




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__a							     =					'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__a							     =					'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__a							     =					'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'

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






    """simple docstring"""





    def 				a__ ( self    )					->						Tuple:
         if version.parse(scb.__version__    ) < version.parse('''1.4.12'''    ):
              raise ImportWarning(
                  '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                  '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''    )
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,citation=_CITATION  ,homepage='''http://www.cs.umd.edu/~snover/tercom/'''  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''  ,id='''sequence'''    ),
                     '''references''': datasets.Sequence(datasets.Value('''string'''  ,id='''sequence'''    )  ,id='''references'''    ),
                 }    )  ,codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']  ,reference_urls=[
                 '''https://github.com/jhclark/tercom''',
             ]  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,)					->						Optional[Any]:
         UpperCAmelCase_     :				Tuple  =     len(references[0]    )
         if any(len(_SCREAMING_SNAKE_CASE    ) != references_per_prediction for refs in references    ):
              raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
         UpperCAmelCase_     :				str  =     [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE    )]

         UpperCAmelCase_     :				Tuple  =     TER(
             normalized=_SCREAMING_SNAKE_CASE  ,no_punct=_SCREAMING_SNAKE_CASE  ,asian_support=_SCREAMING_SNAKE_CASE  ,case_sensitive=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				Optional[Any]  =     sb_ter.corpus_score(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	KandinskyVaaPipeline
 _lowerCamelCase								=	[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 _lowerCamelCase								=	["""image_embeds""", """negative_image_embeds"""]
 _lowerCamelCase								=	[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCamelCase								=	False

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 32

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 32

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.time_input_dim

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.time_input_dim * 4

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 100

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )

     __A       :		Optional[Any]							=	{
         '''in_channels''': 4,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''image''',
         '''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,
         '''encoder_hid_dim''': self.text_embedder_hidden_size,
         '''encoder_hid_dim_type''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     __A       :		Optional[Any]							=	UNetaDConditionModel(**__lowerCamelCase    )
     return model

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		Tuple							=	VQModel(**self.dummy_movq_kwargs    )
     return model

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	self.dummy_unet
     __A       :		Optional[int]							=	self.dummy_movq

     __A       :		List[Any]							=	DDIMScheduler(
         num_train_timesteps=1000   ,		beta_schedule='''linear'''   ,		beta_start=0.0_0_0_8_5   ,		beta_end=0.0_1_2   ,		clip_sample=__lowerCamelCase   ,		set_alpha_to_one=__lowerCamelCase   ,		steps_offset=1   ,		prediction_type='''epsilon'''   ,		thresholding=__lowerCamelCase   ,		)

     __A       :		Optional[int]							=	{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }
     return components

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=0    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	floats_tensor((1, self.text_embedder_hidden_size)   ,		rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
     __A       :		List[Any]							=	floats_tensor((1, self.text_embedder_hidden_size)   ,		rng=random.Random(seed + 1    )    ).to(
         __lowerCamelCase    )
     if str(__lowerCamelCase    ).startswith('''mps'''    ):
         __A       :		Optional[Any]							=	torch.manual_seed(__lowerCamelCase    )
     else:
         __A       :		Dict							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
     __A       :		Dict							=	{
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''guidance_scale''': 4.0,
         '''num_inference_steps''': 2,
         '''output_type''': '''np''',
     }
     return inputs







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	'''cpu'''

     __A       :		str							=	self.get_dummy_components()

     __A       :		Dict							=	self.pipeline_class(**__lowerCamelCase    )
     __A       :		Tuple							=	pipe.to(__lowerCamelCase    )

     pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		List[str]							=	pipe(**self.get_dummy_inputs(__lowerCamelCase    )    )
     __A       :		Tuple							=	output.images

     __A       :		Tuple							=	pipe(
         **self.get_dummy_inputs(__lowerCamelCase    )   ,		return_dict=__lowerCamelCase   ,		)[0]

     __A       :		Dict							=	image[0, -3:, -3:, -1]
     __A       :		Any							=	image_from_tuple[0, -3:, -3:, -1]

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

     __A       :		Optional[int]							=	np.array(
         [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]    )

     assert (
         np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






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





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''    )

     __A       :		Optional[Any]							=	KandinskyVaaPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''   ,		torch_dtype=torch.floataa    )
     pipe_prior.to(__lowerCamelCase    )

     __A       :		int							=	KandinskyVaaPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-decoder'''   ,		torch_dtype=torch.floataa    )
     __A       :		Union[str, Any]							=	pipeline.to(__lowerCamelCase    )
     pipeline.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		List[Any]							=	'''red cat, 4k photo'''

     __A       :		Union[str, Any]							=	torch.Generator(device='''cuda'''    ).manual_seed(0    )
     __A					,			__A       :		List[str]							=	pipe_prior(
         __lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=5   ,		negative_prompt=''''''   ,		).to_tuple()

     __A       :		Any							=	torch.Generator(device='''cuda'''    ).manual_seed(0    )
     __A       :		Optional[int]							=	pipeline(
         image_embeds=__lowerCamelCase   ,		negative_image_embeds=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=100   ,		output_type='''np'''   ,		)

     __A       :		Union[str, Any]							=	output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(__lowerCamelCase   ,		__lowerCamelCase    )




