


'''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   __lowerCAmelCase						:





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							BlenderbotSmallConfig
      lowerCAmelCase__					:     List[Any]  					=							{}
      lowerCAmelCase__					:     List[Any]  					=							"""gelu"""



      def __init__(self    :    Union[str, Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    List[Any]=13   ,						UpperCamelCase    :    int=7   ,						UpperCamelCase    :    int=True   ,						UpperCamelCase    :    str=False   ,						UpperCamelCase    :    str=99   ,						UpperCamelCase    :    Optional[Any]=32   ,						UpperCamelCase    :    Any=2   ,						UpperCamelCase    :    Union[str, Any]=4   ,						UpperCamelCase    :    List[Any]=37   ,						UpperCamelCase    :    Union[str, Any]=0.1   ,						UpperCamelCase    :    List[Any]=0.1   ,						UpperCamelCase    :    List[str]=20   ,						UpperCamelCase    :    Optional[int]=2   ,						UpperCamelCase    :    int=1   ,						UpperCamelCase    :    Tuple=0   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= seq_length
             lowercase__      			= is_training
             lowercase__      			= use_labels
             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= intermediate_size

             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= max_position_embeddings
             lowercase__      			= eos_token_id
             lowercase__      			= pad_token_id
             lowercase__      			= bos_token_id



      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.seq_length - 1]   ,						self.vocab_size )
             lowercase__      			= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )   ,						1 )
             lowercase__      			= tf.concat([input_ids, eos_tensor]   ,						axis=1 )

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

             lowercase__      			= 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__      			= prepare_blenderbot_small_inputs_dict(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
             return config, inputs_dict






      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    Dict ):

             '''simple docstring'''







             lowercase__      			= TFBlenderbotSmallModel(config=UpperCamelCase ).get_decoder()
             lowercase__      			= inputs_dict['''input_ids''']

             lowercase__      			= input_ids[:1, :]
             lowercase__      			= inputs_dict['''attention_mask'''][:1, :]
             lowercase__      			= inputs_dict['''head_mask''']
             lowercase__      			= 1

             # first forward pass
             lowercase__      			= model(UpperCamelCase   ,						attention_mask=UpperCamelCase   ,						head_mask=UpperCamelCase   ,						use_cache=UpperCamelCase )

             lowercase__			,lowercase__      			= outputs.to_tuple()

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

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

             lowercase__      			= model(UpperCamelCase   ,						attention_mask=UpperCamelCase )[0]
             lowercase__      			= 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__      			= int(ids_tensor((1,)   ,						output_from_past.shape[-1] ) )
             lowercase__      			= output_from_no_past[:, -3:, random_slice_idx]
             lowercase__      			= 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      (A  , A  , A  , A=None  , A=None  , A=None  , A=None  , A=None  , )       ->       List[Any]:






       """simple docstring"""







       if attention_mask is None:
              lowercase__      			= tf.cast(tf.math.not_equal(A  , config.pad_token_id       )  , tf.inta       )
       if decoder_attention_mask is None:
              lowercase__      			= 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__      			= tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              lowercase__      			= tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              lowercase__      			= 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   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






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



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= TFBlenderbotSmallModelTester(self )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase )



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             self.config_tester.run_common_tests()






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase )






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





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							[
          """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?"""
      ]
      lowerCAmelCase__					:     Dict  					=							"""facebook/blenderbot_small-90M"""



      @cached_property
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







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



      @cached_property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
             return model






      @slow
      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.tokenizer(self.src_text   ,						return_tensors='''tf''' )
             lowercase__      			= self.model.generate(
                 model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2   ,						use_cache=UpperCamelCase   ,						)
             lowercase__      			= 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'''

from ....utils import logging


lowerCamelCase							:   Optional[Any]  						=	logging.get_logger(__name__)
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    int=2048 ):

             '''simple docstring'''







             lowercase__      			= config.__dict__
             lowercase__      			= modal_hidden_size
             if num_labels:
                    lowercase__      			= num_labels




