




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case            = "▁"

_snake_case            = get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		BertGenerationTokenizer
     _a											=		False
     _a											=		True

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           _A							:  int    =		BertGenerationTokenizer(_a	,  keep_accents=_a       )
           tokenizer.save_pretrained(self.tmpdirname       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Dict    =		"""<s>"""
           _A							:  Any    =		1

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

     def       a__			(						self       )		->							Dict:
           _A							:  str    =		list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]	,  """<unk>"""       )
           self.assertEqual(vocab_keys[1]	,  """<s>"""       )
           self.assertEqual(vocab_keys[-1]	,  """<pad>"""       )
           self.assertEqual(len(_a       )	,  1002       )

     def       a__			(						self       )		->							int:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1000       )

     def       a__			(						self       )		->							Any:
           _A							:  Dict    =		BertGenerationTokenizer(_a	,  keep_accents=_a       )

           _A							:  List[Any]    =		tokenizer.tokenize("""This is a test"""       )
           self.assertListEqual(_a	,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(_a       )	,  [285, 46, 10, 170, 382]	,  )

           _A							:  Tuple    =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
           self.assertListEqual(
               _a	,  [
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """9""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """é""",
                   """.""",
               ]	,  )
           _A							:  str    =		tokenizer.convert_tokens_to_ids(_a       )
           self.assertListEqual(
               _a	,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,  )

           _A							:  Dict    =		tokenizer.convert_ids_to_tokens(_a       )
           self.assertListEqual(
               _a	,  [
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """<unk>""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """<unk>""",
                   """.""",
               ]	,  )

     @cached_property
     def       a__			(						self       )		->							List[Any]:
           return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""       )

     @slow
     def       a__			(						self       )		->							List[str]:
           _A							:  str    =		"""Hello World!"""
           _A							:  Optional[Any]    =		[1_8536, 2260, 101]

           self.assertListEqual(_a	,  self.big_tokenizer.encode(_a       )       )

     @slow
     def       a__			(						self       )		->							Tuple:
           _A							:  List[Any]    =		(
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           _A							:  int    =		[
               871,
               419,
               358,
               946,
               991,
               2521,
               452,
               358,
               1357,
               387,
               7751,
               3536,
               112,
               985,
               456,
               126,
               865,
               938,
               5400,
               5734,
               458,
               1368,
               467,
               786,
               2462,
               5246,
               1159,
               633,
               865,
               4519,
               457,
               582,
               852,
               2557,
               427,
               916,
               508,
               405,
               3_4324,
               497,
               391,
               408,
               1_1342,
               1244,
               385,
               100,
               938,
               985,
               456,
               574,
               362,
               1_2597,
               3200,
               3129,
               1172,
           ]

           self.assertListEqual(_a	,  self.big_tokenizer.encode(_a       )       )

     @require_torch
     @slow
     def       a__			(						self       )		->							Optional[int]:
           import torch

           from transformers import BertGenerationConfig, BertGenerationEncoder

           # Build sequence
           _A							:  str    =		list(self.big_tokenizer.get_vocab().keys()       )[:10]
           _A							:  Tuple    =		""" """.join(_a       )
           _A							:  Optional[int]    =		self.big_tokenizer.encode_plus(_a	,  return_tensors="""pt"""	,  return_token_type_ids=_a       )
           _A							:  Dict    =		self.big_tokenizer.batch_encode_plus(
               [sequence + """ """ + sequence]	,  return_tensors="""pt"""	,  return_token_type_ids=_a       )

           _A							:  List[str]    =		BertGenerationConfig()
           _A							:  Optional[int]    =		BertGenerationEncoder(_a       )

           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                 model(**_a       )
                 model(**_a       )



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[str]    =		{"""input_ids""": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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, 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, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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, 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, 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]], """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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0], [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, 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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_a	,  model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""	,  revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""	,  )







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		KandinskyVaaImgaImgPipeline
     _a											=		["image_embeds", "negative_image_embeds", "image"]
     _a											=		[
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _a											=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _a											=		False

     @property
     def       a__			(						self       )		->							int:
           return 32

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return 32

     @property
     def       a__			(						self       )		->							List[str]:
           return self.time_input_dim

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return self.time_input_dim * 4

     @property
     def       a__			(						self       )		->							str:
           return 100

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )

           _A							:  str    =		{
               """in_channels""": 4,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 8,
               """addition_embed_type""": """image""",
               """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							:  Union[str, Any]    =		UNetaDConditionModel(**_a       )
           return model

     @property
     def       a__			(						self       )		->							int:
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["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",
               ],
               "vq_embed_dim": 4,
           }

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  Dict    =		VQModel(**self.dummy_movq_kwargs       )
           return model

     def       a__			(						self       )		->							int:
           _A							:  Any    =		self.dummy_unet
           _A							:  List[Any]    =		self.dummy_movq

           _A							:  str    =		{
               """num_train_timesteps""": 1000,
               """beta_schedule""": """linear""",
               """beta_start""": 0.00085,
               """beta_end""": 0.012,
               """clip_sample""": False,
               """set_alpha_to_one""": False,
               """steps_offset""": 0,
               """prediction_type""": """epsilon""",
               """thresholding""": False,
           }

           _A							:  int    =		DDIMScheduler(**_a       )

           _A							:  Tuple    =		{
               """unet""": unet,
               """scheduler""": scheduler,
               """movq""": movq,
           }

           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							str:
           _A							:  Optional[int]    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Dict    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1       )       ).to(
               _a       )
           # create init_image
           _A							:  Optional[Any]    =		floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Optional[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       )[0]
           _A							:  Optional[Any]    =		Image.fromarray(np.uinta(_a       )       ).convert("""RGB"""       ).resize((256, 256)       )

           if str(_a       ).startswith("""mps"""       ):
                 _A							:  Tuple    =		torch.manual_seed(_a       )
           else:
                 _A							:  str    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Optional[Any]    =		{
               """image""": init_image,
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """num_inference_steps""": 10,
               """guidance_scale""": 7.0,
               """strength""": 0.2,
               """output_type""": """np""",
           }
           return inputs



     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		"""cpu"""

           _A							:  int    =		self.get_dummy_components()

           _A							:  Optional[int]    =		self.pipeline_class(**_a       )
           _A							:  Any    =		pipe.to(_a       )

           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[Any]    =		pipe(**self.get_dummy_inputs(_a       )       )
           _A							:  Dict    =		output.images

           _A							:  List[str]    =		pipe(
               **self.get_dummy_inputs(_a       )	,  return_dict=_a	,  )[0]

           _A							:  Dict    =		image[0, -3:, -3:, -1]
           _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

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

           _A							:  Optional[int]    =		np.array(
               [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]       )
           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 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							List[Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def       a__			(						self       )		->							List[str]:
           _A							:  Any    =		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_img2img_frog.npy"""       )

           _A							:  Union[str, Any]    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
           _A							:  Dict    =		"""A red cartoon frog, 4k"""

           _A							:  Dict    =		KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""	,  torch_dtype=torch.floataa       )
           pipe_prior.to(_a       )

           _A							:  int    =		KandinskyVaaImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder"""	,  torch_dtype=torch.floataa       )
           _A							:  Dict    =		pipeline.to(_a       )

           pipeline.set_progress_bar_config(disable=_a       )

           _A							:  Tuple    =		torch.Generator(device="""cpu"""       ).manual_seed(0       )
           _A    ,     _A							:  List[str]    =		pipe_prior(
               _a	,  generator=_a	,  num_inference_steps=5	,  negative_prompt=""""""	,  ).to_tuple()

           _A							:  int    =		pipeline(
               image=_a	,  image_embeds=_a	,  negative_image_embeds=_a	,  generator=_a	,  num_inference_steps=100	,  height=768	,  width=768	,  strength=0.2	,  output_type="""np"""	,  )

           _A							:  Optional[int]    =		output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(_a	,  _a       )

