


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    :   int	    =					logging.get_logger(__name__)

snake_case    :   List[str]	    =					{
    '''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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'perceiver'




 def __init__(		self ,  _lowerCamelCase=256 ,  _lowerCamelCase=1280 ,  _lowerCamelCase=768 ,  _lowerCamelCase=1 ,  _lowerCamelCase=26 ,  _lowerCamelCase=8 ,  _lowerCamelCase=8 ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase="kv" ,  _lowerCamelCase=1 ,  _lowerCamelCase=1 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=True ,  _lowerCamelCase=262 ,  _lowerCamelCase=2048 ,  _lowerCamelCase=56 ,  _lowerCamelCase=[368, 496] ,  _lowerCamelCase=16 ,  _lowerCamelCase=1920 ,  _lowerCamelCase=16 ,  _lowerCamelCase=[1, 16, 224, 224] ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

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







class 		_snake_case  (				_snake_case							):




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.task == "multiple-choice":
             a						:Optional[Any]										=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             a						:Dict										=	{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''inputs''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = -1 ,  _lowerCamelCase = -1 ,  _lowerCamelCase = -1 ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = 3 ,  _lowerCamelCase = 40 ,  _lowerCamelCase = 40 ,  ):
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

snake_case    :   str	    =					logging.get_logger(__name__)







@add_end_docstrings(_snake_case							)
class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       super().__init__(*_lowerCamelCase ,  **_lowerCamelCase   )
       requires_backends(self ,  '''vision'''   )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None   ):
       a						:Tuple										=	{}
       if top_k is not None:
             a						:int										=	top_k
       return {}, {}, postprocess_params




 def __call__(		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       return super().__call__(_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	load_image(_lowerCamelCase   )
       a						:Any										=	self.image_processor(images=_lowerCamelCase ,  return_tensors=self.framework   )
       return model_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.model(**_lowerCamelCase   )
       return model_outputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=5   ):
       if top_k > self.model.config.num_labels:
             a						:List[Any]										=	self.model.config.num_labels

       if self.framework == "pt":
             a						:int										=	model_outputs.logits.softmax(-1   )[0]
             a							,    a						:Union[str, Any]										=	probs.topk(_lowerCamelCase   )
       elif self.framework == "tf":
             a						:Optional[Any]										=	stable_softmax(model_outputs.logits ,  axis=-1   )[0]
             a						:Union[str, Any]										=	tf.math.top_k(_lowerCamelCase ,  k=_lowerCamelCase   )
             a							,    a						:Optional[int]										=	topk.values.numpy(), topk.indices.numpy()
       else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''   )

       a						:Optional[int]										=	scores.tolist()
       a						:str										=	ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase ,  _lowerCamelCase   )]





