'''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       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



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

       @property
       def _lowerCAmelCase(						self			)  ->  List[str]:
        return 32

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        return 32

       @property
       def _lowerCAmelCase(						self			)  ->  str:
        return self.time_input_dim

       @property
       def _lowerCAmelCase(						self			)  ->  Any:
        return self.time_input_dim * 4

       @property
       def _lowerCAmelCase(						self			)  ->  int:
        return 100

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        torch.manual_seed(0			)

        lowercase__    :   Tuple						= {
            '''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,
        }

        lowercase__    :   Optional[Any]						= UNetaDConditionModel(**__lowerCAmelCase			)
        return model

       @property
       def _lowerCAmelCase(						self			)  ->  int:
        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 _lowerCAmelCase(						self			)  ->  List[Any]:
        torch.manual_seed(0			)
        lowercase__    :   Any						= VQModel(**self.dummy_movq_kwargs			)
        return model

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Dict						= self.dummy_unet
        lowercase__    :   List[str]						= self.dummy_movq

        lowercase__    :   Optional[Any]						= DDIMScheduler(
            num_train_timesteps=1000     ,       beta_schedule='''linear'''     ,       beta_start=0.0_0_0_8_5     ,       beta_end=0.0_1_2     ,       clip_sample=__lowerCAmelCase     ,       set_alpha_to_one=__lowerCAmelCase     ,       steps_offset=1     ,       prediction_type='''epsilon'''     ,       thresholding=__lowerCAmelCase     ,       )

        lowercase__    :   Tuple						= {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''movq''': movq,
        }
        return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  Any:
        lowercase__    :   Union[str, Any]						= floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
        lowercase__    :   Dict						= floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(seed + 1			)			).to(
            __lowerCAmelCase			)

        # create hint
        lowercase__    :   Any						= floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)

        if str(__lowerCAmelCase			).startswith('''mps'''			):
         lowercase__    :   str						= torch.manual_seed(__lowerCAmelCase			)
        else:
         lowercase__    :   List[Any]						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
        lowercase__    :   Tuple						= {
            '''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 _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= '''cpu'''

        lowercase__    :   Tuple						= self.get_dummy_components()

        lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
        lowercase__    :   Optional[Any]						= pipe.to(__lowerCAmelCase			)

        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Dict						= pipe(**self.get_dummy_inputs(__lowerCAmelCase			)			)
        lowercase__    :   List[str]						= output.images

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

        lowercase__    :   Dict						= image[0, -3:, -3:, -1]
        lowercase__    :   Tuple						= image_from_tuple[0, -3:, -3:, -1]

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

        lowercase__    :   Tuple						= np.array(
            [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]			)

        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       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   List[str]						= load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''			)

        lowercase__    :   Dict						= load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinskyv22/hint_image_cat.png'''			)
        lowercase__    :   Any						= torch.from_numpy(np.array(__lowerCAmelCase			)			).float() / 2_5_5.0
        lowercase__    :   List[str]						= hint.permute(2     ,       0     ,       1			).unsqueeze(0			)

        lowercase__    :   Dict						= KandinskyVaaPriorPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-2-prior'''     ,       torch_dtype=torch.floataa			)
        pipe_prior.to(__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= KandinskyVaaControlnetPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-2-controlnet-depth'''     ,       torch_dtype=torch.floataa			)
        lowercase__    :   Optional[int]						= pipeline.to(__lowerCAmelCase			)
        pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= '''A robot, 4k photo'''

        lowercase__    :   List[Any]						= torch.Generator(device='''cuda'''			).manual_seed(0			)
        lowercase__			,							lowercase__    :   List[str]						= pipe_prior(
            __lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       num_inference_steps=5     ,       negative_prompt=''''''     ,       ).to_tuple()

        lowercase__    :   Union[str, Any]						= torch.Generator(device='''cuda'''			).manual_seed(0			)
        lowercase__    :   Union[str, Any]						= pipeline(
            image_embeds=__lowerCAmelCase     ,       negative_image_embeds=__lowerCAmelCase     ,       hint=__lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       num_inference_steps=100     ,       output_type='''np'''     ,       )

        lowercase__    :   Tuple						= output.images[0]

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

        assert_mean_pixel_difference(__lowerCAmelCase     ,       __lowerCAmelCase			)


'''simple docstring'''

import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ProphetNetTokenizer
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Any:
        super().setUp()

        lowercase__    :   Tuple						= [
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        lowercase__    :   List[str]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']			)
        with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''			) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Union[str, Any]						= '''UNwant\u00E9d,running'''
        lowercase__    :   List[Any]						= '''unwanted, running'''
        return input_text, output_text

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[str]						= self.tokenizer_class(self.vocab_file			)

        lowercase__    :   Union[str, Any]						= tokenizer.tokenize('''UNwant\u00E9d,running'''			)
        self.assertListEqual(__lowerCAmelCase     ,       ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       [9, 6, 7, 12, 10, 11]			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''			)     ,       ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''			)     ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''h\u00E9llo''']			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   str						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''			)     ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       never_split=['''[UNK]''']			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''			)     ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        lowercase__    :   Union[str, Any]						= {}
        for i, token in enumerate(__lowerCAmelCase			):
         lowercase__    :   List[Any]						= i
        lowercase__    :   Dict						= WordpieceTokenizer(vocab=__lowerCAmelCase     ,       unk_token='''[UNK]'''			)

        self.assertListEqual(tokenizer.tokenize(''''''			)     ,       []			)

        self.assertListEqual(tokenizer.tokenize('''unwanted running'''			)     ,       ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']			)

        self.assertListEqual(tokenizer.tokenize('''unwantedX running'''			)     ,       ['''[UNK]''', '''runn''', '''##ing''']			)

       @require_torch
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Union[str, Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   Optional[int]						= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        lowercase__    :   Dict						= [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
        lowercase__    :   int						= tokenizer(__lowerCAmelCase     ,       padding=__lowerCAmelCase     ,       return_tensors='''pt'''			)
        self.assertIsInstance(__lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Optional[int]						= list(batch.input_ids.numpy()[0]			)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        self.assertEqual((2, 9)     ,       batch.input_ids.shape			)
        self.assertEqual((2, 9)     ,       batch.attention_mask.shape			)

       def _lowerCAmelCase(						self			)  ->  str:
        self.assertTrue(_is_whitespace(''' '''			)			)
        self.assertTrue(_is_whitespace('''\t'''			)			)
        self.assertTrue(_is_whitespace('''\r'''			)			)
        self.assertTrue(_is_whitespace('''\n'''			)			)
        self.assertTrue(_is_whitespace('''\u00A0'''			)			)

        self.assertFalse(_is_whitespace('''A'''			)			)
        self.assertFalse(_is_whitespace('''-'''			)			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        self.assertTrue(_is_control('''\u0005'''			)			)

        self.assertFalse(_is_control('''A'''			)			)
        self.assertFalse(_is_control(''' '''			)			)
        self.assertFalse(_is_control('''\t'''			)			)
        self.assertFalse(_is_control('''\r'''			)			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        self.assertTrue(_is_punctuation('''-'''			)			)
        self.assertTrue(_is_punctuation('''$'''			)			)
        self.assertTrue(_is_punctuation('''`'''			)			)
        self.assertTrue(_is_punctuation('''.'''			)			)

        self.assertFalse(_is_punctuation('''A'''			)			)
        self.assertFalse(_is_punctuation(''' '''			)			)


       @slow
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   List[Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   List[str]						= tokenizer.encode('''sequence builders'''     ,       add_special_tokens=__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= tokenizer.encode('''multi-sequence build'''     ,       add_special_tokens=__lowerCAmelCase			)

        lowercase__    :   Tuple						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
        lowercase__    :   List[str]						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase     ,       __lowerCAmelCase			)

        assert encoded_sentence == text + [102]
        assert encoded_pair == text + [102] + text_a + [102]

