



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 				__snake_case						(     UpperCamelCase_						):
							_a			      = ['''vqvae''']




							def __init__(       self     :       Tuple				,				A_     :       AutoencoderKL				,				A_     :       UNetaDConditionModel				,				A_     :       Mel				,				A_     :       Union[DDIMScheduler, DDPMScheduler]				,				):
									super().__init__()
									self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_)




							def    UpperCAmelCase__			(       self     :       Any):
									return 5_0 if isinstance(self.scheduler				,				A_) else 1_0_0_0




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

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(A_				,				A_)
											lowerCAmelCase_			:     Optional[int]									= self.mel.audio_slice_to_image(A_)
											lowerCAmelCase_			:     int									= np.frombuffer(input_image.tobytes()				,				dtype='''uint8''').reshape(
											    (input_image.height, input_image.width))
											lowerCAmelCase_			:     str									= (input_image / 2_5_5) * 2 - 1
											lowerCAmelCase_			:     List[Any]									= torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float).to(self.device)

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

											if start_step > 0:
													lowerCAmelCase_			:     List[str]									= self.scheduler.add_noise(A_				,				A_				,				self.scheduler.timesteps[start_step - 1])

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

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
											if isinstance(self.unet				,				A_):
													lowerCAmelCase_			:     Optional[Any]									= self.unet(A_				,				A_				,				A_)['''sample''']
											else:
													lowerCAmelCase_			:     Dict									= self.unet(A_				,				A_)['''sample''']

											if isinstance(self.scheduler				,				A_):
													lowerCAmelCase_			:     List[Any]									= self.scheduler.step(
													    model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
											else:
													lowerCAmelCase_			:     int									= self.scheduler.step(
													    model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''prev_sample''']

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

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

									lowerCAmelCase_			:     Optional[int]									= (images / 2 + 0.5).clamp(0				,				1)
									lowerCAmelCase_			:     List[str]									= images.cpu().permute(0				,				2				,				3				,				1).numpy()
									lowerCAmelCase_			:     Union[str, Any]									= (images * 2_5_5).round().astype('''uint8''')
									lowerCAmelCase_			:     Optional[Any]									= list(
									    (Image.fromarray(_[:, :, 0]) for _ in images)
									    if images.shape[3] == 1
									    else (Image.fromarray(A_				,				mode='''RGB''').convert('''L''') for _ in images))

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

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




							@torch.no_grad()
							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       List[Image.Image]				,				A_     :       int = 5_0):
									assert isinstance(self.scheduler				,				A_)
									self.scheduler.set_timesteps(A_)
									lowerCAmelCase_			:     int									= np.array(
									    [np.frombuffer(image.tobytes()				,				dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
									lowerCAmelCase_			:     Any									= (sample / 2_5_5) * 2 - 1
									lowerCAmelCase_			:     List[str]									= torch.Tensor(A_).to(self.device)

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

									return sample







							@staticmethod
							def    UpperCAmelCase__			(       A_     :       torch.Tensor				,				A_     :       torch.Tensor				,				A_     :       float):
									lowerCAmelCase_			:     Dict									= acos(torch.dot(torch.flatten(A_)				,				torch.flatten(A_)) / torch.norm(A_) / torch.norm(A_))
									return sin((1 - alpha) * theta) * xa / sin(A_) + sin(alpha * theta) * xa / sin(A_)












import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCAmelCase	:	int		     =       {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE						)

  a__	:					Dict										=finetuning_task.lower() if finetuning_task is not None else ""
  if finetuning_task in GLUE_TASKS_NUM_LABELS:
    print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
    a__	:					List[str]										=finetuning_task
    a__	:					Tuple										=GLUE_TASKS_NUM_LABELS[finetuning_task]
    a__	:					List[Any]										=XLNetForSequenceClassification(SCREAMING_SNAKE_CASE						)
  elif "squad" in finetuning_task:
    a__	:					Optional[int]										=finetuning_task
    a__	:					Dict										=XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					List[Any]										=XLNetLMHeadModel(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  torch.save(model.state_dict()       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  with open(SCREAMING_SNAKE_CASE       ,       "w"       ,       encoding="utf-8"						) as f:
    f.write(config.to_json_string()						)


if __name__ == "__main__":
     UpperCAmelCase	:	List[str]		     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--xlnet_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained XLNet model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
     )
     parser.add_argument(
         """--finetuning_task""",
         default=None,
         type=str,
         help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
     )
     UpperCAmelCase	:	int		     =       parser.parse_args()
     print(args)

     convert_xlnet_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
     )






