






'''simple docstring'''
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


a_					= [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
a_					= [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

a_					= (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

a_					= (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

a_					= [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      for tf_name, hf_name in patterns:
            SCREAMING_SNAKE_CASE__     :				str									=k.replace(UpperCamelCase__,	UpperCamelCase__							)
      return k





def  _a(  UpperCamelCase__				:						dict,	UpperCamelCase__				:						dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=BigBirdPegasusConfig(**UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									=BigBirdPegasusForConditionalGeneration(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=torch_model.state_dict()
      SCREAMING_SNAKE_CASE__     :				Dict									={}

      # separating decoder weights
      SCREAMING_SNAKE_CASE__     :				int									={k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''							)}
      SCREAMING_SNAKE_CASE__     :				List[Any]									={k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder'''							)}

      for k, v in tqdm(decoder_weights.items(),	'''tf -> hf conversion'''							):
            SCREAMING_SNAKE_CASE__     :				Any									=[k.endswith(UpperCamelCase__							) for ending in KEYS_TO_IGNORE]
            if any(UpperCamelCase__							):
                  continue
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=DECODER_PATTERNS
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=rename_state_dict_key(UpperCamelCase__,	UpperCamelCase__							)
            if new_k not in state_dict:
                  raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})"							)
            if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value''']							):
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=v.T
            SCREAMING_SNAKE_CASE__     :				int									=torch.from_numpy(UpperCamelCase__							)
            assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"

      for k, v in tqdm(remaining_weights.items(),	'''tf -> hf conversion'''							):
            SCREAMING_SNAKE_CASE__     :				Dict									=[k.endswith(UpperCamelCase__							) for ending in KEYS_TO_IGNORE]
            if any(UpperCamelCase__							):
                  continue
            SCREAMING_SNAKE_CASE__     :				List[Any]									=REMAINING_PATTERNS
            SCREAMING_SNAKE_CASE__     :				Any									=rename_state_dict_key(UpperCamelCase__,	UpperCamelCase__							)
            if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
                  raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})"							)
            if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value''']							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=v.T
            SCREAMING_SNAKE_CASE__     :				List[str]									=torch.from_numpy(UpperCamelCase__							)
            if k != "pegasus/embeddings/position_embeddings":
                  assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"

      SCREAMING_SNAKE_CASE__     :				Dict									=mapping['''model.embed_positions.weight''']
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=mapping.pop('''model.embed_positions.weight'''							)
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=torch_model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				str									=[
          k
          for k in missing
          if k
          not in [
              '''final_logits_bias''',
              '''model.encoder.embed_tokens.weight''',
              '''model.decoder.embed_tokens.weight''',
              '''lm_head.weight''',
          ]
      ]
      assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
      assert extra == [], f"no matches found for the following tf keys {extra}"
      return torch_model





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tf.train.list_variables(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Dict									={}
      SCREAMING_SNAKE_CASE__     :				int									=['''global_step''']
      for name, shape in tqdm(UpperCamelCase__,	desc='''converting tf checkpoint to dict'''							):
            SCREAMING_SNAKE_CASE__     :				List[str]									=any(pat in name for pat in ignore_name							)
            if skip_key:
                  continue
            SCREAMING_SNAKE_CASE__     :				Any									=tf.train.load_variable(UpperCamelCase__,	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Dict									=array
      return tf_weights





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str,	UpperCamelCase__				:						dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=get_tf_weights_as_numpy(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=convert_bigbird_pegasus(UpperCamelCase__,	UpperCamelCase__							)
      torch_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  a_					= argparse.ArgumentParser()
  parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
  parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
  a_					= parser.parse_args()
  a_					= {}
  convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







'''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      )