

"""simple docstring"""
import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  CLIPTokenizer
  SCREAMING_SNAKE_CASE					      =  CLIPTokenizerFast
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  {}
  SCREAMING_SNAKE_CASE					      =  False



  def  _a      (self	):


        """simple docstring"""

        super().setUp()

        # fmt: off
        UpperCAmelCase__			:							Optional[Any]							=    ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
        # fmt: on
        UpperCAmelCase__			:							Optional[int]							=    dict(zip(_lowerCamelCase							,       range(len(_lowerCamelCase	)	)	)	)
        UpperCAmelCase__			:							List[str]							=    ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
        UpperCAmelCase__			:							Dict							=    {"""unk_token""": """<unk>"""}

        UpperCAmelCase__			:							Optional[Any]							=    os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES["""vocab_file"""]	)
        UpperCAmelCase__			:							int							=    os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES["""merges_file"""]	)
        with open(self.vocab_file							,       """w"""							,       encoding="""utf-8"""	) as fp:
              fp.write(json.dumps(_lowerCamelCase	) + """\n"""	)
        with open(self.merges_file							,       """w"""							,       encoding="""utf-8"""	) as fp:
              fp.write("""\n""".join(_lowerCamelCase	)	)



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        kwargs.update(self.special_tokens_map	)
        return CLIPTokenizer.from_pretrained(self.tmpdirname							,       **_lowerCamelCase	)



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        kwargs.update(self.special_tokens_map	)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,       **_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    """lower newer"""
        UpperCAmelCase__			:							List[Any]							=    """lower newer"""
        return input_text, output_text



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    CLIPTokenizer(self.vocab_file							,       self.merges_file							,       **self.special_tokens_map	)
        UpperCAmelCase__			:							Tuple							=    """lower newer"""
        UpperCAmelCase__			:							List[str]							=    ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
        UpperCAmelCase__			:							int							=    tokenizer.tokenize(_lowerCamelCase	)
        self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

        UpperCAmelCase__			:							Union[str, Any]							=    tokens + [tokenizer.unk_token]
        UpperCAmelCase__			:							List[Any]							=    [10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase	)							,       _lowerCamelCase	)



  @require_ftfy
  def  _a      (self	):


        """simple docstring"""

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                    UpperCAmelCase__			:							List[Any]							=    self.tokenizer_class.from_pretrained(_lowerCamelCase							,       **_lowerCamelCase	)
                    UpperCAmelCase__			:							Optional[Any]							=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase							,       **_lowerCamelCase	)

                    UpperCAmelCase__			:							str							=    """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
                    UpperCAmelCase__			:							Union[str, Any]							=    tokenizer_s.tokenize(_lowerCamelCase	)
                    UpperCAmelCase__			:							Dict							=    tokenizer_r.tokenize(_lowerCamelCase	)

                    self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

                    # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                    # with Tilde) encoded in 2 different ways
                    UpperCAmelCase__			:							List[Any]							=    """xa\u0303y""" + """ """ + """x\xe3y"""
                    UpperCAmelCase__			:							Dict							=    tokenizer_s.tokenize(_lowerCamelCase	)
                    UpperCAmelCase__			:							Any							=    tokenizer_r.tokenize(_lowerCamelCase	)

                    self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

                    # Test that the tokenization is identical on unicode of space type
                    UpperCAmelCase__			:							Optional[Any]							=    [
                        """\u0009""",  # (horizontal tab, '\t')
                        """\u000B""",  # (vertical tab)
                        """\u000C""",  # (form feed)
                        """\u0020""",  # (space, ' ')
                        """\u200E""",  # (left-to-right mark):w
                        """\u200F""",  # (right-to-left mark)
                    ]
                    for unicode_seq in spaces_unicodes:
                          UpperCAmelCase__			:							List[str]							=    tokenizer_s.tokenize(_lowerCamelCase	)
                          UpperCAmelCase__			:							Any							=    tokenizer_r.tokenize(_lowerCamelCase	)

                          self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)

                    # Test that the tokenization is identical on unicode of line break type
                    UpperCAmelCase__			:							int							=    [
                        """\u000A""",  # (line feed, '\n')
                        """\r\n""",  # (carriage return and line feed, '\r\n')
                        """\u000D""",  # (carriage return, '\r')
                        """\r""",  # (carriage return, '\r')
                        """\u000D""",  # (carriage return, '\r')
                        """\u2028""",  # (line separator)
                        """\u2029""",  # (paragraph separator)
                        # "\u0085", # (next line)
                    ]

                    # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                    # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                    # space (and thus into an empty list).

                    for unicode_seq in line_break_unicodes:
                          UpperCAmelCase__			:							Any							=    tokenizer_s.tokenize(_lowerCamelCase	)
                          UpperCAmelCase__			:							Tuple							=    tokenizer_r.tokenize(_lowerCamelCase	)

                          self.assertListEqual(_lowerCamelCase							,       _lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                    UpperCAmelCase__			:							str							=    """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                    UpperCAmelCase__			:							Dict							=    F"""{text_of_1_token} {text_of_1_token}"""

                    UpperCAmelCase__			:							Dict							=    self.rust_tokenizer_class.from_pretrained(
                        _lowerCamelCase							,       use_fast=_lowerCamelCase							,       )
                    UpperCAmelCase__			:							Union[str, Any]							=    tokenizer_r(_lowerCamelCase							,       return_offsets_mapping=_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
                    self.assertEqual(encoding.offset_mapping[0]							,       (0, len(_lowerCamelCase	))	)
                    self.assertEqual(
                        encoding.offset_mapping[1]							,       (len(_lowerCamelCase	) + 1, len(_lowerCamelCase	) + 1 + len(_lowerCamelCase	))							,       )

                    UpperCAmelCase__			:							List[Any]							=    F""" {text}"""

                    UpperCAmelCase__			:							Optional[Any]							=    self.rust_tokenizer_class.from_pretrained(
                        _lowerCamelCase							,       use_fast=_lowerCamelCase							,       )
                    UpperCAmelCase__			:							int							=    tokenizer_r(_lowerCamelCase							,       return_offsets_mapping=_lowerCamelCase							,       add_special_tokens=_lowerCamelCase	)
                    self.assertEqual(encoding.offset_mapping[0]							,       (1, 1 + len(_lowerCamelCase	))	)
                    self.assertEqual(
                        encoding.offset_mapping[1]							,       (1 + len(_lowerCamelCase	) + 1, 1 + len(_lowerCamelCase	) + 1 + len(_lowerCamelCase	))							,       )



  def  _a      (self	):


        """simple docstring"""

        with self.assertRaises(_lowerCamelCase	) as context:
              self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""	)

        self.assertTrue(
            context.exception.args[0].startswith(
                """The `backend_tokenizer` provided does not match the expected format."""	)	)



  @require_ftfy
  def  _a      (self	):


        """simple docstring"""

        super().test_tokenization_python_rust_equals()




  def  _a      (self	):


        """simple docstring"""

        pass




"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    MCLIPConfig(
            numDims=self.cross_attention_dim							,       transformerDimensions=self.text_embedder_hidden_size							,       hidden_size=self.text_embedder_hidden_size							,       intermediate_size=37							,       num_attention_heads=4							,       num_hidden_layers=5							,       vocab_size=1005							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """in_channels""": 9,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """text_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""": """text_image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        UpperCAmelCase__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """simple docstring"""

        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	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1	)	).to(_lowerCamelCase	)
        # create init_image
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    image.cpu().permute(0							,       2							,       3							,       1	)[0]
        UpperCAmelCase__			:							Optional[Any]							=    Image.fromarray(np.uinta(_lowerCamelCase	)	).convert("""RGB"""	).resize((256, 256)	)
        # create mask
        UpperCAmelCase__			:							List[Any]							=    np.ones((64, 64)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """prompt""": """horse""",
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

        print(F"""image.shape {image.shape}"""	)

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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()}"""




  def  _a      (self	):


        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

        UpperCAmelCase__			:							Union[str, Any]							=    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
        UpperCAmelCase__			:							List[str]							=    np.ones((768, 768)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

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

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

