"""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 		__A							:
     _UpperCamelCase							:    List[str]	      =	BlenderbotSmallConfig
     _UpperCamelCase							:    str	      =	{}
     _UpperCamelCase							:    Dict	      =	"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       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Dict						    =  batch_size
       _lowerCAmelCase		: Any						    =  seq_length
       _lowerCAmelCase		: List[str]						    =  is_training
       _lowerCAmelCase		: Tuple						    =  use_labels
       _lowerCAmelCase		: List[Any]						    =  vocab_size
       _lowerCAmelCase		: str						    =  hidden_size
       _lowerCAmelCase		: Any						    =  num_hidden_layers
       _lowerCAmelCase		: Optional[int]						    =  num_attention_heads
       _lowerCAmelCase		: Any						    =  intermediate_size

       _lowerCAmelCase		: List[str]						    =  hidden_dropout_prob
       _lowerCAmelCase		: Optional[Any]						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: Any						    =  max_position_embeddings
       _lowerCAmelCase		: Optional[int]						    =  eos_token_id
       _lowerCAmelCase		: str						    =  pad_token_id
       _lowerCAmelCase		: int						    =  bos_token_id






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

       _lowerCAmelCase		: List[Any]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

       _lowerCAmelCase		: Tuple						    =  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		: Dict						    =  prepare_blenderbot_small_inputs_dict(a__       ,  a__       ,  a__						)
       return config, inputs_dict






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  TFBlenderbotSmallModel(config=a__						).get_decoder()
       _lowerCAmelCase		: List[str]						    =  inputs_dict["""input_ids"""]

       _lowerCAmelCase		: List[Any]						    =  input_ids[:1, :]
       _lowerCAmelCase		: Union[str, Any]						    =  inputs_dict["""attention_mask"""][:1, :]
       _lowerCAmelCase		: str						    =  inputs_dict["""head_mask"""]
       _lowerCAmelCase		: List[Any]						    =  1

       # first forward pass
       _lowerCAmelCase		: str						    =  model(a__       ,  attention_mask=a__       ,  head_mask=a__       ,  use_cache=a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  outputs.to_tuple()

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

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

       _lowerCAmelCase		: Any						    =  model(a__       ,  attention_mask=a__						)[0]
       _lowerCAmelCase		: Tuple						    =  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		: str						    =  int(ids_tensor((1,)       ,  output_from_past.shape[-1]						)						)
       _lowerCAmelCase		: str						    =  output_from_no_past[:, -3:, random_slice_idx]
       _lowerCAmelCase		: Union[str, Any]						    =  output_from_past[:, :, random_slice_idx]

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







def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any=None    ,_lowerCamelCase		: Tuple=None    ,_lowerCamelCase		: Tuple=None    ,_lowerCamelCase		: List[str]=None    ,_lowerCamelCase		: Tuple=None    ,) ->   Optional[int]:
  if attention_mask is None:
    _lowerCAmelCase		: int						    =  tf.cast(tf.math.not_equal(_lowerCamelCase    ,config.pad_token_id			)    ,tf.inta			)
  if decoder_attention_mask is None:
    _lowerCAmelCase		: Tuple						    =  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		: Tuple						    =  tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
  if decoder_head_mask is None:
    _lowerCAmelCase		: str						    =  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							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     _UpperCamelCase							:    Dict	      =	(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     _UpperCamelCase							:    Optional[Any]	      =	(
         {
             "conversational": TFBlenderbotSmallForConditionalGeneration,
             "feature-extraction": TFBlenderbotSmallModel,
             "summarization": TFBlenderbotSmallForConditionalGeneration,
             "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
             "translation": TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     _UpperCamelCase							:    List[str]	      =	True
     _UpperCamelCase							:    List[Any]	      =	False
     _UpperCamelCase							:    Optional[int]	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  TFBlenderbotSmallModelTester(self						)
       _lowerCAmelCase		: int						    =  ConfigTester(self       ,  config_class=a__						)






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






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  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	):
     _UpperCamelCase							:    List[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?"
     ]
     _UpperCamelCase							:    Union[str, Any]	      =	"facebook/blenderbot_small-90M"






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






     @cached_property
     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
       return model






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.tokenizer(self.src_text       ,  return_tensors="""tf"""						)
       _lowerCAmelCase		: Tuple						    =  self.model.generate(
           model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2       ,  use_cache=a__       ,  )
       _lowerCAmelCase		: str						    =  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.",
       )





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




