





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


_snake_case    :       List[str]		       =    logging.get_logger(__name__)



@add_end_docstrings(
    _UpperCamelCase						,       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 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						GenericTensor	)				->		np.ndarray:
     if self.framework == "tf":
        __lowerCAmelCase				      =      tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()
     elif self.framework == "pt":
        __lowerCAmelCase				      =      torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,    as_tuple=lowerCAmelCase_	)
     else:
        raise ValueError('Unsupported framework'	)
     return masked_index






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						GenericTensor	)				->		np.ndarray:
     __lowerCAmelCase				      =      self.get_masked_index(lowerCAmelCase_	)
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						GenericTensor	)				->		List[Any]:
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        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(lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    **lowerCAmelCase_					:						List[Any]	)				->		Dict[str, GenericTensor]:
     if return_tensors is None:
        __lowerCAmelCase				      =      self.framework
     __lowerCAmelCase				      =      self.tokenizer(lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_	)
     self.ensure_exactly_one_mask_token(lowerCAmelCase_	)
     return model_inputs






  def 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						str	)				->		List[Any]:
     __lowerCAmelCase				      =      self.model(**lowerCAmelCase_	)
     __lowerCAmelCase				      =      model_inputs['input_ids']
     return model_outputs






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						Union[str, Any]=5   ,    lowerCAmelCase_					:						int=None	)				->		List[Any]:
     # Cap top_k if there are targets
     if target_ids is not None and target_ids.shape[0] < top_k:
        __lowerCAmelCase				      =      target_ids.shape[0]
     __lowerCAmelCase				      =      model_outputs['input_ids'][0]
     __lowerCAmelCase				      =      model_outputs['logits']

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

        __lowerCAmelCase				      =      outputs.numpy()

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

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

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

        __lowerCAmelCase      ,      __lowerCAmelCase				      =      probs.topk(lowerCAmelCase_	)

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

           __lowerCAmelCase				      =      p
           # Filter padding out:
           __lowerCAmelCase				      =      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
           __lowerCAmelCase				      =      self.tokenizer.decode(lowerCAmelCase_   ,    skip_special_tokens=lowerCAmelCase_	)
           __lowerCAmelCase				      =      {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]	), 'sequence': sequence}
           row.append(lowerCAmelCase_	)
        result.append(lowerCAmelCase_	)
     if single_mask:
        return result[0]
     return result






  def 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[Any]=None	)				->		List[str]:
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      [targets]
     try:
        __lowerCAmelCase				      =      self.tokenizer.get_vocab()
     except Exception:
        __lowerCAmelCase				      =      {}
     __lowerCAmelCase				      =      []
     for target in targets:
        __lowerCAmelCase				      =      vocab.get(lowerCAmelCase_   ,    lowerCAmelCase_	)
        if id_ is None:
           __lowerCAmelCase				      =      self.tokenizer(
               lowerCAmelCase_   ,    add_special_tokens=lowerCAmelCase_   ,    return_attention_mask=lowerCAmelCase_   ,    return_token_type_ids=lowerCAmelCase_   ,    max_length=1   ,    truncation=lowerCAmelCase_   ,    )['input_ids']
           if len(lowerCAmelCase_	) == 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
           __lowerCAmelCase				      =      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_	)
     __lowerCAmelCase				      =      list(set(lowerCAmelCase_	)	)
     if len(lowerCAmelCase_	) == 0:
        raise ValueError('At least one target must be provided when passed.'	)
     __lowerCAmelCase				      =      np.array(lowerCAmelCase_	)
     return target_ids






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						Any=None   ,    lowerCAmelCase_					:						Any=None	)				->		Tuple:
     __lowerCAmelCase				      =      {}

     if targets is not None:
        __lowerCAmelCase				      =      self.get_target_ids(lowerCAmelCase_   ,    lowerCAmelCase_	)
        __lowerCAmelCase				      =      target_ids

     if top_k is not None:
        __lowerCAmelCase				      =      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					:						List[Any]   ,    lowerCAmelCase_					:						Optional[Any]   ,    *lowerCAmelCase_					:						Dict   ,    **lowerCAmelCase_					:						Union[str, Any]	)				->		Optional[int]:
     __lowerCAmelCase				      =      super().__call__(lowerCAmelCase_   ,    **lowerCAmelCase_	)
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) and len(lowerCAmelCase_	) == 1:
        return outputs[0]
     return outputs



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			str						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = PIL.Image.BICUBIC							,				a = True							,				a = None							,				a = 1 / 2_55							,				a = True							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""height""": 2_56, """width""": 2_56}
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size
								_UpperCamelCase         =						resample
								_UpperCamelCase         =						do_center_crop
								_UpperCamelCase         =						crop_size
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase         =						image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 			A_					(      self							,				a							,				a							,				a = PIL.Image.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return resize(
								    a							,				size=(size["""height"""], size["""width"""])							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a					)
								if "height" not in size or "width" not in size:
										raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
								return center_crop(a							,				size=(size["""height"""], size["""width"""])							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   List[str]:


								'''simple docstring'''

								return rescale(a							,				scale=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								return normalize(a							,				mean=a							,				std=a							,				data_format=a							,				**a					)







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a=None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase         =						do_resize if do_resize is not None else self.do_resize
								_UpperCamelCase         =						resample if resample is not None else self.resample
								_UpperCamelCase         =						do_center_crop if do_center_crop is not None else self.do_center_crop
								_UpperCamelCase         =						do_rescale if do_rescale is not None else self.do_rescale
								_UpperCamelCase         =						rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCamelCase         =						do_normalize if do_normalize is not None else self.do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else self.image_mean
								_UpperCamelCase         =						image_std if image_std is not None else self.image_std

								_UpperCamelCase         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a					)
								_UpperCamelCase         =						crop_size if crop_size is not None else self.crop_size
								_UpperCamelCase         =						get_size_dict(a							,				param_name="""crop_size"""					)

								_UpperCamelCase         =						make_list_of_images(a					)

								if not valid_images(a					):
										raise ValueError(
										    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
										    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

								if do_resize and size is None or resample is None:
										raise ValueError("""Size and resample must be specified if do_resize is True."""					)

								if do_center_crop and crop_size is None:
										raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

								if do_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

								if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

								# All transformations expect numpy arrays.
								_UpperCamelCase         =						[to_numpy_array(a					) for image in images]

								if do_resize:
										_UpperCamelCase         =						[self.resize(image=a							,				size=a							,				resample=a					) for image in images]

								if do_center_crop:
										_UpperCamelCase         =						[self.center_crop(image=a							,				size=a					) for image in images]

								if do_rescale:
										_UpperCamelCase         =						[self.rescale(image=a							,				scale=a					) for image in images]

								if do_normalize:
										_UpperCamelCase         =						[self.normalize(image=a							,				mean=a							,				std=a					) for image in images]

								_UpperCamelCase         =						[to_channel_dimension_format(a							,				a					) for image in images]

								_UpperCamelCase         =						{"""pixel_values""": images}
								return BatchFeature(data=a							,				tensor_type=a					)



