


import tensorflow as tf

from ...tf_utils import shape_list






class     UpperCAmelCase__       (					tf.keras.layers.Layer		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_					,   A_					,   A_=1					,   A_=False					,   **A_    )  ->       Union[str, Any]:
         super().__init__(**A_    )

         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=d_embed
         __UpperCamelCase    							=d_proj

         __UpperCamelCase    							=cutoffs + [vocab_size]
         __UpperCamelCase    							=[0] + self.cutoffs
         __UpperCamelCase    							=div_val

         __UpperCamelCase    							=self.cutoffs[0]
         __UpperCamelCase    							=len(self.cutoffs    ) - 1
         __UpperCamelCase    							=self.shortlist_size + self.n_clusters
         __UpperCamelCase    							=keep_order

         __UpperCamelCase    							=[]
         __UpperCamelCase    							=[]






    def _a					(			self					,   A_    )  ->       List[str]:
         if self.n_clusters > 0:
              __UpperCamelCase    							=self.add_weight(
                  shape=(self.n_clusters, self.d_embed)					,   initializer='zeros'					,   trainable=A_					,   name='cluster_weight'    )
              __UpperCamelCase    							=self.add_weight(
                  shape=(self.n_clusters,)					,   initializer='zeros'					,   trainable=A_					,   name='cluster_bias'    )

         if self.div_val == 1:
              for i in range(len(self.cutoffs    )    ):
                   if self.d_proj != self.d_embed:
                        __UpperCamelCase    							=self.add_weight(
                            shape=(self.d_embed, self.d_proj)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_projs_._{i}'					,   )
                        self.out_projs.append(A_    )
                   else:
                        self.out_projs.append(A_    )
                   __UpperCamelCase    							=self.add_weight(
                       shape=(self.vocab_size, self.d_embed)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_layers_._{i}_._weight'					,   )
                   __UpperCamelCase    							=self.add_weight(
                       shape=(self.vocab_size,)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_layers_._{i}_._bias'					,   )
                   self.out_layers.append((weight, bias)    )
         else:
              for i in range(len(self.cutoffs    )    ):
                   __UpperCamelCase							,      __UpperCamelCase    							=self.cutoff_ends[i], self.cutoff_ends[i + 1]
                   __UpperCamelCase    							=self.d_embed // (self.div_val**i)

                   __UpperCamelCase    							=self.add_weight(
                       shape=(d_emb_i, self.d_proj)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_projs_._{i}'    )
                   self.out_projs.append(A_    )
                   __UpperCamelCase    							=self.add_weight(
                       shape=(r_idx - l_idx, d_emb_i)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_layers_._{i}_._weight'					,   )
                   __UpperCamelCase    							=self.add_weight(
                       shape=(r_idx - l_idx,)					,   initializer='zeros'					,   trainable=A_					,   name=f'out_layers_._{i}_._bias'					,   )
                   self.out_layers.append((weight, bias)    )
         super().build(A_    )






    @staticmethod
    def _a					(			A_					,   A_					,   A_					,   A_=None    )  ->       List[Any]:
         __UpperCamelCase    							=x
         if proj is not None:
              __UpperCamelCase    							=tf.einsum('ibd,ed->ibe'					,   A_					,   A_    )
         return tf.einsum('ibd,nd->ibn'					,   A_					,   A_    ) + b






    @staticmethod
    def _a					(			A_					,   A_    )  ->       Dict:
         __UpperCamelCase    							=shape_list(A_    )
         __UpperCamelCase    							=tf.range(lp_size[0]					,   dtype=target.dtype    )
         __UpperCamelCase    							=tf.stack([r, target]					,   1    )
         return tf.gather_nd(A_					,   A_    )






    def _a					(			self					,   A_					,   A_					,   A_=True					,   A_=False    )  ->       str:
         __UpperCamelCase    							=0
         if self.n_clusters == 0:
              __UpperCamelCase    							=self._logit(A_					,   self.out_layers[0][0]					,   self.out_layers[0][1]					,   self.out_projs[0]    )
              if target is not None:
                   __UpperCamelCase    							=tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A_					,   logits=A_    )
              __UpperCamelCase    							=tf.nn.log_softmax(A_					,   axis=-1    )
         else:
              __UpperCamelCase    							=shape_list(A_    )
              __UpperCamelCase    							=[]
              __UpperCamelCase    							=tf.zeros(hidden_sizes[:2]    )
              for i in range(len(self.cutoffs    )    ):
                   __UpperCamelCase							,      __UpperCamelCase    							=self.cutoff_ends[i], self.cutoff_ends[i + 1]
                   if target is not None:
                        __UpperCamelCase    							=(target >= l_idx) & (target < r_idx)
                        __UpperCamelCase    							=tf.where(A_    )
                        __UpperCamelCase    							=tf.boolean_mask(A_					,   A_    ) - l_idx

                   if self.div_val == 1:
                        __UpperCamelCase    							=self.out_layers[0][0][l_idx:r_idx]
                        __UpperCamelCase    							=self.out_layers[0][1][l_idx:r_idx]
                   else:
                        __UpperCamelCase    							=self.out_layers[i][0]
                        __UpperCamelCase    							=self.out_layers[i][1]

                   if i == 0:
                        __UpperCamelCase    							=tf.concat([cur_W, self.cluster_weight]					,   0    )
                        __UpperCamelCase    							=tf.concat([cur_b, self.cluster_bias]					,   0    )

                        __UpperCamelCase    							=self._logit(A_					,   A_					,   A_					,   self.out_projs[0]    )
                        __UpperCamelCase    							=tf.nn.log_softmax(A_    )
                        out.append(head_logprob[..., : self.cutoffs[0]]    )
                        if target is not None:
                             __UpperCamelCase    							=tf.boolean_mask(A_					,   A_    )
                             __UpperCamelCase    							=self._gather_logprob(A_					,   A_    )
                   else:
                        __UpperCamelCase    							=self._logit(A_					,   A_					,   A_					,   self.out_projs[i]    )
                        __UpperCamelCase    							=tf.nn.log_softmax(A_    )
                        __UpperCamelCase    							=self.cutoffs[0] + i - 1  # No probability for the head cluster
                        __UpperCamelCase    							=head_logprob[..., cluster_prob_idx, None] + tail_logprob
                        out.append(A_    )
                        if target is not None:
                             __UpperCamelCase    							=tf.boolean_mask(A_					,   A_    )
                             __UpperCamelCase    							=tf.boolean_mask(A_					,   A_    )
                             __UpperCamelCase    							=self._gather_logprob(A_					,   A_    )
                             cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                   if target is not None:
                        loss += tf.scatter_nd(A_					,   -cur_logprob					,   shape_list(A_    )    )
              __UpperCamelCase    							=tf.concat(A_					,   axis=-1    )

         if target is not None:
              if return_mean:
                   __UpperCamelCase    							=tf.reduce_mean(A_    )
              # Add the training-time loss value to the layer using `self.add_loss()`.
              self.add_loss(A_    )

              # Log the loss as a metric (we could log arbitrary metrics,
              # including different metrics for training and inference.
              self.add_metric(A_					,   name=self.name					,   aggregation='mean' if return_mean else ''    )

         return out










"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowercase : Tuple      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['pixel_values']



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  int = 8,  **lowerCamelCase							:  Tuple,  )->					None:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		int			=do_rescale
								lowerCamelCase__		:		Dict			=rescale_factor
								lowerCamelCase__		:		Union[str, Any]			=do_pad
								lowerCamelCase__		:		Union[str, Any]			=pad_size



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  float,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  int     )->					np.ndarray:
								return rescale(lowerCamelCase,  scale=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None     )->					List[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=get_image_size(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(old_height // size + 1) * size - old_height
								lowerCamelCase__		:		List[str]			=(old_width // size + 1) * size - old_width

								return pad(lowerCamelCase,  ((0, pad_height), (0, pad_width)),  mode='''symmetric''',  data_format=lowerCamelCase     )




					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Union[str, Any],  )->					Dict:
								lowerCamelCase__		:		List[str]			=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		Tuple			=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		str			=do_pad if do_pad is not None else self.do_pad
								lowerCamelCase__		:		int			=pad_size if pad_size is not None else self.pad_size

								lowerCamelCase__		:		Optional[int]			=make_list_of_images(lowerCamelCase     )

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

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

								# All transformations expect numpy arrays.
								lowerCamelCase__		:		Tuple			=[to_numpy_array(lowerCamelCase     ) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Tuple			=[self.rescale(image=lowerCamelCase,  scale=lowerCamelCase     ) for image in images]

								if do_pad:
											lowerCamelCase__		:		Tuple			=[self.pad(lowerCamelCase,  size=lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		int			=[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		Dict			={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase     )




