

"""simple docstring"""






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        _lowercase					(     datasets.Metric     ):







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=False       ):
      if concatenate_texts:
       return compute_measures(UpperCamelCase_ ,						UpperCamelCase_       )["wer"]
      else:
       __magic_name__     =      0
       __magic_name__     =      0
       for prediction, reference in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
        __magic_name__     =      compute_measures(UpperCamelCase_ ,						UpperCamelCase_       )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
       return incorrect / total




"""simple docstring"""






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      [tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]						)
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       StableDiffusionLatentUpscalePipeline
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     _lowerCamelCase     =       PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase     =       frozenset(
         []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _lowerCamelCase     =       frozenset([]     )

     _lowerCamelCase     =       True







     @property
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      1
      __magic_name__     =      4
      __magic_name__     =      (16, 16)

      __magic_name__     =      floats_tensor((batch_size, num_channels) + sizes ,						rng=random.Random(0       )       ).to(UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      UNetaDConditionModel(
          act_fn='''gelu''' ,						attention_head_dim=8 ,						norm_num_groups=UpperCamelCase_ ,						block_out_channels=[32, 32, 64, 64] ,						time_cond_proj_dim=160 ,						conv_in_kernel=1 ,						conv_out_kernel=1 ,						cross_attention_dim=32 ,						down_block_types=(
              '''KDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
          ) ,						in_channels=8 ,						mid_block_type=UpperCamelCase_ ,						only_cross_attention=UpperCamelCase_ ,						out_channels=5 ,						resnet_time_scale_shift='''scale_shift''' ,						time_embedding_type='''fourier''' ,						timestep_post_act='''gelu''' ,						up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') ,						)
      __magic_name__     =      AutoencoderKL(
          block_out_channels=[32, 32, 64, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=[
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
          ] ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,						latent_channels=4 ,						)
      __magic_name__     =      EulerDiscreteScheduler(prediction_type='''sample'''       )
      __magic_name__     =      CLIPTextConfig(
          bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-0_5 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1000 ,						hidden_act='''quick_gelu''' ,						projection_dim=512 ,						)
      __magic_name__     =      CLIPTextModel(UpperCamelCase_       )
      __magic_name__     =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

      __magic_name__     =      {
          '''unet''': model.eval(),
          '''vae''': vae.eval(),
          '''scheduler''': scheduler,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
      }

      return components







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0       ):
      if str(UpperCamelCase_       ).startswith('''mps'''       ):
       __magic_name__     =      torch.manual_seed(UpperCamelCase_       )
      else:
       __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': self.dummy_image.cpu(),
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''cpu'''

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

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

      self.assertEqual(image.shape ,						(1, 256, 256, 3)       )
      __magic_name__     =      np.array(
          [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]       )
      __magic_name__     =      np.abs(image_slice.flatten() - expected_slice       ).max()
      self.assertLessEqual(UpperCamelCase_ ,						1E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_attention_slicing_forward_pass(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_cpu_offload_forward_pass(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_inference_batch_single_identical(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_local(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_optional_components(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [
          '''DDIMScheduler''',
          '''DDPMScheduler''',
          '''PNDMScheduler''',
          '''HeunDiscreteScheduler''',
          '''EulerAncestralDiscreteScheduler''',
          '''KDPM2DiscreteScheduler''',
          '''KDPM2AncestralDiscreteScheduler''',
          '''DPMSolverSDEScheduler''',
      ]
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )

      # make sure that PNDM does not need warm-up
      pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_       )

      pipe.to(UpperCamelCase_       )
      pipe.set_progress_bar_config(disable=UpperCamelCase_       )
      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      2

      __magic_name__     =      []
      for scheduler_enum in KarrasDiffusionSchedulers:
       if scheduler_enum.name in skip_schedulers:
        # no sigma schedulers are not supported
        # no schedulers
        continue

       __magic_name__     =      getattr(UpperCamelCase_ ,						scheduler_enum.name       )
       __magic_name__     =      scheduler_cls.from_config(pipe.scheduler.config       )
       __magic_name__     =      pipe(**UpperCamelCase_       )[0]
       outputs.append(UpperCamelCase_       )

      assert check_same_shape(UpperCamelCase_       )


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







     def 	lowerCAmelCase__    (      self       ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' ,						torch_dtype=torch.floataa       )
      pipe.to('''cuda'''       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

      __magic_name__     =      pipe(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						output_type='''latent'''       ).images

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''       )
      assert np.abs((expected_image - image).mean()       ) < 5E-2







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

      __magic_name__     =      load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''       )

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''       )
      assert np.abs((expected_image - image).max()       ) < 5E-2

