
import tensorflow as tf

from ...tf_utils import shape_list





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







						'''simple docstring'''



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

										lowercase         =      vocab_size
										lowercase         =      d_embed
										lowercase         =      d_proj

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

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

										lowercase         =      []
										lowercase         =      []





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Tuple     )  ->Optional[Any]:
										if self.n_clusters > 0:
														lowercase         =      self.add_weight(
														    shape=(self.n_clusters, self.d_embed)		,    initializer="zeros"		,    trainable=lowerCAmelCase__		,    name="cluster_weight"     )
														lowercase         =      self.add_weight(
														    shape=(self.n_clusters,)		,    initializer="zeros"		,    trainable=lowerCAmelCase__		,    name="cluster_bias"     )

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

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





						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Tuple=None     )  ->List[Any]:
										lowercase         =      x
										if proj is not None:
														lowercase         =      tf.einsum("ibd,ed->ibe"		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										return tf.einsum("ibd,nd->ibn"		,    lowerCAmelCase__		,    lowerCAmelCase__     ) + b





						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:str     )  ->List[str]:
										lowercase         =      shape_list(lowerCAmelCase__     )
										lowercase         =      tf.range(lp_size[0]		,    dtype=target.dtype     )
										lowercase         =      tf.stack([r, target]		,    1     )
										return tf.gather_nd(lowerCAmelCase__		,    lowerCAmelCase__     )





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

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

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

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

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

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

										return out


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

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

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

										super().__init__(
										    lowerCAmelCase__		,    tokenizer_file=lowerCAmelCase__		,    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowercase         =      [self.cls_token_id]
										lowercase         =      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep     ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
														    "tokenizer."     )

										if not os.path.isdir(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCAmelCase__     ):
														copyfile(self.vocab_file		,    lowerCAmelCase__     )

										return (out_vocab_file,)
