from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


lowercase_     :   int 				=				''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):






      class 		_lowerCamelCase		(   tr.AbstractTransform    ):
          def __init__(  self , lowerCAmelCase = " "   )       ->					Optional[Any]:
           SCREAMING_SNAKE_CASE__:   Any=      sentence_delimiter
          def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
           return list(lowerCAmelCase   )



          def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Optional[int]:
           SCREAMING_SNAKE_CASE__:   List[Any]=      []
           for sent_idx, sentence in enumerate(lowerCAmelCase   ):
            chars.extend(self.process_string(lowerCAmelCase   )   )
            if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCAmelCase   ) - 1:
             chars.append(self.sentence_delimiter   )
           return chars
      lowercase_     :   Optional[int] 				=				tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      lowercase_     :   List[Any] 				=				tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


lowercase_     :   Dict 				=				'\\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'

lowercase_     :   Optional[Any] 				=				'\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (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 characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

lowercase_     :   int 				=				'\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character 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    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,     _KWARGS_DESCRIPTION    )
class 		_lowerCamelCase		(   datasets.Metric    ):
    def 						UpperCamelCase_				(  self   )       ->					int:
     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''',
             '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
         ] , )



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False   )       ->					Any:
     if concatenate_texts:
      return jiwer.compute_measures(
          lowerCAmelCase , lowerCAmelCase , truth_transform=lowerCAmelCase , hypothesis_transform=lowerCAmelCase , )["wer"]

     SCREAMING_SNAKE_CASE__:   Dict=      0
     SCREAMING_SNAKE_CASE__:   Tuple=      0
     for prediction, reference in zip(lowerCAmelCase , lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      jiwer.compute_measures(
          lowerCAmelCase , lowerCAmelCase , truth_transform=lowerCAmelCase , hypothesis_transform=lowerCAmelCase , )
      incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
      total += measures["substitutions"] + measures["deletions"] + measures["hits"]

     return incorrect / total


import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      AltDiffusionPipeline
    __a  =      TEXT_TO_IMAGE_PARAMS
    __a  =      TEXT_TO_IMAGE_BATCH_PARAMS
    __a  =      TEXT_TO_IMAGE_IMAGE_PARAMS
    __a  =      TEXT_TO_IMAGE_IMAGE_PARAMS
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      UNetaDConditionModel(
         block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
     SCREAMING_SNAKE_CASE__:   Tuple=      DDIMScheduler(
         beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCAmelCase , set_alpha_to_one=lowerCAmelCase , )
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      AutoencoderKL(
         block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )

     # TODO: address the non-deterministic text encoder (fails for save-load tests)
     # torch.manual_seed(0)
     # text_encoder_config = RobertaSeriesConfig(
     #     hidden_size=32,
     #     project_dim=32,
     #     intermediate_size=37,
     #     layer_norm_eps=1e-05,
     #     num_attention_heads=4,
     #     num_hidden_layers=5,
     #     vocab_size=5002,
     # )
     # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      CLIPTextConfig(
         bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
     SCREAMING_SNAKE_CASE__:   List[Any]=      CLIPTextModel(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      77

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''safety_checker''': None,
         '''feature_extractor''': None,
     }
     return components
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0   )       ->					List[Any]:
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Any=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 6.0,
         '''output_type''': '''numpy''',
     }
     return inputs
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__:   Tuple=      self.get_dummy_components()
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      RobertaSeriesConfig(
         hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
     # TODO: remove after fixing the non-deterministic text encoder
     SCREAMING_SNAKE_CASE__:   List[str]=      RobertaSeriesModelWithTransformation(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      text_encoder

     SCREAMING_SNAKE_CASE__:   List[Any]=      AltDiffusionPipeline(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      self.get_dummy_inputs(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      '''A photo of an astronaut'''
     SCREAMING_SNAKE_CASE__:   Any=      alt_pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      output.images
     SCREAMING_SNAKE_CASE__:   Tuple=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE__:   Any=      np.array(
         [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]   )

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



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[str]=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__:   Any=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__:   List[str]=      PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      RobertaSeriesConfig(
         hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
     # TODO: remove after fixing the non-deterministic text encoder
     SCREAMING_SNAKE_CASE__:   str=      RobertaSeriesModelWithTransformation(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      text_encoder
     SCREAMING_SNAKE_CASE__:   Any=      AltDiffusionPipeline(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_dummy_inputs(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      alt_pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      output.images
     SCREAMING_SNAKE_CASE__:   Tuple=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE__:   List[Any]=      np.array(
         [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]   )

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






@slow
@require_torch_gpu
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__:   Any=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      '''A painting of a squirrel eating a burger'''
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      alt_pipe([prompt] , generator=lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      output.images

     SCREAMING_SNAKE_CASE__:   Dict=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE__:   Optional[int]=      np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]   )

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



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=lowerCAmelCase , safety_checker=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      '''A painting of a squirrel eating a burger'''
     SCREAMING_SNAKE_CASE__:   Any=      torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   str=      alt_pipe([prompt] , generator=lowerCAmelCase , num_inference_steps=2 , output_type='''numpy'''   )
     SCREAMING_SNAKE_CASE__:   List[str]=      output.images

     SCREAMING_SNAKE_CASE__:   Dict=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]   )

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

