





"""simple docstring"""





import datasets

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


_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\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 		SCREAMING_SNAKE_CASE_							(    datasets.Metric   ):
    """simple docstring"""




    def 					__lowercase  (     self  :List[str]			):
           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 					__lowercase  (     self  :List[Any]						,    __lowercase  :str						,    __lowercase  :Any						,    __lowercase  :Tuple=4						,    __lowercase  :Dict=False			):
           __lowerCamelCase      :      List[Any]         =compute_bleu(
               reference_corpus=__lowercase						,    translation_corpus=__lowercase						,    max_order=__lowercase						,    smooth=__lowercase			)
           (__lowerCamelCase)      :      Any         =score
           return {
               "bleu": bleu,
               "precisions": precisions,
               "brevity_penalty": bp,
               "length_ratio": ratio,
               "translation_length": translation_length,
               "reference_length": reference_length,
           }











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =AutoencoderKL(
											    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,    latent_channels=4						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      int         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''An astronaut riding an elephant''',
											    '''source_prompt''': '''An astronaut riding a horse''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''eta''': 0.1,
											    '''strength''': 0.8,
											    '''guidance_scale''': 3,
											    '''source_guidance_scale''': 1,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

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




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




				@unittest.skip('''non-deterministic pipeline'''			)
				def 					__lowercase  (     self  :Dict			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




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




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image			).max() < 5e-1




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

											assert np.abs(image - expected_image			).max() < 2e-2




