




"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()






class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def       A_					(						self						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					1
							_lowerCamelCase  :    List[Any]					=					3
							_lowerCamelCase  :    str					=					(32, 32)

							_lowerCamelCase  :    List[Any]					=					floats_tensor((batch_size, num_channels) + sizes						,   rng=random.Random(0						)						).to(lowercase						)
							return image
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    Optional[Any]					=					UNetaDConditionModel(
							    block_out_channels=(32, 32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=7						,   out_channels=4						,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')						,   up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')						,   cross_attention_dim=32						,   attention_head_dim=8						,   use_linear_projection=lowercase						,   only_cross_attention=(True, True, False)						,   num_class_embeds=100						,   )
							return model
			@property
			def       A_					(						self						):
							torch.manual_seed(0						)
							_lowerCamelCase  :    Dict					=					AutoencoderKL(
							    block_out_channels=[32, 32, 64]						,   in_channels=3						,   out_channels=3						,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']						,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,   latent_channels=4						,   )
							return model
			@property
			def       A_					(						self						):
							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-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1000						,   hidden_act='gelu'						,   projection_dim=512						,   )
							return CLIPTextModel(lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
							_lowerCamelCase  :    Any					=					self.dummy_cond_unet_upscale
							_lowerCamelCase  :    str					=					DDPMScheduler()
							_lowerCamelCase  :    Dict					=					DDIMScheduler(prediction_type='v_prediction'						)
							_lowerCamelCase  :    Optional[int]					=					self.dummy_vae
							_lowerCamelCase  :    Tuple					=					self.dummy_text_encoder
							_lowerCamelCase  :    Optional[Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							_lowerCamelCase  :    Optional[Any]					=					self.dummy_image.cpu().permute(0						,   2						,   3						,   1						)[0]
							_lowerCamelCase  :    str					=					Image.fromarray(np.uinta(lowercase						)						).convert('RGB'						).resize((64, 64)						)

							# make sure here that pndm scheduler skips prk
							_lowerCamelCase  :    Any					=					StableDiffusionUpscalePipeline(
							    unet=lowercase						,   low_res_scheduler=lowercase						,   scheduler=lowercase						,   vae=lowercase						,   text_encoder=lowercase						,   tokenizer=lowercase						,   max_noise_level=350						,   )
							_lowerCamelCase  :    List[Any]					=					sd_pipe.to(lowercase						)
							sd_pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					'A painting of a squirrel eating a burger'
							_lowerCamelCase  :    Optional[int]					=					torch.Generator(device=lowercase						).manual_seed(0						)
							_lowerCamelCase  :    Any					=					sd_pipe(
							    [prompt]						,   image=lowercase						,   generator=lowercase						,   guidance_scale=6.0						,   noise_level=20						,   num_inference_steps=2						,   output_type='np'						,   )

							_lowerCamelCase  :    Optional[int]					=					output.images

							_lowerCamelCase  :    List[str]					=					torch.Generator(device=lowercase						).manual_seed(0						)
							_lowerCamelCase  :    List[Any]					=					sd_pipe(
							    [prompt]						,   image=lowercase						,   generator=lowercase						,   guidance_scale=6.0						,   noise_level=20						,   num_inference_steps=2						,   output_type='np'						,   return_dict=lowercase						,   )[0]

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

							_lowerCamelCase  :    str					=					low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							_lowerCamelCase  :    Dict					=					np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]						)

							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       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
							_lowerCamelCase  :    Dict					=					self.dummy_cond_unet_upscale
							_lowerCamelCase  :    Dict					=					DDPMScheduler()
							_lowerCamelCase  :    Tuple					=					DDIMScheduler(prediction_type='v_prediction'						)
							_lowerCamelCase  :    Dict					=					self.dummy_vae
							_lowerCamelCase  :    int					=					self.dummy_text_encoder
							_lowerCamelCase  :    List[str]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							_lowerCamelCase  :    Optional[int]					=					self.dummy_image.cpu().permute(0						,   2						,   3						,   1						)[0]
							_lowerCamelCase  :    Optional[Any]					=					Image.fromarray(np.uinta(lowercase						)						).convert('RGB'						).resize((64, 64)						)

							# make sure here that pndm scheduler skips prk
							_lowerCamelCase  :    Any					=					StableDiffusionUpscalePipeline(
							    unet=lowercase						,   low_res_scheduler=lowercase						,   scheduler=lowercase						,   vae=lowercase						,   text_encoder=lowercase						,   tokenizer=lowercase						,   max_noise_level=350						,   )
							_lowerCamelCase  :    Dict					=					sd_pipe.to(lowercase						)
							sd_pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    List[str]					=					'A painting of a squirrel eating a burger'
							_lowerCamelCase  :    Tuple					=					sd_pipe(
							    2 * [prompt]						,   image=2 * [low_res_image]						,   guidance_scale=6.0						,   noise_level=20						,   num_inference_steps=2						,   output_type='np'						,   )
							_lowerCamelCase  :    Dict					=					output.images
							assert image.shape[0] == 2

							_lowerCamelCase  :    int					=					torch.Generator(device=lowercase						).manual_seed(0						)
							_lowerCamelCase  :    Union[str, Any]					=					sd_pipe(
							    [prompt]						,   image=lowercase						,   generator=lowercase						,   num_images_per_prompt=2						,   guidance_scale=6.0						,   noise_level=20						,   num_inference_steps=2						,   output_type='np'						,   )
							_lowerCamelCase  :    Optional[int]					=					output.images
							assert image.shape[0] == 2






			@unittest.skipIf(torch_device != 'cuda'						,   'This test requires a GPU'						)
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.dummy_cond_unet_upscale
							_lowerCamelCase  :    str					=					DDPMScheduler()
							_lowerCamelCase  :    Dict					=					DDIMScheduler(prediction_type='v_prediction'						)
							_lowerCamelCase  :    Any					=					self.dummy_vae
							_lowerCamelCase  :    Dict					=					self.dummy_text_encoder
							_lowerCamelCase  :    List[Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							_lowerCamelCase  :    List[str]					=					self.dummy_image.cpu().permute(0						,   2						,   3						,   1						)[0]
							_lowerCamelCase  :    List[str]					=					Image.fromarray(np.uinta(lowercase						)						).convert('RGB'						).resize((64, 64)						)

							# put models in fp16, except vae as it overflows in fp16
							_lowerCamelCase  :    Dict					=					unet.half()
							_lowerCamelCase  :    Optional[int]					=					text_encoder.half()

							# make sure here that pndm scheduler skips prk
							_lowerCamelCase  :    Optional[int]					=					StableDiffusionUpscalePipeline(
							    unet=lowercase						,   low_res_scheduler=lowercase						,   scheduler=lowercase						,   vae=lowercase						,   text_encoder=lowercase						,   tokenizer=lowercase						,   max_noise_level=350						,   )
							_lowerCamelCase  :    str					=					sd_pipe.to(lowercase						)
							sd_pipe.set_progress_bar_config(disable=lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					'A painting of a squirrel eating a burger'
							_lowerCamelCase  :    int					=					torch.manual_seed(0						)
							_lowerCamelCase  :    str					=					sd_pipe(
							    [prompt]						,   image=lowercase						,   generator=lowercase						,   num_inference_steps=2						,   output_type='np'						,   ).images

							_lowerCamelCase  :    List[Any]					=					low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)






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






			'''simple docstring'''



			def       A_					(						self						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'						)
							_lowerCamelCase  :    Optional[Any]					=					load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
							    '/upsampled_cat.npy'						)

							_lowerCamelCase  :    int					=					'stabilityai/stable-diffusion-x4-upscaler'
							_lowerCamelCase  :    Tuple					=					StableDiffusionUpscalePipeline.from_pretrained(lowercase						)
							pipe.to(lowercase						)
							pipe.set_progress_bar_config(disable=lowercase						)
							pipe.enable_attention_slicing()

							_lowerCamelCase  :    List[Any]					=					'a cat sitting on a park bench'

							_lowerCamelCase  :    Union[str, Any]					=					torch.manual_seed(0						)
							_lowerCamelCase  :    Tuple					=					pipe(
							    prompt=lowercase						,   image=lowercase						,   generator=lowercase						,   output_type='np'						,   )
							_lowerCamelCase  :    Optional[int]					=					output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image						).max() < 1E-3
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'						)
							_lowerCamelCase  :    Optional[Any]					=					load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
							    '/upsampled_cat_fp16.npy'						)

							_lowerCamelCase  :    Optional[Any]					=					'stabilityai/stable-diffusion-x4-upscaler'
							_lowerCamelCase  :    int					=					StableDiffusionUpscalePipeline.from_pretrained(
							    lowercase						,   torch_dtype=torch.floataa						,   )
							pipe.to(lowercase						)
							pipe.set_progress_bar_config(disable=lowercase						)
							pipe.enable_attention_slicing()

							_lowerCamelCase  :    Optional[Any]					=					'a cat sitting on a park bench'

							_lowerCamelCase  :    List[Any]					=					torch.manual_seed(0						)
							_lowerCamelCase  :    List[Any]					=					pipe(
							    prompt=lowercase						,   image=lowercase						,   generator=lowercase						,   output_type='np'						,   )
							_lowerCamelCase  :    int					=					output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image						).max() < 5E-1






			def       A_					(						self						):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							_lowerCamelCase  :    int					=					load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-upscale/low_res_cat.png'						)

							_lowerCamelCase  :    int					=					'stabilityai/stable-diffusion-x4-upscaler'
							_lowerCamelCase  :    List[Any]					=					StableDiffusionUpscalePipeline.from_pretrained(
							    lowercase						,   torch_dtype=torch.floataa						,   )
							pipe.to(lowercase						)
							pipe.set_progress_bar_config(disable=lowercase						)
							pipe.enable_attention_slicing(1						)
							pipe.enable_sequential_cpu_offload()

							_lowerCamelCase  :    List[str]					=					'a cat sitting on a park bench'

							_lowerCamelCase  :    int					=					torch.manual_seed(0						)
							_lowerCamelCase  :    Tuple					=					pipe(
							    prompt=lowercase						,   image=lowercase						,   generator=lowercase						,   num_inference_steps=5						,   output_type='np'						,   )

							_lowerCamelCase  :    Union[str, Any]					=					torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9





"""simple docstring"""






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase__  		=		"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

lowercase__  		=		"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

lowercase__  		=		"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""






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






			'''simple docstring'''



			def       A_					(						self						):
							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'						),
							        }						)						,   )






			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = 1						,   lowercase = 4						,   ):
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=lowercase						,   hypotheses=lowercase						,   min_len=lowercase						,   max_len=lowercase						)
							}