



'''simple docstring'''





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)  # pylint: disable=invalid-name






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,)    ->		str:
      super().__init__()

      if safety_checker is None:
          logger.warning(
              F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
              """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
              """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
              """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
              """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
              """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""      )

      self.register_modules(
          speech_model=__UpperCAmelCase  ,speech_processor=__UpperCAmelCase  ,vae=__UpperCAmelCase  ,text_encoder=__UpperCAmelCase  ,tokenizer=__UpperCAmelCase  ,unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase  ,feature_extractor=__UpperCAmelCase  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase = "auto"      )    ->		str:
      if slice_size == "auto":
          lowerCAmelCase__ :  int							      =					self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      self.enable_attention_slicing(__UpperCAmelCase      )






  @torch.no_grad()
  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=1_6000  ,__UpperCAmelCase = 512  ,__UpperCAmelCase = 512  ,__UpperCAmelCase = 50  ,__UpperCAmelCase = 7.5  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 1  ,__UpperCAmelCase = 0.0  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "pil"  ,__UpperCAmelCase = True  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 1  ,**__UpperCAmelCase  ,)    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					self.speech_processor.feature_extractor(
          __UpperCAmelCase  ,return_tensors="""pt"""  ,sampling_rate=__UpperCAmelCase      ).input_features.to(self.device      )
      lowerCAmelCase__ :  Any							      =					self.speech_model.generate(__UpperCAmelCase  ,max_length=48_0000      )

      lowerCAmelCase__ :  Union[str, Any]							      =					self.speech_processor.tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase  ,normalize=__UpperCAmelCase      )[
          0
      ]

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  Any							      =					1
      elif isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  Tuple							      =					len(__UpperCAmelCase      )
      else:
          raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase      )}"""      )

      if height % 8 != 0 or width % 8 != 0:
          raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""      )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) or callback_steps <= 0)
      ):
          raise ValueError(
              F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              F""" {type(__UpperCAmelCase      )}."""      )

      # get prompt text embeddings
      lowerCAmelCase__ :  Union[str, Any]							      =					self.tokenizer(
          __UpperCAmelCase  ,padding="""max_length"""  ,max_length=self.tokenizer.model_max_length  ,return_tensors="""pt"""  ,)
      lowerCAmelCase__ :  List[str]							      =					text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          lowerCAmelCase__ :  Optional[int]							      =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
          logger.warning(
              """The following part of your input was truncated because CLIP can only handle sequences up to"""
              F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""      )
          lowerCAmelCase__ :  Optional[Any]							      =					text_input_ids[:, : self.tokenizer.model_max_length]
      lowerCAmelCase__ :  Optional[int]							      =					self.text_encoder(text_input_ids.to(self.device      )      )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					text_embeddings.shape
      lowerCAmelCase__ :  Tuple							      =					text_embeddings.repeat(1  ,__UpperCAmelCase  ,1      )
      lowerCAmelCase__ :  List[str]							      =					text_embeddings.view(bs_embed * num_images_per_prompt  ,__UpperCAmelCase  ,-1      )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      lowerCAmelCase__ :  Optional[int]							      =					guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
          lowerCAmelCase__ :  List[str]
          if negative_prompt is None:
              lowerCAmelCase__ :  Any							      =					[""""""] * batch_size
          elif type(__UpperCAmelCase      ) is not type(__UpperCAmelCase      ):
              raise TypeError(
                  F"""`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCAmelCase      )} !="""
                  F""" {type(__UpperCAmelCase      )}."""      )
          elif isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
              lowerCAmelCase__ :  Any							      =					[negative_prompt]
          elif batch_size != len(__UpperCAmelCase      ):
              raise ValueError(
                  F"""`negative_prompt`: {negative_prompt} has batch size {len(__UpperCAmelCase      )}, but `prompt`:"""
                  F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                  """ the batch size of `prompt`."""      )
          else:
              lowerCAmelCase__ :  List[Any]							      =					negative_prompt

          lowerCAmelCase__ :  List[str]							      =					text_input_ids.shape[-1]
          lowerCAmelCase__ :  Union[str, Any]							      =					self.tokenizer(
              __UpperCAmelCase  ,padding="""max_length"""  ,max_length=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""  ,)
          lowerCAmelCase__ :  Union[str, Any]							      =					self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          lowerCAmelCase__ :  Optional[int]							      =					uncond_embeddings.shape[1]
          lowerCAmelCase__ :  int							      =					uncond_embeddings.repeat(1  ,__UpperCAmelCase  ,1      )
          lowerCAmelCase__ :  Any							      =					uncond_embeddings.view(batch_size * num_images_per_prompt  ,__UpperCAmelCase  ,-1      )

          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          lowerCAmelCase__ :  List[Any]							      =					torch.cat([uncond_embeddings, text_embeddings]      )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      lowerCAmelCase__ :  Tuple							      =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      lowerCAmelCase__ :  Tuple							      =					text_embeddings.dtype
      if latents is None:
          if self.device.type == "mps":
              # randn does not exist on mps
              lowerCAmelCase__ :  Dict							      =					torch.randn(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device="""cpu"""  ,dtype=__UpperCAmelCase      ).to(
                  self.device      )
          else:
              lowerCAmelCase__ :  int							      =					torch.randn(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=self.device  ,dtype=__UpperCAmelCase      )
      else:
          if latents.shape != latents_shape:
              raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""      )
          lowerCAmelCase__ :  int							      =					latents.to(self.device      )

      # set timesteps
      self.scheduler.set_timesteps(__UpperCAmelCase      )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      lowerCAmelCase__ :  Any							      =					self.scheduler.timesteps.to(self.device      )

      # scale the initial noise by the standard deviation required by the scheduler
      lowerCAmelCase__ :  Any							      =					latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      lowerCAmelCase__ :  List[str]							      =					"""eta""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
      lowerCAmelCase__ :  int							      =					{}
      if accepts_eta:
          lowerCAmelCase__ :  List[str]							      =					eta

      for i, t in enumerate(self.progress_bar(__UpperCAmelCase      )      ):
          # expand the latents if we are doing classifier free guidance
          lowerCAmelCase__ :  str							      =					torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
          lowerCAmelCase__ :  Any							      =					self.scheduler.scale_model_input(__UpperCAmelCase  ,__UpperCAmelCase      )

          # predict the noise residual
          lowerCAmelCase__ :  List[str]							      =					self.unet(__UpperCAmelCase  ,__UpperCAmelCase  ,encoder_hidden_states=__UpperCAmelCase      ).sample

          # perform guidance
          if do_classifier_free_guidance:
              lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					noise_pred.chunk(2      )
              lowerCAmelCase__ :  List[Any]							      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase__ :  Dict							      =					self.scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase      ).prev_sample

          # call the callback, if provided
          if callback is not None and i % callback_steps == 0:
              callback(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[int]							      =					1 / 0.1_8_2_1_5 * latents
      lowerCAmelCase__ :  Optional[int]							      =					self.vae.decode(__UpperCAmelCase      ).sample

      lowerCAmelCase__ :  Any							      =					(image / 2 + 0.5).clamp(0  ,1      )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      lowerCAmelCase__ :  Any							      =					image.cpu().permute(0  ,2  ,3  ,1      ).float().numpy()

      if output_type == "pil":
          lowerCAmelCase__ :  Dict							      =					self.numpy_to_pil(__UpperCAmelCase      )

      if not return_dict:
          return image

      return StableDiffusionPipelineOutput(images=__UpperCAmelCase  ,nsfw_content_detected=__UpperCAmelCase      )






'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__		:					int												=  (
    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__		:					Tuple												=  (
    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__		:					Tuple												=  [
    """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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{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'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		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']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		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'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		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']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	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}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  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__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






