






'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
 from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ =			get_tests_dir('fixtures/test_sentencepiece.model')

UpperCamelCase__ =			{'target_lang': 'fi', 'source_lang': 'en'}
UpperCamelCase__ =			'>>zh<<'
UpperCamelCase__ =			'Helsinki-NLP/'

if is_torch_available():
 UpperCamelCase__ =			'pt'
elif is_tf_available():
 UpperCamelCase__ =			'tf'
else:
 UpperCamelCase__ =			'jax'


@require_sentencepiece
class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Optional[int]		       =    MarianTokenizer
    __lowerCamelCase:   Optional[Any]		       =    False
    __lowerCamelCase:   Optional[Any]		       =    True




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       super().setUp()
       lowercase_	:    int           =     ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
       lowercase_	:    Optional[int]           =     dict(zip(a  ,     range(len(a			)			)			)			)
       lowercase_	:    Any           =     Path(self.tmpdirname			)
       save_json(a  ,     save_dir / VOCAB_FILES_NAMES["vocab"]			)
       save_json(a  ,     save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]			)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
          copyfile(a  ,     save_dir / VOCAB_FILES_NAMES["source_spm"]			)
          copyfile(a  ,     save_dir / VOCAB_FILES_NAMES["target_spm"]			)

       lowercase_	:    str           =     MarianTokenizer.from_pretrained(self.tmpdirname			)
       tokenizer.save_pretrained(self.tmpdirname			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     **a  :       Union[str, Any]			):






       '''simple docstring'''






       return MarianTokenizer.from_pretrained(self.tmpdirname  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Union[str, Any]			):






       '''simple docstring'''






       return (
           "This is a test",
           "This is a test",
       )




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     "</s>"
       lowercase_	:    Optional[Any]           =     0

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




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     list(self.get_tokenizer().get_vocab().keys()			)

       self.assertEqual(vocab_keys[0]  ,     "</s>"			)
       self.assertEqual(vocab_keys[1]  ,     "<unk>"			)
       self.assertEqual(vocab_keys[-1]  ,     "<pad>"			)
       self.assertEqual(len(a			)  ,     9			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       self.assertEqual(self.get_tokenizer().vocab_size  ,     9			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""			)
       lowercase_	:    List[Any]           =     en_de_tokenizer(["I am a small frog"]  ,     return_tensors=a			)
       self.assertIsInstance(a  ,     a			)
       lowercase_	:    List[str]           =     [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(a  ,     batch.input_ids[0]			)

       lowercase_	:    str           =     tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(a			)
       lowercase_	:    str           =     [x.name for x in Path(a			).glob("*"			)]
       self.assertIn("source.spm"  ,     a			)
       MarianTokenizer.from_pretrained(a			)




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     self.get_tokenizer()

       lowercase_	:    List[str]           =     tok(
           ["I am a small frog" * 1_0_0_0, "I am a small frog"]  ,     padding=a  ,     truncation=a  ,     return_tensors=a			)
       self.assertIsInstance(a  ,     a			)
       self.assertEqual(batch.input_ids.shape  ,     (2, 5_1_2)			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.get_tokenizer()
       lowercase_	:    Dict           =     tok(["I am a tiny frog", "I am a small frog"]  ,     padding=a  ,     return_tensors=a			)
       self.assertIsInstance(a  ,     a			)
       self.assertEqual(batch_smaller.input_ids.shape  ,     (2, 1_0)			)




    @slow
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     {"input_ids": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_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, 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], [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, 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="Helsinki-NLP/opus-mt-en-de"  ,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"  ,     decode_kwargs={"use_source_tokenizer": True}  ,     )





    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"			)

       lowercase_	:    Tuple           =     "Tämä on testi"
       lowercase_	:    Any           =     "This is a test"

       lowercase_	:    int           =     [7_6, 7, 2_0_4_7, 2]
       lowercase_	:    int           =     [6_9, 1_2, 1_1, 9_4_0, 2]

       lowercase_	:    Optional[int]           =     tokenizer(a			).input_ids
       self.assertListEqual(a  ,     a			)

       lowercase_	:    Dict           =     tokenizer(text_target=a			).input_ids
       self.assertListEqual(a  ,     a			)

       lowercase_	:    List[Any]           =     tokenizer.decode(a  ,     skip_special_tokens=a			)
       self.assertEqual(a  ,     a			)












'''simple docstring'''
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




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






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




    @property
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return 1_0_0




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






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




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






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     CLIPTextConfig(
           bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
       return CLIPTextModelWithProjection(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size  ,     image_size=2_2_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1_4  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




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






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






       if str(a			).startswith("mps"			):
          lowercase_	:    int           =     torch.manual_seed(a			)
       else:
          lowercase_	:    Optional[Any]           =     torch.Generator(device=a			).manual_seed(a			)
       lowercase_	:    Any           =     {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs




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






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

       self._test_inference_batch_single_identical(
           test_max_difference=a  ,     relax_max_difference=a  ,     test_mean_pixel_difference=a  ,     )





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

       self._test_attention_slicing_forward_pass(
           test_max_difference=a  ,     test_mean_pixel_difference=a  ,     )




