


from jiwer import compute_measures

import datasets


lowerCAmelCase	=							'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

lowerCAmelCase	=							'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

lowerCAmelCase	=							'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


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

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       str:





									"""simple docstring"""







									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/jitsi/jiwer/''']   ,						reference_urls=[
									        '''https://en.wikipedia.org/wiki/Word_error_rate''',
									    ]   ,						)


							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Any=None   ,						UpperCamelCase_:     str=None   ,						UpperCamelCase_:     Tuple=False							)      ->       str:





									"""simple docstring"""







									if concatenate_texts:
											return compute_measures(UpperCamelCase_   ,						UpperCamelCase_							)["wer"]
									else:
											lowercase__						=				0
											lowercase__						=				0
											for prediction, reference in zip(UpperCamelCase_   ,						UpperCamelCase_							):
													lowercase__						=				compute_measures(UpperCamelCase_   ,						UpperCamelCase_							)
													incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
													total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Union[str, Any]  				=			ShapEImgaImgPipeline
							_lowercase     :      Optional[Any]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase     :      Tuple  				=			False

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       int:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									return 8

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,						image_size=64   ,						projection_dim=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_channels=3   ,						num_hidden_layers=5   ,						patch_size=1   ,						)

									lowercase__						=				CLIPVisionModel(UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				CLIPImageProcessor(
									    crop_size=224   ,						do_center_crop=UpperCamelCase_   ,						do_normalize=UpperCamelCase_   ,						do_resize=UpperCamelCase_   ,						image_mean=[0.48145466, 0.4578275, 0.40821073]   ,						image_std=[0.26862954, 0.26130258, 0.27577711]   ,						resample=3   ,						size=224   ,						)

									return image_processor

							@property
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 16,
									    '''embedding_dim''': self.time_input_dim,
									    '''num_embeddings''': 32,
									    '''embedding_proj_dim''': self.text_embedder_hidden_size,
									    '''time_embed_dim''': self.time_embed_dim,
									    '''num_layers''': 1,
									    '''clip_embed_dim''': self.time_input_dim * 2,
									    '''additional_embeddings''': 0,
									    '''time_embed_act_fn''': '''gelu''',
									    '''norm_in_type''': '''layer''',
									    '''embedding_proj_norm_type''': '''layer''',
									    '''encoder_hid_proj_type''': None,
									    '''added_emb_type''': None,
									}

									lowercase__						=				PriorTransformer(**UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''param_shapes''': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    '''d_latent''': self.time_input_dim,
									    '''d_hidden''': self.renderer_dim,
									    '''n_output''': 12,
									    '''background''': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									lowercase__						=				ShapERenderer(**UpperCamelCase_							)
									return model

							def 				lowerCamelCase_			(							self:     str							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.dummy_prior
									lowercase__						=				self.dummy_image_encoder
									lowercase__						=				self.dummy_image_processor
									lowercase__						=				self.dummy_renderer

									lowercase__						=				HeunDiscreteScheduler(
									    beta_schedule='''exp'''   ,						num_train_timesteps=1_024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase_   ,						clip_sample=UpperCamelCase_   ,						clip_sample_range=1.0   ,						)
									lowercase__						=				{
									    '''prior''': prior,
									    '''image_encoder''': image_encoder,
									    '''image_processor''': image_processor,
									    '''renderer''': renderer,
									    '''scheduler''': scheduler,
									}

									return components

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[int]=0							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)

									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''image''': input_image,
									    '''generator''': generator,
									    '''num_inference_steps''': 1,
									    '''frame_size''': 32,
									    '''output_type''': '''np''',
									}
									return inputs

							def 				lowerCamelCase_			(							self:     int							)      ->       str:





									"""simple docstring"""







									lowercase__						=				'''cpu'''

									lowercase__						=				self.get_dummy_components()

									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
									lowercase__						=				output.images[0]
									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									lowercase__						=				np.array(
									    [
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									    ]							)

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

							def 				lowerCamelCase_			(							self:     int							)      ->       int:





									"""simple docstring"""







									self._test_inference_batch_consistent(batch_sizes=[1, 2]							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				torch_device == '''cpu'''
									lowercase__						=				True
									self._test_inference_batch_single_identical(
									    batch_size=2   ,						test_max_difference=UpperCamelCase_   ,						relax_max_difference=UpperCamelCase_   ,						)


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





									"""simple docstring"""







									lowercase__						=				self.get_dummy_components()
									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				1
									lowercase__						=				2

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)

									for key in inputs.keys():
											if key in self.batch_params:
													lowercase__						=				batch_size * [inputs[key]]

									lowercase__						=				pipe(**UpperCamelCase_   ,						num_images_per_prompt=UpperCamelCase_							)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt


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

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







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


							def 				lowerCamelCase_			(							self:     str							)      ->       str:





									"""simple docstring"""







									lowercase__						=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''							)
									lowercase__						=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/shap_e/test_shap_e_img2img_out.npy'''							)
									lowercase__						=				ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)

									lowercase__						=				pipe(
									    UpperCamelCase_   ,						generator=UpperCamelCase_   ,						guidance_scale=3.0   ,						num_inference_steps=64   ,						frame_size=64   ,						output_type='''np'''   ,						).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(UpperCamelCase_   ,						UpperCamelCase_							)


