






from ...configuration_utils import PretrainedConfig


_a    :   Dict        =			{
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''tapas'''
      def __init__(   self    ,	_lowerCAmelCase=30_522    ,	_lowerCAmelCase=768    ,	_lowerCAmelCase=12    ,	_lowerCAmelCase=12    ,	_lowerCAmelCase=3_072    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=1_024    ,	_lowerCAmelCase=[3, 256, 256, 2, 256, 256, 10]    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=1e-12    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=10.0    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase="ratio"    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=64    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(pad_token_id=_lowerCAmelCase    ,	**_lowerCAmelCase		)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          lowerCAmelCase__   :str      							=				vocab_size
          lowerCAmelCase__   :Tuple      							=				hidden_size
          lowerCAmelCase__   :int      							=				num_hidden_layers
          lowerCAmelCase__   :Any      							=				num_attention_heads
          lowerCAmelCase__   :str      							=				hidden_act
          lowerCAmelCase__   :Tuple      							=				intermediate_size
          lowerCAmelCase__   :Union[str, Any]      							=				hidden_dropout_prob
          lowerCAmelCase__   :Dict      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :int      							=				max_position_embeddings
          lowerCAmelCase__   :Any      							=				type_vocab_sizes
          lowerCAmelCase__   :Any      							=				initializer_range
          lowerCAmelCase__   :Dict      							=				layer_norm_eps

          # Fine-tuning task hyperparameters
          lowerCAmelCase__   :Union[str, Any]      							=				positive_label_weight
          lowerCAmelCase__   :Any      							=				num_aggregation_labels
          lowerCAmelCase__   :Any      							=				aggregation_loss_weight
          lowerCAmelCase__   :Any      							=				use_answer_as_supervision
          lowerCAmelCase__   :int      							=				answer_loss_importance
          lowerCAmelCase__   :Tuple      							=				use_normalized_answer_loss
          lowerCAmelCase__   :Optional[Any]      							=				huber_loss_delta
          lowerCAmelCase__   :Tuple      							=				temperature
          lowerCAmelCase__   :int      							=				aggregation_temperature
          lowerCAmelCase__   :Tuple      							=				use_gumbel_for_cells
          lowerCAmelCase__   :str      							=				use_gumbel_for_aggregation
          lowerCAmelCase__   :Optional[int]      							=				average_approximation_function
          lowerCAmelCase__   :Union[str, Any]      							=				cell_selection_preference
          lowerCAmelCase__   :Optional[int]      							=				answer_loss_cutoff
          lowerCAmelCase__   :Optional[int]      							=				max_num_rows
          lowerCAmelCase__   :Optional[Any]      							=				max_num_columns
          lowerCAmelCase__   :List[str]      							=				average_logits_per_cell
          lowerCAmelCase__   :Tuple      							=				select_one_column
          lowerCAmelCase__   :Any      							=				allow_empty_column_selection
          lowerCAmelCase__   :int      							=				init_cell_selection_weights_to_zero
          lowerCAmelCase__   :Dict      							=				reset_position_index_per_cell
          lowerCAmelCase__   :List[str]      							=				disable_per_token_loss

          # Aggregation hyperparameters
          lowerCAmelCase__   :List[Any]      							=				aggregation_labels
          lowerCAmelCase__   :List[str]      							=				no_aggregation_label_index

          if isinstance(self.aggregation_labels    ,	_lowerCAmelCase		):
              lowerCAmelCase__   :Union[str, Any]      							=				{int(_lowerCAmelCase		): v for k, v in aggregation_labels.items()}











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 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''vqvae''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__()
          self.register_modules(unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	mel=_lowerCAmelCase    ,	vqvae=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 50 if isinstance(self.scheduler    ,	_lowerCAmelCase		) else 1_000
      @torch.no_grad()
      def __call__(   self    ,	_lowerCAmelCase = 1    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				steps or self.get_default_steps()
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
              lowerCAmelCase__   :Dict      							=				(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
              lowerCAmelCase__   :Optional[int]      							=				randn_tensor(
                  (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size[0],
                      self.unet.config.sample_size[1],
                  )    ,	generator=_lowerCAmelCase    ,	device=self.device    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				noise
          lowerCAmelCase__   :Any      							=				None

          if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.mel.audio_slice_to_image(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				np.frombuffer(input_image.tobytes()    ,	dtype="uint8"		).reshape(
                  (input_image.height, input_image.width)		)
              lowerCAmelCase__   :Tuple      							=				(input_image / 255) * 2 - 1
              lowerCAmelCase__   :Any      							=				torch.tensor(input_image[np.newaxis, :, :]    ,	dtype=torch.float		).to(self.device		)

              if self.vqvae is not None:
                  lowerCAmelCase__   :str      							=				self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase    ,	0		)		).latent_dist.sample(
                      generator=_lowerCAmelCase		)[0]
                  lowerCAmelCase__   :Dict      							=				self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                  lowerCAmelCase__   :Dict      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	self.scheduler.timesteps[start_step - 1]		)

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

          for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
              if isinstance(self.unet    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Optional[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              else:
                  lowerCAmelCase__   :Dict      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]

              if isinstance(self.scheduler    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Any      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	eta=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]
              else:
                  lowerCAmelCase__   :List[str]      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]

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

          if self.vqvae is not None:
              # 0.18215 was scaling factor used in training to ensure unit variance
              lowerCAmelCase__   :Any      							=				1 / self.vqvae.config.scaling_factor * images
              lowerCAmelCase__   :List[Any]      							=				self.vqvae.decode(_lowerCAmelCase		)["sample"]

          lowerCAmelCase__   :Dict      							=				(images / 2 + 0.5).clamp(0    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				images.cpu().permute(0    ,	2    ,	3    ,	1		).numpy()
          lowerCAmelCase__   :Optional[int]      							=				(images * 255).round().astype("uint8"		)
          lowerCAmelCase__   :Optional[int]      							=				list(
              (Image.fromarray(_[:, :, 0]		) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(_lowerCAmelCase    ,	mode="RGB"		).convert("L"		) for _ in images)		)

          lowerCAmelCase__   :Optional[Any]      							=				[self.mel.image_to_audio(_lowerCAmelCase		) for _ in images]
          if not return_dict:
              return images, (self.mel.get_sample_rate(), audios)

          return BaseOutput(**AudioPipelineOutput(np.array(_lowerCAmelCase		)[:, np.newaxis, :]		)    ,	**ImagePipelineOutput(_lowerCAmelCase		)		)
      @torch.no_grad()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = 50		):



          '''simple docstring'''
          assert isinstance(self.scheduler    ,	_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				np.array(
              [np.frombuffer(image.tobytes()    ,	dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          lowerCAmelCase__   :Dict      							=				(sample / 255) * 2 - 1
          lowerCAmelCase__   :Optional[Any]      							=				torch.Tensor(_lowerCAmelCase		).to(self.device		)

          for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,	(0,)		)		):
              lowerCAmelCase__   :List[Any]      							=				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
              lowerCAmelCase__   :Any      							=				self.scheduler.alphas_cumprod[t]
              lowerCAmelCase__   :List[Any]      							=				(
                  self.scheduler.alphas_cumprod[prev_timestep]
                  if prev_timestep >= 0
                  else self.scheduler.final_alpha_cumprod
              )
              lowerCAmelCase__   :List[str]      							=				1 - alpha_prod_t
              lowerCAmelCase__   :List[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              lowerCAmelCase__   :int      							=				(1 - alpha_prod_t_prev) ** 0.5 * model_output
              lowerCAmelCase__   :List[Any]      							=				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
              lowerCAmelCase__   :Union[str, Any]      							=				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

          return sample



      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				acos(torch.dot(torch.flatten(_lowerCAmelCase		)    ,	torch.flatten(_lowerCAmelCase		)		) / torch.norm(_lowerCAmelCase		) / torch.norm(_lowerCAmelCase		)		)
          return sin((1 - alpha) * theta		) * xa / sin(_lowerCAmelCase		) + sin(alpha * theta		) * xa / sin(_lowerCAmelCase		)



