




'''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 			_lowercase						:
     _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    ,   ) ->	List[str]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			seq_length
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			vocab_size
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size

      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			max_position_embeddings
      snake_case				=			eos_token_id
      snake_case				=			pad_token_id
      snake_case				=			bos_token_id







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size    )
      snake_case				=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )    ,   1    )
      snake_case				=			tf.concat([input_ids, eos_tensor]    ,   axis=1    )

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

      snake_case				=			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    ,   )
      snake_case				=			prepare_blenderbot_small_inputs_dict(lowercase__    ,   lowercase__    ,   lowercase__    )
      return config, inputs_dict







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	int:
      snake_case				=			TFBlenderbotSmallModel(config=lowercase__    ).get_decoder()
      snake_case				=			inputs_dict["""input_ids"""]

      snake_case				=			input_ids[:1, :]
      snake_case				=			inputs_dict["""attention_mask"""][:1, :]
      snake_case				=			inputs_dict["""head_mask"""]
      snake_case				=			1

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

      snake_case				=			outputs.to_tuple()

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

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

      snake_case				=			model(lowercase__    ,   attention_mask=lowercase__    )[0]
      snake_case				=			model(lowercase__    ,   attention_mask=lowercase__    ,   past_key_values=lowercase__    )[0]

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

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

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




def    __UpperCamelCase	(	a    : Any					,   a    : int					,   a    : str					,   a    : Tuple=None					,   a    : Optional[int]=None					,   a    : Union[str, Any]=None					,   a    : Union[str, Any]=None					,   a    : int=None					,   ) ->Optional[Any]:





 if attention_mask is None:
  snake_case				=			tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__					,   config.pad_token_id     )					,   tf.inta     )
 if decoder_attention_mask is None:
  snake_case				=			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:
  snake_case				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
 if decoder_head_mask is None:
  snake_case				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
 if cross_attn_head_mask is None:
  snake_case				=			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 			_lowercase						(       _UpperCAmelCase				,		_UpperCAmelCase				,		unittest.TestCase					):
     _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     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			TFBlenderbotSmallModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=lowercase__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      self.config_tester.run_common_tests()







     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowercase__    )




@require_tokenizers
@require_tf
class 			_lowercase						(       unittest.TestCase					):
     _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     UpperCamelCase			( self    ) ->	Optional[Any]:
      # use "old" tokenizer here because of bug when downloading new tokenizer
      return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''    )







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
      return model







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.tokenizer(self.src_text    ,   return_tensors='''tf'''    )
      snake_case				=			self.model.generate(
          model_inputs.input_ids    ,   attention_mask=model_inputs.attention_mask    ,   num_beams=2    ,   use_cache=lowercase__    ,   )
      snake_case				=			self.tokenizer.batch_decode(generated_ids.numpy()    ,   skip_special_tokens=lowercase__    )[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 random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs







     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()    ,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,   )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1    )







     def     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

