



import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 						UpperCAmelCase_					( a     ,      unittest.TestCase):
							lowerCamelCase__         =	UnCLIPImageVariationPipeline
							lowerCamelCase__         =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
							lowerCamelCase__         =	IMAGE_VARIATION_BATCH_PARAMS

							lowerCamelCase__         =	[
							    'generator',
							    'return_dict',
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]
							lowerCamelCase__         =	False







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 32







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 32







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self.time_input_dim







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self.time_input_dim * 4







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 100







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase  :							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=37,    layer_norm_eps=1E-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=1000,    )
														return CLIPTextModelWithProjection(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase  :							str				     =					CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size,    projection_dim=self.text_embedder_hidden_size,    num_hidden_layers=5,    num_attention_heads=4,    image_size=32,    intermediate_size=37,    patch_size=1,    )
														return CLIPVisionModelWithProjection(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							Union[str, Any]				     =					{
														    "clip_embeddings_dim": self.text_embedder_hidden_size,
														    "time_embed_dim": self.time_embed_dim,
														    "cross_attention_dim": self.cross_attention_dim,
														}

														_lowerCAmelCase  :							Union[str, Any]				     =					UnCLIPTextProjModel(**__a)
														return model







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							Optional[int]				     =					{
														    "sample_size": 32,
														    # RGB in channels
														    "in_channels": 3,
														    # Out channels is double in channels because predicts mean and variance
														    "out_channels": 6,
														    "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,
														    "cross_attention_dim": self.cross_attention_dim,
														    "attention_head_dim": 4,
														    "resnet_time_scale_shift": "scale_shift",
														    "class_embed_type": "identity",
														}

														_lowerCAmelCase  :							Tuple				     =					UNetaDConditionModel(**__a)
														return model







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return {
														    "sample_size": 64,
														    "layers_per_block": 1,
														    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
														    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
														    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
														    "in_channels": 6,
														    "out_channels": 3,
														}







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							str				     =					UNetaDModel(**self.dummy_super_res_kwargs)
														return model







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(1)

														_lowerCAmelCase  :							Union[str, Any]				     =					UNetaDModel(**self.dummy_super_res_kwargs)
														return model







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.dummy_decoder
														_lowerCAmelCase  :							int				     =					self.dummy_text_proj
														_lowerCAmelCase  :							Tuple				     =					self.dummy_text_encoder
														_lowerCAmelCase  :							str				     =					self.dummy_tokenizer
														_lowerCAmelCase  :							Optional[Any]				     =					self.dummy_super_res_first
														_lowerCAmelCase  :							List[Any]				     =					self.dummy_super_res_last

														_lowerCAmelCase  :							Tuple				     =					UnCLIPScheduler(
														    variance_type="learned_range",    prediction_type="epsilon",    num_train_timesteps=1000,    )

														_lowerCAmelCase  :							Any				     =					UnCLIPScheduler(
														    variance_type="fixed_small_log",    prediction_type="epsilon",    num_train_timesteps=1000,    )

														_lowerCAmelCase  :							Dict				     =					CLIPImageProcessor(crop_size=32,    size=32)

														_lowerCAmelCase  :							str				     =					self.dummy_image_encoder

														return {
														    "decoder": decoder,
														    "text_encoder": text_encoder,
														    "tokenizer": tokenizer,
														    "text_proj": text_proj,
														    "feature_extractor": feature_extractor,
														    "image_encoder": image_encoder,
														    "super_res_first": super_res_first,
														    "super_res_last": super_res_last,
														    "decoder_scheduler": decoder_scheduler,
														    "super_res_scheduler": super_res_scheduler,
														}







							def      snake_case__   (     self,    __a,    __a=0,    __a=True):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					floats_tensor((1, 3, 32, 32),    rng=random.Random(__a)).to(__a)
														if str(__a).startswith("mps"):
																					_lowerCAmelCase  :							List[Any]				     =					torch.manual_seed(__a)
														else:
																					_lowerCAmelCase  :							Dict				     =					torch.Generator(device=__a).manual_seed(__a)

														if pil_image:
																					_lowerCAmelCase  :							Any				     =					input_image * 0.5 + 0.5
																					_lowerCAmelCase  :							int				     =					input_image.clamp(0,    1)
																					_lowerCAmelCase  :							Optional[Any]				     =					input_image.cpu().permute(0,    2,    3,    1).float().numpy()
																					_lowerCAmelCase  :							Dict				     =					DiffusionPipeline.numpy_to_pil(__a)[0]

														return {
														    "image": input_image,
														    "generator": generator,
														    "decoder_num_inference_steps": 2,
														    "super_res_num_inference_steps": 2,
														    "output_type": "np",
														}







							def      snake_case__   (     self):

														'''simple docstring'''

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

														_lowerCAmelCase  :							Union[str, Any]				     =					self.get_dummy_components()

														_lowerCAmelCase  :							int				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							int				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							List[str]				     =					pipe(**__a)
														_lowerCAmelCase  :							Optional[int]				     =					output.images

														_lowerCAmelCase  :							str				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[int]				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

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

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

														_lowerCAmelCase  :							str				     =					np.array(
														    [
														        0.9_997,
														        0.0_002,
														        0.9_997,
														        0.9_997,
														        0.9_969,
														        0.0_023,
														        0.9_997,
														        0.9_969,
														        0.9_970,
														    ])

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







							def      snake_case__   (     self):

														'''simple docstring'''

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

														_lowerCAmelCase  :							Dict				     =					self.get_dummy_components()

														_lowerCAmelCase  :							int				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							Tuple				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							List[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[int]				     =					pipe(**__a)
														_lowerCAmelCase  :							Any				     =					output.images

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[Any]				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

														_lowerCAmelCase  :							str				     =					image[0, -3:, -3:, -1]
														_lowerCAmelCase  :							List[Any]				     =					image_from_tuple[0, -3:, -3:, -1]

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

														_lowerCAmelCase  :							Any				     =					np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971])

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					"cpu"

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

														_lowerCAmelCase  :							Optional[int]				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							List[str]				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							int				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														_lowerCAmelCase  :							List[str]				     =					[
														    pipeline_inputs["image"],
														    pipeline_inputs["image"],
														]

														_lowerCAmelCase  :							Union[str, Any]				     =					pipe(**__a)
														_lowerCAmelCase  :							Optional[Any]				     =					output.images

														_lowerCAmelCase  :							Any				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														_lowerCAmelCase  :							Tuple				     =					[
														    tuple_pipeline_inputs["image"],
														    tuple_pipeline_inputs["image"],
														]

														_lowerCAmelCase  :							Dict				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

														_lowerCAmelCase  :							Tuple				     =					image[0, -3:, -3:, -1]
														_lowerCAmelCase  :							str				     =					image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (2, 64, 64, 3)

														_lowerCAmelCase  :							Dict				     =					np.array(
														    [
														        0.9_997,
														        0.9_989,
														        0.0_008,
														        0.0_021,
														        0.9_960,
														        0.0_018,
														        0.0_014,
														        0.0_002,
														        0.9_933,
														    ])

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					torch.device("cpu")



														class 						UpperCAmelCase_					:
																					lowerCamelCase__         =	1



														_lowerCAmelCase  :							Optional[int]				     =					self.get_dummy_components()

														_lowerCAmelCase  :							Union[str, Any]				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							Dict				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Any				     =					torch.Generator(device=__a).manual_seed(0)
														_lowerCAmelCase  :							List[str]				     =					pipe.decoder.dtype
														_lowerCAmelCase  :							Optional[int]				     =					1

														_lowerCAmelCase  :							List[Any]				     =					(
														    batch_size,
														    pipe.decoder.config.in_channels,
														    pipe.decoder.config.sample_size,
														    pipe.decoder.config.sample_size,
														)
														_lowerCAmelCase  :							List[Any]				     =					pipe.prepare_latents(
														    __a,    dtype=__a,    device=__a,    generator=__a,    latents=__a,    scheduler=DummyScheduler())

														_lowerCAmelCase  :							Union[str, Any]				     =					(
														    batch_size,
														    pipe.super_res_first.config.in_channels // 2,
														    pipe.super_res_first.config.sample_size,
														    pipe.super_res_first.config.sample_size,
														)
														_lowerCAmelCase  :							int				     =					pipe.prepare_latents(
														    __a,    dtype=__a,    device=__a,    generator=__a,    latents=__a,    scheduler=DummyScheduler())

														_lowerCAmelCase  :							str				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Dict				     =					pipe(
														    **__a,    decoder_latents=__a,    super_res_latents=__a).images

														_lowerCAmelCase  :							int				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														# Don't pass image, instead pass embedding
														_lowerCAmelCase  :							Union[str, Any]				     =					pipeline_inputs.pop("image")
														_lowerCAmelCase  :							Dict				     =					pipe.image_encoder(__a).image_embeds

														_lowerCAmelCase  :							str				     =					pipe(
														    **__a,    decoder_latents=__a,    super_res_latents=__a,    image_embeddings=__a,    ).images

														# make sure passing text embeddings manually is identical
														assert np.abs(img_out_a - img_out_a).max() < 1E-4







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					torch_device == "cpu"

														# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
														_lowerCAmelCase  :							Optional[int]				     =					1E-2

														self._test_attention_slicing_forward_pass(
														    test_max_difference=__a,    expected_max_diff=__a)







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					torch_device == "cpu"
														_lowerCAmelCase  :							Optional[Any]				     =					True
														_lowerCAmelCase  :							List[str]				     =					[
														    "decoder_num_inference_steps",
														    "super_res_num_inference_steps",
														]

														self._test_inference_batch_single_identical(
														    test_max_difference=__a,    relax_max_difference=__a,    additional_params_copy_to_batched_inputs=__a,    )







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					[
														    "decoder_num_inference_steps",
														    "super_res_num_inference_steps",
														]

														if torch_device == "mps":
																					# TODO: MPS errors with larger batch sizes
																					_lowerCAmelCase  :							Optional[int]				     =					[2, 3]
																					self._test_inference_batch_consistent(
																					    batch_sizes=__a,    additional_params_copy_to_batched_inputs=__a,    )
														else:
																					self._test_inference_batch_consistent(
																					    additional_params_copy_to_batched_inputs=__a)







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_save_load_local()







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_save_load_optional_components()



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







							def      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png")
														_lowerCAmelCase  :							List[str]				     =					load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy")

														_lowerCAmelCase  :							Any				     =					UnCLIPImageVariationPipeline.from_pretrained(
														    "kakaobrain/karlo-v1-alpha-image-variations",    torch_dtype=torch.floataa)
														_lowerCAmelCase  :							Dict				     =					pipeline.to(__a)
														pipeline.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Dict				     =					torch.Generator(device="cpu").manual_seed(0)
														_lowerCAmelCase  :							Tuple				     =					pipeline(
														    __a,    generator=__a,    output_type="np",    )

														_lowerCAmelCase  :							Any				     =					output.images[0]

														assert image.shape == (256, 256, 3)

														assert_mean_pixel_difference(__a,    __a,    15)





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_snake_case	=						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

_snake_case	=						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

_snake_case	=						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,      _KWARGS_DESCRIPTION)
class 						UpperCAmelCase_					( datasets.Metric):







							def      snake_case__   (     self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
														        {
														            "predictions": datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),
														            "references": datasets.Sequence(
														                datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),    id="references"),
														        }),    codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"],    reference_urls=[
														        "https://en.wikipedia.org/wiki/BLEU",
														        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
														    ],    )







							def      snake_case__   (     self,    __a,    __a,    __a=4,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					compute_bleu(
														    reference_corpus=__a,    translation_corpus=__a,    max_order=__a,    smooth=__a)
														((_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase))  :							Dict				     =					score
														return {
														    "bleu": bleu,
														    "precisions": precisions,
														    "brevity_penalty": bp,
														    "length_ratio": ratio,
														    "translation_length": translation_length,
														    "reference_length": reference_length,
														}
