






'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							AudioLDMPipeline
    lowerCAmelCase__      				=							TEXT_TO_AUDIO_PARAMS
    lowerCAmelCase__      				=							TEXT_TO_AUDIO_BATCH_PARAMS
    lowerCAmelCase__      				=							frozenset(
        [
            "num_inference_steps",
            "num_waveforms_per_prompt",
            "generator",
            "latents",
            "output_type",
            "return_dict",
            "callback",
            "callback_steps",
        ]      )

    def    a__				(					self     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




     torch.manual_seed(0     )
     lowerCamelCase__  =     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, 64)				,     class_embed_type="simple_projection"				,     projection_class_embeddings_input_dim=32				,     class_embeddings_concat=__lowerCamelCase				,     )
     lowerCamelCase__  =     DDIMScheduler(
         beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="scaled_linear"				,     clip_sample=__lowerCamelCase				,     set_alpha_to_one=__lowerCamelCase				,     )
     torch.manual_seed(0     )
     lowerCamelCase__  =     AutoencoderKL(
         block_out_channels=[32, 64]				,     in_channels=1				,     out_channels=1				,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,     latent_channels=4				,     )
     torch.manual_seed(0     )
     lowerCamelCase__  =     ClapTextConfig(
         bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1000				,     projection_dim=32				,     )
     lowerCamelCase__  =     ClapTextModelWithProjection(__lowerCamelCase     )
     lowerCamelCase__  =     RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"				,     model_max_length=77     )

     lowerCamelCase__  =     SpeechTaHifiGanConfig(
         model_in_dim=8				,     sampling_rate=16000				,     upsample_initial_channel=16				,     upsample_rates=[2, 2]				,     upsample_kernel_sizes=[4, 4]				,     resblock_kernel_sizes=[3, 7]				,     resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]				,     normalize_before=__lowerCamelCase				,     )

     lowerCamelCase__  =     SpeechTaHifiGan(__lowerCamelCase     )

     lowerCamelCase__  =     {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "vocoder": vocoder,
     }
     return components

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Optional[int]=0     )							->							List[Any]:

     '''simple docstring'''




     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "prompt": "A hammer hitting a wooden surface",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
     }
     return inputs

    def    a__				(					self     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"  # ensure determinism for the device-dependent torch.Generator

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) == 256

     lowerCamelCase__  =     audio[:10]
     lowerCamelCase__  =     np.array(
         [-0.0_0_5_0, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_3, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_3]     )

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

    def    a__				(					self     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     3 * [inputs["prompt"]]

     # forward
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     3 * [inputs.pop("prompt"     )]

     lowerCamelCase__  =     audioldm_pipe.tokenizer(
         __lowerCamelCase				,     padding="max_length"				,     max_length=audioldm_pipe.tokenizer.model_max_length				,     truncation=__lowerCamelCase				,     return_tensors="pt"				,     )
     lowerCamelCase__  =     text_inputs["input_ids"].to(__lowerCamelCase     )

     lowerCamelCase__  =     audioldm_pipe.text_encoder(
         __lowerCamelCase				,     )
     lowerCamelCase__  =     prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     lowerCamelCase__  =     F.normalize(__lowerCamelCase				,     dim=-1     )

     lowerCamelCase__  =     prompt_embeds

     # forward
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert np.abs(audio_a - audio_a     ).max() < 1E-2

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

     '''simple docstring'''




     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     3 * ["this is a negative prompt"]
     lowerCamelCase__  =     negative_prompt
     lowerCamelCase__  =     3 * [inputs["prompt"]]

     # forward
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     3 * [inputs.pop("prompt"     )]

     lowerCamelCase__  =     []
     for p in [prompt, negative_prompt]:
      lowerCamelCase__  =     audioldm_pipe.tokenizer(
          __lowerCamelCase				,     padding="max_length"				,     max_length=audioldm_pipe.tokenizer.model_max_length				,     truncation=__lowerCamelCase				,     return_tensors="pt"				,     )
      lowerCamelCase__  =     text_inputs["input_ids"].to(__lowerCamelCase     )

      lowerCamelCase__  =     audioldm_pipe.text_encoder(
          __lowerCamelCase				,     )
      lowerCamelCase__  =     text_embeds.text_embeds
      # additional L_2 normalization over each hidden-state
      lowerCamelCase__  =     F.normalize(__lowerCamelCase				,     dim=-1     )

      embeds.append(__lowerCamelCase     )

     lowerCamelCase__  ,	lowerCamelCase__  =     embeds

     # forward
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert np.abs(audio_a - audio_a     ).max() < 1E-2

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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     PNDMScheduler(skip_prk_steps=__lowerCamelCase     )
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     "egg cracking"
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase				,     negative_prompt=__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) == 256

     lowerCamelCase__  =     audio[:10]
     lowerCamelCase__  =     np.array(
         [-0.0_0_5_1, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_4, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_2]     )

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

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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     PNDMScheduler(skip_prk_steps=__lowerCamelCase     )
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "A hammer hitting a wooden surface"

     # test num_waveforms_per_prompt=1 (default)
     lowerCamelCase__  =     audioldm_pipe(__lowerCamelCase				,     num_inference_steps=2     ).audios

     assert audios.shape == (1, 256)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     lowerCamelCase__  =     2
     lowerCamelCase__  =     audioldm_pipe([prompt] * batch_size				,     num_inference_steps=2     ).audios

     assert audios.shape == (batch_size, 256)

     # test num_waveforms_per_prompt for single prompt
     lowerCamelCase__  =     2
     lowerCamelCase__  =     audioldm_pipe(__lowerCamelCase				,     num_inference_steps=2				,     num_waveforms_per_prompt=__lowerCamelCase     ).audios

     assert audios.shape == (num_waveforms_per_prompt, 256)

     # test num_waveforms_per_prompt for batch of prompts
     lowerCamelCase__  =     2
     lowerCamelCase__  =     audioldm_pipe(
         [prompt] * batch_size				,     num_inference_steps=2				,     num_waveforms_per_prompt=__lowerCamelCase     ).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)

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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.vocoder.config.sampling_rate

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe(audio_length_in_s=0.0_1_6				,     **__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) / vocoder_sampling_rate == 0.0_1_6

     lowerCamelCase__  =     audioldm_pipe(audio_length_in_s=0.0_3_2				,     **__lowerCamelCase     )
     lowerCamelCase__  =     output.audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) / vocoder_sampling_rate == 0.0_3_2

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

     '''simple docstring'''




     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     AudioLDMPipeline(**__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     ["hey"]

     lowerCamelCase__  =     audioldm_pipe(__lowerCamelCase				,     num_inference_steps=1     )
     lowerCamelCase__  =     output.audios.shape
     assert audio_shape == (1, 256)

     lowerCamelCase__  =     audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     lowerCamelCase__  =     SpeechTaHifiGan(__lowerCamelCase     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe(__lowerCamelCase				,     num_inference_steps=1     )
     lowerCamelCase__  =     output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 256)

    def    a__				(					self     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__lowerCamelCase     )

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

     '''simple docstring'''




     self._test_inference_batch_single_identical(test_mean_pixel_difference=__lowerCamelCase     )




    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()				,     reason="XFormers attention is only available with CUDA and `xformers` installed"				,     )
    def    a__				(					self     :     Tuple     )							->							List[Any]:

     '''simple docstring'''




     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__lowerCamelCase     )
@slow
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def    a__				(					self     :     List[Any]     )							->							int:

     '''simple docstring'''




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

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]="cpu"				,     __lowerCamelCase     :     Any=torch.floataa				,     __lowerCamelCase     :     Optional[int]=0     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     np.random.RandomState(__lowerCamelCase     ).standard_normal((1, 8, 128, 16)     )
     lowerCamelCase__  =     torch.from_numpy(__lowerCamelCase     ).to(device=__lowerCamelCase				,     dtype=__lowerCamelCase     )
     lowerCamelCase__  =     {
         "prompt": "A hammer hitting a wooden surface",
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 2.5,
     }
     return inputs

    def    a__				(					self     :     Tuple     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     25
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     ).audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) == 81920

     lowerCamelCase__  =     audio[77230:77240]
     lowerCamelCase__  =     np.array(
         [-0.4_8_8_4, -0.4_6_0_7, 0.0_0_2_3, 0.5_0_0_7, 0.5_8_9_6, 0.5_1_5_1, 0.3_8_1_3, -0.0_2_0_8, -0.3_6_8_7, -0.4_3_1_5]     )
     lowerCamelCase__  =     np.abs(expected_slice - audio_slice     ).max()
     assert max_diff < 1E-2




    def    a__				(					self     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
     lowerCamelCase__  =     LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
     lowerCamelCase__  =     audioldm_pipe.to(__lowerCamelCase     )
     audioldm_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     audioldm_pipe(**__lowerCamelCase     ).audios[0]

     assert audio.ndim == 1
     assert len(__lowerCamelCase     ) == 81920

     lowerCamelCase__  =     audio[27780:27790]
     lowerCamelCase__  =     np.array([-0.2_1_3_1, -0.0_8_7_3, -0.0_1_2_4, -0.0_1_8_9, 0.0_5_6_9, 0.1_3_7_3, 0.1_8_8_3, 0.2_8_8_6, 0.3_2_9_7, 0.2_2_1_2]     )
     lowerCamelCase__  =     np.abs(expected_slice - audio_slice     ).max()
     assert max_diff < 3E-2









'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"char"
    lowerCAmelCase__      				=							"bpe"
    lowerCAmelCase__      				=							"wp"
__A							:							Any  							=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "char_tokenizer"]
    lowerCAmelCase__      				=							"ViTImageProcessor"
    lowerCAmelCase__      				=							"MgpstrTokenizer"

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     **__lowerCamelCase     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,     __lowerCamelCase				,     )
      lowerCamelCase__  =     kwargs.pop("feature_extractor"     )

     lowerCamelCase__  =     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."     )
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."     )

     lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("gpt2"     )
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-uncased"     )

     super().__init__(__lowerCamelCase				,     __lowerCamelCase     )

    def __call__(					self     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     str=None				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     if images is None and text is None:
      raise ValueError("You need to specify either an `images` or `text` input to process."     )

     if images is not None:
      lowerCamelCase__  =     self.image_processor(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )
     if text is not None:
      lowerCamelCase__  =     self.char_tokenizer(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      lowerCamelCase__  =     encodings["input_ids"]
      return inputs

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     sequences
     lowerCamelCase__  =     char_preds.size(0     )

     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "char"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "bpe"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "wp"     )

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []
     for i in range(__lowerCamelCase     ):
      lowerCamelCase__  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
      lowerCamelCase__  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
      lowerCamelCase__  =     scores.index(max(__lowerCamelCase     )     )
      final_strs.append(strs[max_score_index]     )
      final_scores.append(scores[max_score_index]     )

     lowerCamelCase__  =     {}
     lowerCamelCase__  =     final_strs
     lowerCamelCase__  =     final_scores
     lowerCamelCase__  =     char_strs
     lowerCamelCase__  =     bpe_strs
     lowerCamelCase__  =     wp_strs
     return out

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     if format == DecodeType.CHARACTER:
      lowerCamelCase__  =     self.char_decode
      lowerCamelCase__  =     1
      lowerCamelCase__  =     "[s]"
     elif format == DecodeType.BPE:
      lowerCamelCase__  =     self.bpe_decode
      lowerCamelCase__  =     2
      lowerCamelCase__  =     "#"
     elif format == DecodeType.WORDPIECE:
      lowerCamelCase__  =     self.wp_decode
      lowerCamelCase__  =     102
      lowerCamelCase__  =     "[SEP]"
     else:
      raise ValueError(f'''Format {format} is not supported.'''     )

     lowerCamelCase__  ,	lowerCamelCase__  =     [], []
     lowerCamelCase__  =     pred_logits.size(0     )
     lowerCamelCase__  =     pred_logits.size(1     )
     lowerCamelCase__  ,	lowerCamelCase__  =     pred_logits.topk(1				,     dim=-1				,     largest=__lowerCamelCase				,     sorted=__lowerCamelCase     )
     lowerCamelCase__  =     preds_index.view(-1				,     __lowerCamelCase     )[:, 1:]
     lowerCamelCase__  =     decoder(__lowerCamelCase     )
     lowerCamelCase__  ,	lowerCamelCase__  =     torch.nn.functional.softmax(__lowerCamelCase				,     dim=2     ).max(dim=2     )
     lowerCamelCase__  =     preds_max_prob[:, 1:]

     for index in range(__lowerCamelCase     ):
      lowerCamelCase__  =     preds_str[index].find(__lowerCamelCase     )
      lowerCamelCase__  =     preds_str[index][:pred_eos]
      lowerCamelCase__  =     preds_index[index].cpu().tolist()
      lowerCamelCase__  =     pred_index.index(__lowerCamelCase     ) if eos_token in pred_index else -1
      lowerCamelCase__  =     preds_max_prob[index][: pred_eos_index + 1]
      lowerCamelCase__  =     pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(__lowerCamelCase     )
      conf_scores.append(__lowerCamelCase     )

     return dec_strs, conf_scores

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.bpe_tokenizer.batch_decode(__lowerCamelCase     )




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

