


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   							=						logging.get_logger(__name__)



@add_end_docstrings(_a						)
class 	__lowerCamelCase			(_a						):







       def __init__(			self: str,*A_: List[Any],**A_: Optional[int]					):






            '''simple docstring'''




            super().__init__(*A_,**A_					)
            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 		snake_case_	(			self: Dict,A_: Tuple=None					):






            '''simple docstring'''




            __UpperCamelCase =			{}
            if top_k is not None:
                 __UpperCamelCase =			top_k
            return {}, {}, postprocess_params







       def __call__(			self: List[Any],A_: Union[str, List[str], "Image.Image", List["Image.Image"]],**A_: Optional[int]					):






            '''simple docstring'''




            return super().__call__(A_,**A_					)







       def 		snake_case_	(			self: Optional[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			load_image(A_					)
            __UpperCamelCase =			self.image_processor(images=A_,return_tensors=self.framework					)
            return model_inputs







       def 		snake_case_	(			self: str,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model(**A_					)
            return model_outputs







       def 		snake_case_	(			self: Optional[Any],A_: Dict,A_: Optional[int]=5					):






            '''simple docstring'''




            if top_k > self.model.config.num_labels:
                 __UpperCamelCase =			self.model.config.num_labels

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

            __UpperCamelCase =			scores.tolist()
            __UpperCamelCase =			ids.tolist()
            return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A_,A_					)]










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


