




'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase_							:	List[Any]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Any	     =		{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[Any]										=      '''beit'''


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=8192     ,	__SCREAMING_SNAKE_CASE=768     ,	__SCREAMING_SNAKE_CASE=12     ,	__SCREAMING_SNAKE_CASE=12     ,	__SCREAMING_SNAKE_CASE=3072     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-12     ,	__SCREAMING_SNAKE_CASE=224     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=[3, 5, 7, 11]     ,	__SCREAMING_SNAKE_CASE=[1, 2, 3, 6]     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0.4     ,	__SCREAMING_SNAKE_CASE=256     ,	__SCREAMING_SNAKE_CASE=1     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=255     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	str:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	initializer_range
            __snake_case            =	layer_norm_eps

            __snake_case            =	image_size
            __snake_case            =	patch_size
            __snake_case            =	num_channels
            __snake_case            =	use_mask_token
            __snake_case            =	use_absolute_position_embeddings
            __snake_case            =	use_relative_position_bias
            __snake_case            =	use_shared_relative_position_bias
            __snake_case            =	layer_scale_init_value
            __snake_case            =	drop_path_rate
            __snake_case            =	use_mean_pooling
            # decode head attributes (semantic segmentation)
            __snake_case            =	out_indices
            __snake_case            =	pool_scales
            # auxiliary head attributes (semantic segmentation)
            __snake_case            =	use_auxiliary_head
            __snake_case            =	auxiliary_loss_weight
            __snake_case            =	auxiliary_channels
            __snake_case            =	auxiliary_num_convs
            __snake_case            =	auxiliary_concat_input
            __snake_case            =	semantic_loss_ignore_index





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[int]										=      version.parse('''1.11''')


       @property
       def    lowerCAmelCase   ( self	)   ->	Mapping[str, Mapping[int, str]]:



            '''simple docstring'''




            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ]	)




       @property
       def    lowerCAmelCase   ( self	)   ->	float:



            '''simple docstring'''




            return 1E-4












'''simple docstring'''



from collections import UserDict
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_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase)
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            requires_backends(self     ,	'''vision'''	)
            self.check_model_type(
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == '''tf'''
                else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	{}
            if "candidate_labels" in kwargs:
                 __snake_case            =	kwargs['''candidate_labels''']
            if "hypothesis_template" in kwargs:
                 __snake_case            =	kwargs['''hypothesis_template''']

            return preprocess_params, {}, {}


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="This is a photo of {}."	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	load_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.image_processor(images=[image]     ,	return_tensors=self.framework	)
            __snake_case            =	candidate_labels
            __snake_case            =	[hypothesis_template.format(__SCREAMING_SNAKE_CASE	) for x in candidate_labels]
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=self.framework     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[text_inputs]
            return inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	model_inputs.pop('''candidate_labels'''	)
            __snake_case            =	model_inputs.pop('''text_inputs'''	)
            if isinstance(text_inputs[0]     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	text_inputs[0]
            else:
                 # Batching case.
                 __snake_case            =	text_inputs[0][0]

            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	{
                '''candidate_labels''': candidate_labels,
                '''logits''': outputs.logits_per_image,
            }
            return model_outputs




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	model_outputs.pop('''candidate_labels'''	)
            __snake_case            =	model_outputs['''logits'''][0]
            if self.framework == "pt":
                 __snake_case            =	logits.softmax(dim=-1	).squeeze(-1	)
                 __snake_case            =	probs.tolist()
                 if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	[scores]
            elif self.framework == "tf":
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 __snake_case            =	probs.numpy().tolist()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''	)

            __snake_case            =	[
                {'''score''': score, '''label''': candidate_label}
                for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)     ,	key=lambda __SCREAMING_SNAKE_CASE	: -x[0]	)
            ]
            return result






