


'''simple docstring'''


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__lowerCAmelCase		(							__a  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							BarthezTokenizer
  a_  					=							BarthezTokenizerFast
  a_  					=							True
  a_  					=							True






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        super().setUp()

        A_						:  Optional[int]          =				BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""" )
        tokenizer.save_pretrained(self.tmpdirname )
        tokenizer.save_pretrained(self.tmpdirname	,legacy_format=snake_case__ )
        A_						:  Any          =				tokenizer






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				"""<pad>"""
        A_						:  List[Any]          =				1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ )	,snake_case__ )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ )	,snake_case__ )






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

        '''simple docstring'''




        A_						:  Optional[int]          =				list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]	,"""<s>""" )
        self.assertEqual(vocab_keys[1]	,"""<pad>""" )
        self.assertEqual(vocab_keys[-1]	,"""<mask>""" )
        self.assertEqual(len(snake_case__ )	,101122 )






  def        _a							(							self :       str ):

        '''simple docstring'''




        self.assertEqual(self.get_tokenizer().vocab_size	,101122 )






  @require_torch
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        A_						:  Dict          =				[0, 57, 3018, 70307, 91, 2]

        A_						:  Tuple          =				self.tokenizer(
            snake_case__	,max_length=len(snake_case__ )	,padding=snake_case__	,truncation=snake_case__	,return_tensors="""pt""" )
        self.assertIsInstance(snake_case__	,snake_case__ )

        self.assertEqual((2, 6)	,batch.input_ids.shape )
        self.assertEqual((2, 6)	,batch.attention_mask.shape )
        A_						:  List[Any]          =				batch.input_ids.tolist()[0]
        self.assertListEqual(snake_case__	,snake_case__ )






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        if not self.test_rust_tokenizer:
              return

        A_						:  Any          =				self.get_tokenizer()
        A_						:  Dict          =				self.get_rust_tokenizer()

        A_						:  List[str]          =				"""I was born in 92000, and this is falsé."""

        A_						:  Any          =				tokenizer.tokenize(snake_case__ )
        A_						:  str          =				rust_tokenizer.tokenize(snake_case__ )
        self.assertListEqual(snake_case__	,snake_case__ )

        A_						:  int          =				tokenizer.encode(snake_case__	,add_special_tokens=snake_case__ )
        A_						:  int          =				rust_tokenizer.encode(snake_case__	,add_special_tokens=snake_case__ )
        self.assertListEqual(snake_case__	,snake_case__ )

        A_						:  List[Any]          =				self.get_rust_tokenizer()
        A_						:  Optional[Any]          =				tokenizer.encode(snake_case__ )
        A_						:  Any          =				rust_tokenizer.encode(snake_case__ )
        self.assertListEqual(snake_case__	,snake_case__ )






  @slow
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Any          =				{"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        A_						:  Tuple          =				[
            """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
            """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
            """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
            """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
            """telles que la traduction et la synthèse de texte.""",
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=snake_case__	,model_name="""moussaKam/mbarthez"""	,revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""	,sequences=snake_case__	,)




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							KandinskyVaaControlnetPipeline
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  List[Any]          =				{
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A_						:  Tuple          =				UNetaDConditionModel(**_a )
        return model






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.dummy_unet
        A_						:  int          =				self.dummy_movq

        A_						:  Tuple          =				DDIMScheduler(
            num_train_timesteps=1000	,beta_schedule="""linear"""	,beta_start=0.00085	,beta_end=0.012	,clip_sample=_a	,set_alpha_to_one=_a	,steps_offset=1	,prediction_type="""epsilon"""	,thresholding=_a	,)

        A_						:  int          =				{
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






  def        _a							(							self :       Dict	,_a :       str	,_a :       Union[str, Any]=0 ):

        '''simple docstring'''




        A_						:  Dict          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(_a ) ).to(_a )
        A_						:  int          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1 ) ).to(
            _a )

        # create hint
        A_						:  List[Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[Any]          =				torch.manual_seed(_a )
        else:
              A_						:  str          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  List[Any]          =				{
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  List[Any]          =				"""cpu"""

        A_						:  List[str]          =				self.get_dummy_components()

        A_						:  Tuple          =				self.pipeline_class(**_a )
        A_						:  Dict          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images

        A_						:  Optional[Any]          =				pipe(
            **self.get_dummy_inputs(_a )	,return_dict=_a	,)[0]

        A_						:  Tuple          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A_						:  List[Any]          =				np.array(
            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )

        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )

        A_						:  Optional[int]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png""" )
        A_						:  Optional[int]          =				torch.from_numpy(np.array(_a ) ).float() / 255.0
        A_						:  List[Any]          =				hint.permute(2	,0	,1 ).unsqueeze(0 )

        A_						:  List[Any]          =				KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(_a )

        A_						:  Union[str, Any]          =				KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,torch_dtype=torch.floataa )
        A_						:  Union[str, Any]          =				pipeline.to(_a )
        pipeline.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				"""A robot, 4k photo"""

        A_						:  Any          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_   ,					A_						:  List[str]          =				pipe_prior(
            _a	,generator=_a	,num_inference_steps=5	,negative_prompt=""""""	,).to_tuple()

        A_						:  int          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_						:  List[Any]          =				pipeline(
            image_embeds=_a	,negative_image_embeds=_a	,hint=_a	,generator=_a	,num_inference_steps=100	,output_type="""np"""	,)

        A_						:  Dict          =				output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_a	,_a )
