'''simple docstring'''

from jiwer import compute_measures

import datasets


UpperCamelCase__  :						str					=      '\\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'

UpperCamelCase__  :						Union[str, Any]					=      '\\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'

UpperCamelCase__  :						Dict					=      '\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 ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''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       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=False				)      ->      List[Any]:



         '''simple docstring'''



         if concatenate_texts:
            return compute_measures(lowerCamelCase_       ,lowerCamelCase_				)["wer"]
         else:
            UpperCAmelCase__				:					List[Any] 					=	0
            UpperCAmelCase__				:					List[Any] 					=	0
            for prediction, reference in zip(lowerCamelCase_       ,lowerCamelCase_				):
               UpperCAmelCase__				:					Optional[Any] 					=	compute_measures(lowerCamelCase_       ,lowerCamelCase_				)
               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 PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= KandinskyInpaintPipeline
      UpperCAmelCase_   :   str   				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_   :   Optional[int]   				= False




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return self.time_input_dim




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
         return tokenizer




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Any 					=	MCLIPConfig(
             numDims=self.cross_attention_dim       ,transformerDimensions=self.text_embedder_hidden_size       ,hidden_size=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_hidden_layers=5       ,vocab_size=1005       ,)

         UpperCAmelCase__				:					Any 					=	MultilingualCLIP(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	text_encoder.eval()

         return text_encoder




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



         '''simple docstring'''



         torch.manual_seed(0				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_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''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         UpperCAmelCase__				:					List[str] 					=	UNetaDConditionModel(**lowerCamelCase_				)
         return model




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



         '''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 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	VQModel(**self.dummy_movq_kwargs				)
         return model




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.dummy_text_encoder
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_tokenizer
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_unet
         UpperCAmelCase__				:					Any 					=	self.dummy_movq

         UpperCAmelCase__				:					Tuple 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0085       ,beta_end=0.012       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=lowerCamelCase_       ,)

         UpperCAmelCase__				:					Dict 					=	{
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1				)				).to(lowerCamelCase_				)
         # create init_image
         UpperCAmelCase__				:					str 					=	floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					Union[str, Any] 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
         # create mask
         UpperCAmelCase__				:					List[str] 					=	np.ones((64, 64)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	0

         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Dict 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	{
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()

         UpperCAmelCase__				:					Tuple 					=	self.pipeline_class(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	pipe.to(lowerCamelCase_				)

         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	pipe(**self.get_dummy_inputs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[Any] 					=	output.images

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(
             **self.get_dummy_inputs(lowerCamelCase_				)       ,return_dict=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         UpperCAmelCase__				:					int 					=	image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''				)

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

         UpperCAmelCase__				:					List[Any] 					=	np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

         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()}'''




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




@slow
@require_torch_gpu
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


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



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

         UpperCAmelCase__				:					str 					=	load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.ones((768, 768)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					int 					=	0

         UpperCAmelCase__				:					List[Any] 					=	'''a hat'''

         UpperCAmelCase__				:					str 					=	KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa				)
         pipe_prior.to(lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Tuple 					=	pipeline.to(lowerCamelCase_				)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	pipe_prior(
             lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase__				:					Optional[Any] 					=	pipeline(
             lowerCamelCase_       ,image=lowerCamelCase_       ,mask_image=lowerCamelCase_       ,image_embeds=lowerCamelCase_       ,negative_image_embeds=lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase__				:					str 					=	output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(lowerCamelCase_       ,lowerCamelCase_				)




