"""simple docstring"""






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     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = BlenderbotSmallConfig
    _UpperCAmelCase	:Any						    = {}
    _UpperCAmelCase	:Tuple						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0    ,			):
       lowercase__:   Any					=      parent
       lowercase__:   Optional[int]					=      batch_size
       lowercase__:   str					=      seq_length
       lowercase__:   Union[str, Any]					=      is_training
       lowercase__:   Tuple					=      use_labels
       lowercase__:   Optional[Any]					=      vocab_size
       lowercase__:   List[Any]					=      hidden_size
       lowercase__:   List[str]					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   Tuple					=      intermediate_size

       lowercase__:   List[Any]					=      hidden_dropout_prob
       lowercase__:   List[str]					=      attention_probs_dropout_prob
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      eos_token_id
       lowercase__:   Optional[int]					=      pad_token_id
       lowercase__:   int					=      bos_token_id






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size							)
       lowercase__:   Tuple					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,			1							)
       lowercase__:   Optional[Any]					=      tf.concat([input_ids, eos_tensor]    ,			axis=1							)

       lowercase__:   Optional[Any]					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   int					=      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    ,			)
       lowercase__:   Tuple					=      prepare_blenderbot_small_inputs_dict(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      TFBlenderbotSmallModel(config=_UpperCAmelCase							).get_decoder()
       lowercase__:   Dict					=      inputs_dict['''input_ids''']

       lowercase__:   Any					=      input_ids[:1, :]
       lowercase__:   Dict					=      inputs_dict['''attention_mask'''][:1, :]
       lowercase__:   List[Any]					=      inputs_dict['''head_mask''']
       lowercase__:   Union[str, Any]					=      1

       # first forward pass
       lowercase__:   Tuple					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			use_cache=_UpperCAmelCase							)

       lowercase__, lowercase__:   Any					=      outputs.to_tuple()

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

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

       lowercase__:   Optional[int]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase							)[0]
       lowercase__:   int					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			past_key_values=_UpperCAmelCase							)[0]

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

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

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  List[str]:
   if attention_mask is None:
      lowercase__:   Union[str, Any]					=      tf.cast(tf.math.not_equal(__UpperCAmelCase       ,    config.pad_token_id )       ,    tf.inta )
   if decoder_attention_mask is None:
      lowercase__:   int					=      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:
      lowercase__:   Optional[int]					=      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase__:   List[Any]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase__:   Optional[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     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Tuple						    = (
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    _UpperCAmelCase	:Optional[int]						    = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    _UpperCAmelCase	:List[Any]						    = (
        {
            "conversational": TFBlenderbotSmallForConditionalGeneration,
            "feature-extraction": TFBlenderbotSmallModel,
            "summarization": TFBlenderbotSmallForConditionalGeneration,
            "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
            "translation": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _UpperCAmelCase	:List[Any]						    = True
    _UpperCAmelCase	:int						    = False
    _UpperCAmelCase	:Union[str, Any]						    = False






    def _snake_case			(   self							):
       lowercase__:   Any					=      TFBlenderbotSmallModelTester(self							)
       lowercase__:   List[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)



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



    """simple docstring"""

    _UpperCAmelCase	:int						    = [
        "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	:str						    = "facebook/blenderbot_small-90M"






    @cached_property
    def _snake_case			(   self							):
       # use "old" tokenizer here because of bug when downloading new tokenizer
       return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''							)






    @cached_property
    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model






    @slow
    def _snake_case			(   self							):
       lowercase__:   Tuple					=      self.tokenizer(self.src_text    ,			return_tensors='''tf'''							)
       lowercase__:   List[Any]					=      self.model.generate(
           model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2    ,			use_cache=_UpperCAmelCase    ,			)
       lowercase__:   Optional[int]					=      self.tokenizer.batch_decode(generated_ids.numpy()    ,			skip_special_tokens=_UpperCAmelCase							)[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.",
       )

"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A			   = logging.get_logger(__name__)

__A			   = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A			   = {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, Any]					=      [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							) * [0] + len(token_ids_a + sep							) * [1]






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
