





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {'vocab_file': 'sentencepiece.bpe.model'}

_lowerCamelCase      							=   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

_lowerCamelCase      							=   {
    'camembert-base': 512,
}

_lowerCamelCase      							=   '▁'







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__							=   ["""input_ids""", """attention_mask"""]


      def __init__(		self   ,				a__   ,				a__="<s>"   ,				a__="</s>"   ,				a__="</s>"   ,				a__="<s>"   ,				a__="<unk>"   ,				a__="<pad>"   ,				a__="<mask>"   ,				a__=["<s>NOTUSED", "</s>NOTUSED"]   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__) if isinstance(a__   ,				a__) else mask_token

             _lowerCamelCase				:  Any						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=a__   ,				eos_token=a__   ,				unk_token=a__   ,				sep_token=a__   ,				cls_token=a__   ,				pad_token=a__   ,				mask_token=a__   ,				additional_special_tokens=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)
             _lowerCamelCase				:  Dict						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(str(a__))
             _lowerCamelCase				:  Dict						=		vocab_file
             # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
             # sentencepiece vocabulary (this is the case for <s> and </s>
             _lowerCamelCase				:  Dict						=		{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
             _lowerCamelCase				:  Any						=		len(self.fairseq_tokens_to_ids)
             _lowerCamelCase				:  Dict						=		len(self.sp_model) + len(self.fairseq_tokens_to_ids)
             _lowerCamelCase				:  Optional[Any]						=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = False):



             """simple docstring"""





             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a__   ,				token_ids_a=a__   ,				already_has_special_tokens=a__)

             if token_ids_a is None:
                    return [1] + ([0] * len(a__)) + [1]
             return [1] + ([0] * len(a__)) + [1, 1] + ([0] * len(a__)) + [1]


      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		[self.sep_token_id]
             _lowerCamelCase				:  str						=		[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]


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.fairseq_tokens_to_ids) + len(self.sp_model)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		{self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
             vocab.update(self.added_tokens_encoder)
             return vocab


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.encode(a__   ,				out_type=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             elif self.sp_model.PieceToId(a__) == 0:
                    # Convert sentence piece unk token to fairseq unk token index
                    return self.unk_token_id
             return self.fairseq_offset + self.sp_model.PieceToId(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		[]
             _lowerCamelCase				:  Optional[Any]						=		''''''
             _lowerCamelCase				:  Optional[int]						=		False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(a__) + token
                           _lowerCamelCase				:  Dict						=		True
                           _lowerCamelCase				:  List[Any]						=		[]
                    else:
                           current_sub_tokens.append(a__)
                           _lowerCamelCase				:  Union[str, Any]						=		False
             out_string += self.sp_model.decode(a__)
             return out_string.strip()


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.__dict__.copy()
             _lowerCamelCase				:  List[Any]						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  Optional[Any]						=		{}

             _lowerCamelCase				:  Optional[int]						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(self.vocab_file)



      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





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

             if os.path.abspath(self.vocab_file) != os.path.abspath(a__) and os.path.isfile(self.vocab_file):
                    copyfile(self.vocab_file   ,				a__)
             elif not os.path.isfile(self.vocab_file):
                    with open(a__   ,				'''wb''') as fi:
                           _lowerCamelCase				:  List[Any]						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (out_vocab_file,)








from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   BlenderbotSmallConfig
      UpperCAmelCase__							=   {}
      UpperCAmelCase__							=   """gelu"""


      def __init__(		self   ,				a__   ,				a__=13   ,				a__=7   ,				a__=True   ,				a__=False   ,				a__=99   ,				a__=32   ,				a__=2   ,				a__=4   ,				a__=37   ,				a__=0.1   ,				a__=0.1   ,				a__=20   ,				a__=2   ,				a__=1   ,				a__=0   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		parent
             _lowerCamelCase				:  Any						=		batch_size
             _lowerCamelCase				:  str						=		seq_length
             _lowerCamelCase				:  Optional[int]						=		is_training
             _lowerCamelCase				:  int						=		use_labels
             _lowerCamelCase				:  List[str]						=		vocab_size
             _lowerCamelCase				:  List[Any]						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  int						=		intermediate_size

             _lowerCamelCase				:  int						=		hidden_dropout_prob
             _lowerCamelCase				:  Tuple						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Dict						=		max_position_embeddings
             _lowerCamelCase				:  str						=		eos_token_id
             _lowerCamelCase				:  Any						=		pad_token_id
             _lowerCamelCase				:  str						=		bos_token_id


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size)
             _lowerCamelCase				:  Any						=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)   ,				1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([input_ids, eos_tensor]   ,				axis=1)

             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size)

             _lowerCamelCase				:  Dict						=		self.config_cls(
                 vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
             _lowerCamelCase				:  Optional[int]						=		prepare_blenderbot_small_inputs_dict(a__   ,				a__   ,				a__)
             return config, inputs_dict



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		TFBlenderbotSmallModel(config=a__).get_decoder()
             _lowerCamelCase				:  Dict						=		inputs_dict['''input_ids''']

             _lowerCamelCase				:  int						=		input_ids[:1, :]
             _lowerCamelCase				:  Union[str, Any]						=		inputs_dict['''attention_mask'''][:1, :]
             _lowerCamelCase				:  List[str]						=		inputs_dict['''head_mask''']
             _lowerCamelCase				:  Optional[int]						=		1

             # first forward pass
             _lowerCamelCase				:  Union[str, Any]						=		model(a__   ,				attention_mask=a__   ,				head_mask=a__   ,				use_cache=a__)

             _lowerCamelCase,	_lowerCamelCase				:  str						=		outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _lowerCamelCase				:  Any						=		ids_tensor((self.batch_size, 3)   ,				config.vocab_size)
             _lowerCamelCase				:  Optional[Any]						=		tf.cast(ids_tensor((self.batch_size, 3)   ,				2)   ,				tf.inta)

             # append to next input_ids and
             _lowerCamelCase				:  Any						=		tf.concat([input_ids, next_tokens]   ,				axis=-1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([attention_mask, next_attn_mask]   ,				axis=-1)

             _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__)[0]
             _lowerCamelCase				:  str						=		model(a__   ,				attention_mask=a__   ,				past_key_values=a__)[0]

             self.parent.assertEqual(next_tokens.shape[1]   ,				output_from_past.shape[1])

             # select random slice
             _lowerCamelCase				:  int						=		int(ids_tensor((1,)   ,				output_from_past.shape[-1]))
             _lowerCamelCase				:  Any						=		output_from_no_past[:, -3:, random_slice_idx]
             _lowerCamelCase				:  int						=		output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(a__   ,				a__   ,				rtol=1e-3)




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     ):
       if attention_mask is None:
              _lowerCamelCase				:  List[Any]						=		tf.cast(tf.math.not_equal(lowercase_			,     config.pad_token_id   )			,     tf.inta   )
       if decoder_attention_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape			,     dtype=tf.inta   ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,     config.pad_token_id   )			,     tf.inta   ),
                  ]			,     axis=-1			,     )
       if head_mask is None:
              _lowerCamelCase				:  List[str]						=		tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _lowerCamelCase				:  Optional[int]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }







@require_tf
class       __A							(   lowerCamelCase__			,lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      UpperCAmelCase__							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      UpperCAmelCase__							=   (
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		TFBlenderbotSmallModelTester(self)
             _lowerCamelCase				:  Optional[int]						=		ConfigTester(self   ,				config_class=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.config_tester.run_common_tests()



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_tokenizers
@require_tf
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   [
          """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
          """  i'm going to throw up.\nand why is that?"""
      ]
      UpperCAmelCase__							=   """facebook/blenderbot_small-90M"""


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.tokenizer(self.src_text   ,				return_tensors='''tf''')
             _lowerCamelCase				:  str						=		self.model.generate(
                 model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=a__   ,				)
             _lowerCamelCase				:  Tuple						=		self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=a__)[0]
             assert generated_words in (
                 "i don't know. i just feel like i'm going to throw up. it's not fun.",
                 "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                 "i'm not sure. i just feel like i've been in a bad situation.",
             )

