





import datasets

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


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

UpperCAmelCase_							=				"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality 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,
the 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
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU'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
representing 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
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

UpperCAmelCase_							=				"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				, _KWARGS_DESCRIPTION				)
class     UpperCamelCase__					(     datasets.Metric				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> List[str]:






									"""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,   snake_case__,   snake_case__,   snake_case__=4,   snake_case__=False  )       -> int:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     compute_bleu(
									    reference_corpus=snake_case__,   translation_corpus=snake_case__,   max_order=snake_case__,   smooth=snake_case__  )
									((lowercase_)				,		(lowercase_)				,		(lowercase_)				,		(lowercase_)				,		(lowercase_)				,		(lowercase_))	:   Optional[int]  				=     score
									return {
									    "bleu": bleu,
									    "precisions": precisions,
									    "brevity_penalty": bp,
									    "length_ratio": ratio,
									    "translation_length": translation_length,
									    "reference_length": reference_length,
									}






import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				, unittest.TestCase				):






		'''simple docstring'''






		__a       :       Optional[int]       =	StableUnCLIPPipeline
		__a       :       int       =	TEXT_TO_IMAGE_PARAMS
		__a       :       Optional[Any]       =	TEXT_TO_IMAGE_BATCH_PARAMS
		__a       :       Tuple       =	TEXT_TO_IMAGE_IMAGE_PARAMS
		__a       :       Optional[int]       =	TEXT_TO_IMAGE_IMAGE_PARAMS

		# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
		__a       :       Tuple       =	False






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     32
									lowercase_	:   Tuple  				=     embedder_hidden_size

									# prior components

									torch.manual_seed(0  )
									lowercase_	:   Optional[Any]  				=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									torch.manual_seed(0  )
									lowercase_	:   Dict  				=     CLIPTextModelWithProjection(
									    CLIPTextConfig(
									        bos_token_id=0,   eos_token_id=2,   hidden_size=snake_case__,   projection_dim=snake_case__,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   )  )

									torch.manual_seed(0  )
									lowercase_	:   Tuple  				=     PriorTransformer(
									    num_attention_heads=2,   attention_head_dim=12,   embedding_dim=snake_case__,   num_layers=1,   )

									torch.manual_seed(0  )
									lowercase_	:   Optional[Any]  				=     DDPMScheduler(
									    variance_type="""fixed_small_log""",   prediction_type="""sample""",   num_train_timesteps=10_00,   clip_sample=snake_case__,   clip_sample_range=5.0,   beta_schedule="""squaredcos_cap_v2""",   )

									# regular denoising components

									torch.manual_seed(0  )
									lowercase_	:   Union[str, Any]  				=     StableUnCLIPImageNormalizer(embedding_dim=snake_case__  )
									lowercase_	:   str  				=     DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""  )

									torch.manual_seed(0  )
									lowercase_	:   str  				=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									torch.manual_seed(0  )
									lowercase_	:   List[Any]  				=     CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0,   eos_token_id=2,   hidden_size=snake_case__,   projection_dim=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   )  )

									torch.manual_seed(0  )
									lowercase_	:   Optional[int]  				=     UNetaDConditionModel(
									    sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D"""),   up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D"""),   block_out_channels=(32, 64),   attention_head_dim=(2, 4),   class_embed_type="""projection""",   projection_class_embeddings_input_dim=embedder_projection_dim * 2,   cross_attention_dim=snake_case__,   layers_per_block=1,   upcast_attention=snake_case__,   use_linear_projection=snake_case__,   )

									torch.manual_seed(0  )
									lowercase_	:   Dict  				=     DDIMScheduler(
									    beta_schedule="""scaled_linear""",   beta_start=0.00085,   beta_end=0.012,   prediction_type="""v_prediction""",   set_alpha_to_one=snake_case__,   steps_offset=1,   )

									torch.manual_seed(0  )
									lowercase_	:   str  				=     AutoencoderKL()

									lowercase_	:   str  				=     {
									    # prior components
									    """prior_tokenizer""": prior_tokenizer,
									    """prior_text_encoder""": prior_text_encoder,
									    """prior""": prior,
									    """prior_scheduler""": prior_scheduler,
									    # image noising components
									    """image_normalizer""": image_normalizer,
									    """image_noising_scheduler""": image_noising_scheduler,
									    # regular denoising components
									    """tokenizer""": tokenizer,
									    """text_encoder""": text_encoder,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									}

									return components






		def 						snake_case__				(							self,   snake_case__,   snake_case__=0  )       -> str:






									"""simple docstring"""
									if str(snake_case__  ).startswith("""mps"""  ):
																lowercase_	:   Tuple  				=     torch.manual_seed(snake_case__  )
									else:
																lowercase_	:   int  				=     torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
									lowercase_	:   Dict  				=     {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """prior_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






		def 						snake_case__				(							self  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=snake_case__  )






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     torch_device in ["""cpu""", """mps"""]

									self._test_inference_batch_single_identical(test_max_difference=snake_case__  )




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






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""  )

									lowercase_	:   List[Any]  				=     StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""",   torch_dtype=torch.floataa  )
									pipe.to(snake_case__  )
									pipe.set_progress_bar_config(disable=snake_case__  )
									# stable unclip will oom when integration tests are run on a V100,
									# so turn on memory savings
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									lowercase_	:   Any  				=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
									lowercase_	:   Dict  				=     pipe("""anime turle""",   generator=snake_case__,   output_type="""np"""  )

									lowercase_	:   str  				=     output.images[0]

									assert image.shape == (7_68, 7_68, 3)

									assert_mean_pixel_difference(snake_case__,   snake_case__  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									lowercase_	:   str  				=     StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""",   torch_dtype=torch.floataa  )
									lowercase_	:   Optional[int]  				=     pipe.to(snake_case__  )
									pipe.set_progress_bar_config(disable=snake_case__  )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									lowercase_	:   List[Any]  				=     pipe(
									    """anime turtle""",   prior_num_inference_steps=2,   num_inference_steps=2,   output_type="""np""",   )

									lowercase_	:   Optional[int]  				=     torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9