




'''simple docstring'''



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
     import tensorflow as tf

     from ..tf_utils import stable_softmax


if is_torch_available():
     import torch


UpperCAmelCase_							:	Union[str, Any]	     =		logging.get_logger(__name__)





@add_end_docstrings(
    __lowerCAmelCase					, r'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''					, )
class      lowerCAmelCase (			__lowerCAmelCase):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	np.ndarray:



            '''simple docstring'''




            if self.framework == "tf":
                 __snake_case            =	tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()
            elif self.framework == "pt":
                 __snake_case            =	torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,	as_tuple=__SCREAMING_SNAKE_CASE	)
            else:
                 raise ValueError('''Unsupported framework'''	)
            return masked_index


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	np.ndarray:



            '''simple docstring'''




            __snake_case            =	self.get_masked_index(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	np.prod(masked_index.shape	)
            if numel < 1:
                 raise PipelineException(
                     '''fill-mask'''     ,	self.model.base_model_prefix     ,	F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''     ,	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 for model_input in model_inputs:
                      self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]	)
            else:
                 for input_ids in model_inputs["input_ids"]:
                      self._ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	Dict[str, GenericTensor]:



            '''simple docstring'''




            if return_tensors is None:
                 __snake_case            =	self.framework
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=__SCREAMING_SNAKE_CASE	)
            self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE	)
            return model_inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model_inputs['''input_ids''']
            return model_outputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[Any]:



            '''simple docstring'''




            if target_ids is not None and target_ids.shape[0] < top_k:
                 __snake_case            =	target_ids.shape[0]
            __snake_case            =	model_outputs['''input_ids'''][0]
            __snake_case            =	model_outputs['''logits''']

            if self.framework == "tf":
                 __snake_case            =	tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()[:, 0]

                 __snake_case            =	outputs.numpy()

                 __snake_case            =	outputs[0, masked_index, :]
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 if target_ids is not None:
                      __snake_case            =	tf.gather_nd(tf.squeeze(__SCREAMING_SNAKE_CASE     ,	0	)     ,	target_ids.reshape(-1     ,	1	)	)
                      __snake_case            =	tf.expand_dims(__SCREAMING_SNAKE_CASE     ,	0	)

                 __snake_case            =	tf.math.top_k(__SCREAMING_SNAKE_CASE     ,	k=__SCREAMING_SNAKE_CASE	)
                 __snake_case       ,     __snake_case            =	topk.values.numpy(), topk.indices.numpy()
            else:
                 __snake_case            =	torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,	as_tuple=__SCREAMING_SNAKE_CASE	).squeeze(-1	)
                 # Fill mask pipeline supports only one ${mask_token} per sample

                 __snake_case            =	outputs[0, masked_index, :]
                 __snake_case            =	logits.softmax(dim=-1	)
                 if target_ids is not None:
                      __snake_case            =	probs[..., target_ids]

                 __snake_case       ,     __snake_case            =	probs.topk(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	[]
            __snake_case            =	values.shape[0] == 1
            for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,	predictions.tolist()	)	):
                 __snake_case            =	[]
                 for v, p in zip(_values     ,	_predictions	):
                      # Copy is important since we're going to modify this array in place
                      __snake_case            =	input_ids.numpy().copy()
                      if target_ids is not None:
                           __snake_case            =	target_ids[p].tolist()

                      __snake_case            =	p
                      # Filter padding out:
                      __snake_case            =	tokens[np.where(tokens != self.tokenizer.pad_token_id	)]
                      # Originally we skip special tokens to give readable output.
                      # For multi masks though, the other [MASK] would be removed otherwise
                      # making the output look odd, so we add them back
                      __snake_case            =	self.tokenizer.decode(__SCREAMING_SNAKE_CASE     ,	skip_special_tokens=__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]	), '''sequence''': sequence}
                      row.append(__SCREAMING_SNAKE_CASE	)
                 result.append(__SCREAMING_SNAKE_CASE	)
            if single_mask:
                 return result[0]
            return result


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Any:



            '''simple docstring'''




            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	[targets]
            try:
                 __snake_case            =	self.tokenizer.get_vocab()
            except Exception:
                 __snake_case            =	{}
            __snake_case            =	[]
            for target in targets:
                 __snake_case            =	vocab.get(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 if id_ is None:
                      __snake_case            =	self.tokenizer(
                          __SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	return_token_type_ids=__SCREAMING_SNAKE_CASE     ,	max_length=1     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)['''input_ids''']
                      if len(__SCREAMING_SNAKE_CASE	) == 0:
                           logger.warning(
                               F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                               '''We cannot replace it with anything meaningful, ignoring it'''	)
                           continue
                      __snake_case            =	input_ids[0]
                      # XXX: If users encounter this pass
                      # it becomes pretty slow, so let's make sure
                      # The warning enables them to fix the input to
                      # get faster performance.
                      logger.warning(
                          F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                          F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_	)}`.'''	)
                 target_ids.append(id_	)
            __snake_case            =	list(set(__SCREAMING_SNAKE_CASE	)	)
            if len(__SCREAMING_SNAKE_CASE	) == 0:
                 raise ValueError('''At least one target must be provided when passed.'''	)
            __snake_case            =	np.array(__SCREAMING_SNAKE_CASE	)
            return target_ids


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	{}

            if targets is not None:
                 __snake_case            =	self.get_target_ids(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	target_ids

            if top_k is not None:
                 __snake_case            =	top_k

            if self.tokenizer.mask_token_id is None:
                 raise PipelineException(
                     '''fill-mask'''     ,	self.model.base_model_prefix     ,	'''The tokenizer does not define a `mask_token`.'''	)
            return {}, {}, postprocess_params




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



            '''simple docstring'''




            __snake_case            =	super().__call__(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) == 1:
                 return outputs[0]
            return outputs












'''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






