






'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...tokenization_utils_base import PreTrainedTokenizerBase
  from ...utils import TensorType

a_					= logging.get_logger(__name__)

a_					= {
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
a_					= [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a_					= [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""whisper"""
 snake_case_  							=							["""past_key_values"""]
 snake_case_  							=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




 def __init__(      self	:							Any					,      __lowercase	:							Any=5_18_65					,      __lowercase	:							Optional[Any]=80					,      __lowercase	:							Union[str, Any]=6					,      __lowercase	:							Optional[int]=4					,      __lowercase	:							Optional[int]=6					,      __lowercase	:							int=4					,      __lowercase	:							Dict=15_36					,      __lowercase	:							Any=15_36					,      __lowercase	:							Dict=0.0					,      __lowercase	:							Dict=0.0					,      __lowercase	:							Optional[int]=5_02_57					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							List[str]=True					,      __lowercase	:							Optional[int]="gelu"					,      __lowercase	:							Dict=2_56					,      __lowercase	:							Union[str, Any]=0.0					,      __lowercase	:							Optional[int]=0.0					,      __lowercase	:							Optional[Any]=0.0					,      __lowercase	:							Any=0.02					,      __lowercase	:							Optional[Any]=False					,      __lowercase	:							int=15_00					,      __lowercase	:							Any=4_48					,      __lowercase	:							int=5_02_56					,      __lowercase	:							Dict=5_02_56					,      __lowercase	:							Dict=5_02_56					,      __lowercase	:							List[Any]=None					,      __lowercase	:							Tuple=[2_20, 5_02_56]					,      __lowercase	:							Dict=False					,      __lowercase	:							Dict=2_56					,      __lowercase	:							List[Any]=False					,      __lowercase	:							Union[str, Any]=0.05					,      __lowercase	:							List[Any]=10					,      __lowercase	:							List[str]=2					,      __lowercase	:							Dict=0.0					,      __lowercase	:							int=10					,      __lowercase	:							Any=0					,      __lowercase	:							int=7					,      **__lowercase	:							int					,      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				int									=num_mel_bins
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=d_model
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=encoder_layers
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=encoder_attention_heads
       SCREAMING_SNAKE_CASE__     :				str									=decoder_layers
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=decoder_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=decoder_ffn_dim
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=encoder_ffn_dim
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=dropout
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				str									=activation_dropout
       SCREAMING_SNAKE_CASE__     :				str									=activation_function
       SCREAMING_SNAKE_CASE__     :				Tuple									=init_std
       SCREAMING_SNAKE_CASE__     :				List[str]									=encoder_layerdrop
       SCREAMING_SNAKE_CASE__     :				int									=decoder_layerdrop
       SCREAMING_SNAKE_CASE__     :				List[Any]									=use_cache
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=encoder_layers
       SCREAMING_SNAKE_CASE__     :				List[str]									=scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE__     :				Any									=max_source_positions
       SCREAMING_SNAKE_CASE__     :				List[str]									=max_target_positions

       # Audio Classification-specific parameters. Feel free to ignore for other classes.
       SCREAMING_SNAKE_CASE__     :				List[Any]									=classifier_proj_size
       SCREAMING_SNAKE_CASE__     :				List[str]									=use_weighted_layer_sum

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       SCREAMING_SNAKE_CASE__     :				List[str]									=apply_spec_augment
       SCREAMING_SNAKE_CASE__     :				str									=mask_time_prob
       SCREAMING_SNAKE_CASE__     :				List[str]									=mask_time_length
       SCREAMING_SNAKE_CASE__     :				int									=mask_time_min_masks
       SCREAMING_SNAKE_CASE__     :				Any									=mask_feature_prob
       SCREAMING_SNAKE_CASE__     :				List[str]									=mask_feature_length
       SCREAMING_SNAKE_CASE__     :				int									=mask_feature_min_masks

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=median_filter_width

       super().__init__(
           pad_token_id=__lowercase					,      bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      is_encoder_decoder=__lowercase					,      decoder_start_token_id=__lowercase					,      suppress_tokens=__lowercase					,      begin_suppress_tokens=__lowercase					,      **__lowercase					,      )






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=OrderedDict(
           [
               ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
           ]      )
       if self.use_past:
             SCREAMING_SNAKE_CASE__     :				List[str]									={0: '''batch'''}
       else:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									={0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
             self.fill_with_past_key_values_(__lowercase					,      direction='''inputs'''      )

       return common_inputs




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      __lowercase	:							int = -1					,      __lowercase	:							int = -1					,      __lowercase	:							bool = False					,      __lowercase	:							Optional["TensorType"] = None					,      __lowercase	:							int = 2_20_50					,      __lowercase	:							float = 5.0					,      __lowercase	:							int = 2_20					,      )	->						Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=OrderedDict()
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=OnnxConfig.generate_dummy_inputs(
           self					,      preprocessor=preprocessor.feature_extractor					,      batch_size=__lowercase					,      framework=__lowercase					,      sampling_rate=__lowercase					,      time_duration=__lowercase					,      frequency=__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=encoder_inputs['''input_features'''].shape[2]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=encoder_sequence_length // 2 if self.use_past else seq_length

       SCREAMING_SNAKE_CASE__     :				Any									=super().generate_dummy_inputs(
           preprocessor.tokenizer					,      __lowercase					,      __lowercase					,      __lowercase					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=encoder_inputs.pop('''input_features'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=decoder_inputs.pop('''decoder_input_ids'''      )

       if "past_key_values" in decoder_inputs:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=decoder_inputs.pop('''past_key_values'''      )

       return dummy_inputs




 @property
 def      __magic_name__	(      self	:							Dict      )	->						float:
       return 1e-3







'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""vqvae"""]




 def __init__(      self	:							int					,      __lowercase	:							AutoencoderKL					,      __lowercase	:							UNetaDConditionModel					,      __lowercase	:							Mel					,      __lowercase	:							Union[DDIMScheduler, DDPMScheduler]					,      )	->						int:
       super().__init__()
       self.register_modules(unet=__lowercase					,      scheduler=__lowercase					,      mel=__lowercase					,      vqvae=__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return 50 if isinstance(self.scheduler					,      __lowercase      ) else 10_00




 @torch.no_grad()
 def __call__(      self	:							Dict					,      __lowercase	:							int = 1					,      __lowercase	:							str = None					,      __lowercase	:							np.ndarray = None					,      __lowercase	:							int = 0					,      __lowercase	:							int = 0					,      __lowercase	:							int = None					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Generator = None					,      __lowercase	:							float = 0					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							torch.Tensor = None					,      __lowercase	:							Dict=True					,      )	->						Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=steps or self.get_default_steps()
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size      ) == int:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=randn_tensor(
                 (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size[0],
                     self.unet.config.sample_size[1],
                 )					,      generator=__lowercase					,      device=self.device					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=noise
       SCREAMING_SNAKE_CASE__     :				List[str]									=None

       if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(__lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.mel.audio_slice_to_image(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.frombuffer(input_image.tobytes()					,      dtype='''uint8'''      ).reshape(
                 (input_image.height, input_image.width)      )
             SCREAMING_SNAKE_CASE__     :				int									=(input_image / 2_55) * 2 - 1
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor(input_image[np.newaxis, :, :]					,      dtype=torch.float      ).to(self.device      )

             if self.vqvae is not None:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.vqvae.encode(torch.unsqueeze(__lowercase					,      0      )      ).latent_dist.sample(
                       generator=__lowercase      )[0]
                   SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.config.scaling_factor * input_images

             if start_step > 0:
                   SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      self.scheduler.timesteps[start_step - 1]      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=int(mask_start_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=int(mask_end_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				int									=self.scheduler.add_noise(__lowercase					,      __lowercase					,      torch.tensor(self.scheduler.timesteps[start_step:]      )      )

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]      )      ):
             if isinstance(self.unet					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase					,      __lowercase      )['''sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase      )['''sample''']

             if isinstance(self.scheduler					,      __lowercase      ):
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      eta=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				Any									=self.scheduler.step(
                       model_output=__lowercase					,      timestep=__lowercase					,      sample=__lowercase					,      generator=__lowercase					,      )['''prev_sample''']

             if mask is not None:
                   if mask_start > 0:
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=mask[:, step, :, :mask_start]
                   if mask_end > 0:
                         SCREAMING_SNAKE_CASE__     :				List[str]									=mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
             # 0.18215 was scaling factor used in training to ensure unit variance
             SCREAMING_SNAKE_CASE__     :				str									=1 / self.vqvae.config.scaling_factor * images
             SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.decode(__lowercase      )['''sample''']

       SCREAMING_SNAKE_CASE__     :				List[str]									=(images / 2 + 0.5).clamp(0					,      1      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=images.cpu().permute(0					,      2					,      3					,      1      ).numpy()
       SCREAMING_SNAKE_CASE__     :				Dict									=(images * 2_55).round().astype('''uint8'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=list(
           (Image.fromarray(_[:, :, 0]      ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(__lowercase					,      mode='''RGB'''      ).convert('''L'''      ) for _ in images)      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[self.mel.image_to_audio(__lowercase      ) for _ in images]
       if not return_dict:
             return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(__lowercase      )[:, np.newaxis, :]      )					,      **ImagePipelineOutput(__lowercase      )      )




 @torch.no_grad()
 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Image.Image]					,      __lowercase	:							int = 50      )	->						np.ndarray:
       assert isinstance(self.scheduler					,      __lowercase      )
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array(
           [np.frombuffer(image.tobytes()					,      dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
       SCREAMING_SNAKE_CASE__     :				str									=(sample / 2_55) * 2 - 1
       SCREAMING_SNAKE_CASE__     :				str									=torch.Tensor(__lowercase      ).to(self.device      )

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,      (0,)      )      ):
             SCREAMING_SNAKE_CASE__     :				Tuple									=t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             SCREAMING_SNAKE_CASE__     :				Any									=self.scheduler.alphas_cumprod[t]
             SCREAMING_SNAKE_CASE__     :				int									=(
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             SCREAMING_SNAKE_CASE__     :				int									=1 - alpha_prod_t
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.unet(__lowercase					,      __lowercase      )['''sample''']
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(1 - alpha_prod_t_prev) ** 0.5 * model_output
             SCREAMING_SNAKE_CASE__     :				str									=(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
             SCREAMING_SNAKE_CASE__     :				Any									=sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample




 @staticmethod
 def      __magic_name__	(      __lowercase	:							torch.Tensor					,      __lowercase	:							torch.Tensor					,      __lowercase	:							float      )	->						torch.Tensor:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=acos(torch.dot(torch.flatten(__lowercase      )					,      torch.flatten(__lowercase      )      ) / torch.norm(__lowercase      ) / torch.norm(__lowercase      )      )
       return sin((1 - alpha) * theta      ) * xa / sin(__lowercase      ) + sin(alpha * theta      ) * xa / sin(__lowercase      )