,__A						:						int=256    ,__A						:						List[Any]=1280    ,__A						:						List[Any]=768    ,__A						:						List[str]=1    ,__A						:						Dict=26    ,__A						:						Union[str, Any]=8    ,__A						:						Union[str, Any]=8    ,__A						:						List[Any]=None    ,__A						:						str=None    ,__A						:						str="kv"    ,__A						:						Union[str, Any]=1    ,__A						:						Any=1    ,__A						:						Any="gelu"    ,__A						:						Optional[Any]=0.1    ,__A						:						Dict=0.02    ,__A						:						Union[str, Any]=1e-12    ,__A						:						List[Any]=True    ,__A						:						List[Any]=262    ,__A						:						Any=2048    ,__A						:						str=56    ,__A						:						Tuple=[368, 496]    ,__A						:						Union[str, Any]=16    ,__A						:						List[str]=1920    ,__A						:						List[Any]=16    ,__A						:						str=[1, 16, 224, 224]    ,**__A						:						Tuple    ,)    ->       int:
         super().__init__(**__A	)

         _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_					(   UpperCAmelCase						):







       """simple docstring"""





       @property
       def  __UpperCAmelCase			(     self						:						str	)    ->       Mapping[str, Mapping[int, str]]:
         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  __UpperCAmelCase			(     self						:						int	)    ->       float:
         return 1e-4







       def  __UpperCAmelCase			(     self						:						Union[str, Any]    ,__A						:						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,__A						:						int = -1    ,__A						:						int = -1    ,__A						:						int = -1    ,__A						:						bool = False    ,__A						:						Optional[TensorType] = None    ,__A						:						int = 3    ,__A						:						int = 40    ,__A						:						int = 40    ,)    ->       Mapping[str, Any]:
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, TensorType

__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"imagegpt"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	List[Any]=5_1_2 + 1			,       __lowerCamelCase							:	Dict=3_2 * 3_2			,       __lowerCamelCase							:	List[str]=5_1_2			,       __lowerCamelCase							:	List[Any]=2_4			,       __lowerCamelCase							:	Any=8			,       __lowerCamelCase							:	Tuple=None			,       __lowerCamelCase							:	Any="quick_gelu"			,       __lowerCamelCase							:	Optional[int]=0.1			,       __lowerCamelCase							:	str=0.1			,       __lowerCamelCase							:	int=0.1			,       __lowerCamelCase							:	Tuple=1e-5			,       __lowerCamelCase							:	Dict=0.02			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	str=True			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Dict=False			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__	:Dict      =      vocab_size
    UpperCAmelCase__	:str      =      n_positions
    UpperCAmelCase__	:Tuple      =      n_embd
    UpperCAmelCase__	:Dict      =      n_layer
    UpperCAmelCase__	:List[Any]      =      n_head
    UpperCAmelCase__	:str      =      n_inner
    UpperCAmelCase__	:Optional[Any]      =      activation_function
    UpperCAmelCase__	:str      =      resid_pdrop
    UpperCAmelCase__	:Optional[Any]      =      embd_pdrop
    UpperCAmelCase__	:Tuple      =      attn_pdrop
    UpperCAmelCase__	:int      =      layer_norm_epsilon
    UpperCAmelCase__	:List[Any]      =      initializer_range
    UpperCAmelCase__	:List[Any]      =      scale_attn_weights
    UpperCAmelCase__	:List[str]      =      use_cache
    UpperCAmelCase__	:Tuple      =      scale_attn_by_inverse_layer_idx
    UpperCAmelCase__	:Union[str, Any]      =      reorder_and_upcast_attn
    UpperCAmelCase__	:List[Any]      =      tie_word_embeddings

    super().__init__(tie_word_embeddings=__lowerCamelCase			,       **__lowerCamelCase )




class 				UpperCAmelCase			(			_snake_case						):


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
        ] )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	"FeatureExtractionMixin"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	int = -1			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional["TensorType"] = None			,       __lowerCamelCase							:	int = 3			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       ):
    UpperCAmelCase__	:Tuple      =      self._generate_dummy_images(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      dict(preprocessor(images=__lowerCamelCase			,       return_tensors=__lowerCamelCase ) )

    return inputs



