
import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class       _snake_case					(     unittest.TestCase				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		Optional[Any]	   =     UNetaDModel(
				    block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=3						,   out_channels=3						,   down_block_types=("DownBlock2D", "AttnDownBlock2D")						,   up_block_types=("AttnUpBlock2D", "UpBlock2D")						,   )
				return model







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		Optional[Any]	   =     VQModel(
				    block_out_channels=[32, 64]						,   in_channels=3						,   out_channels=3						,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,   latent_channels=3						,   )
				return model







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		Tuple	   =     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=1_000						,   )
				return CLIPTextModel(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.dummy_uncond_unet
				__magic_name__		:		Tuple	   =     DDIMScheduler()
				__magic_name__		:		List[Any]	   =     self.dummy_vq_model

				__magic_name__		:		Union[str, Any]	   =     LDMPipeline(unet=_a						,   vqvae=_a						,   scheduler=_a		)
				ldm.to(_a		)
				ldm.set_progress_bar_config(disable=_a		)

				__magic_name__		:		str	   =     torch.manual_seed(0		)
				__magic_name__		:		str	   =     ldm(generator=_a						,   num_inference_steps=2						,   output_type="numpy"		).images

				__magic_name__		:		int	   =     torch.manual_seed(0		)
				__magic_name__		:		List[Any]	   =     ldm(generator=_a						,   num_inference_steps=2						,   output_type="numpy"						,   return_dict=_a		)[0]

				__magic_name__		:		List[str]	   =     image[0, -3:, -3:, -1]
				__magic_name__		:		Dict	   =     image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				__magic_name__		:		Any	   =     np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]		)
				__magic_name__		:		Tuple	   =     1e-2 if torch_device != "mps" else 3e-2

				assert np.abs(image_slice.flatten() - expected_slice		).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < tolerance







@slow
@require_torch
class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"		)
				ldm.to(_a		)
				ldm.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Optional[int]	   =     torch.manual_seed(0		)
				__magic_name__		:		Union[str, Any]	   =     ldm(generator=_a						,   num_inference_steps=5						,   output_type="numpy"		).images

				__magic_name__		:		Optional[Any]	   =     image[0, -3:, -3:, -1]

				assert image.shape == (1, 256, 256, 3)
				__magic_name__		:		List[Any]	   =     np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]		)
				__magic_name__		:		int	   =     1e-2 if torch_device != "mps" else 3e-2

				assert np.abs(image_slice.flatten() - expected_slice		).max() < tolerance


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


snake_case :		List[Any]							      =  "\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return datasets.MetricInfo(
				    description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"						,   id="sequence"		),
				            "references": datasets.Value("string"						,   id="sequence"		),
				        }		)						,   codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]						,   reference_urls=[
				        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
				        "https://github.com/google-research/google-research/tree/master/rouge",
				    ]						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

				if use_aggregator:
					__magic_name__		:		Any	   =     aggregator.aggregate()
				else:
					__magic_name__		:		List[Any]	   =     {}
					for key in scores[0]:
						__magic_name__		:		str	   =     [score[key] for score in scores]

				return result
