
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      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__	):






				@register_to_config
				def __init__(						self					:      str    ,     a					:      int    ,     a					:      int    ,     a					:      int    ,     a					:      float    ,     a					:      int    ,     a					:      int    ,     a					:      int    ,     a					:      int    ,     a					:      str    ,     a					:      bool = False    ,     ):






							'''simple docstring'''







							super().__init__()

							lowerCAmelCase__      :      Dict         =		nn.Embedding(a    ,     a   )

							lowerCAmelCase__      :      Dict         =		nn.Embedding(a    ,     a   )
							lowerCAmelCase__      :      Dict         =		False

							lowerCAmelCase__      :      Union[str, Any]         =		nn.Dropout(p=a   )

							lowerCAmelCase__      :      Any         =		TaConfig(
							    vocab_size=a    ,     d_model=a    ,     num_heads=a    ,     d_kv=a    ,     d_ff=a    ,     dropout_rate=a    ,     feed_forward_proj=a    ,     is_decoder=a    ,     is_encoder_decoder=a    ,     )

							lowerCAmelCase__      :      str         =		nn.ModuleList()
							for lyr_num in range(a   ):
										lowerCAmelCase__      :      List[Any]         =		TaBlock(a   )
										self.encoders.append(a   )

							lowerCAmelCase__      :      Tuple         =		TaLayerNorm(a   )
							lowerCAmelCase__      :      Dict         =		nn.Dropout(p=a   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.token_embedder(a   )

							lowerCAmelCase__      :      List[str]         =		encoder_input_tokens.shape[1]
							lowerCAmelCase__      :      Any         =		torch.arange(a    ,     device=encoder_input_tokens.device   )
							x += self.position_encoding(a   )

							lowerCAmelCase__      :      int         =		self.dropout_pre(a   )

							# inverted the attention mask
							lowerCAmelCase__      :      str         =		encoder_input_tokens.size()
							lowerCAmelCase__      :      Dict         =		self.get_extended_attention_mask(a    ,     a   )

							for lyr in self.encoders:
										lowerCAmelCase__      :      Union[str, Any]         =		lyr(a    ,     a   )[0]
							lowerCAmelCase__      :      List[Any]         =		self.layer_norm(a   )

							return self.dropout_post(a   ), encoder_inputs_mask

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

lowerCamelCase__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''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 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






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






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

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

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