





'''simple docstring'''

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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)  # pylint: disable=invalid-name






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Optional[Any]						,	lowercase_				:AutoencoderKL						,	lowercase_				:CLIPTextModel						,	lowercase_				:CLIPTokenizer						,	lowercase_				:UNetaDConditionModel						,	lowercase_				:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,	lowercase_				:StableDiffusionSafetyChecker						,	lowercase_				:CLIPImageProcessor						,	)->							int:
     super().__init__()
     self.register_modules(
         vae=lowercase_						,	text_encoder=lowercase_						,	tokenizer=lowercase_						,	unet=lowercase_						,	scheduler=lowercase_						,	safety_checker=lowercase_						,	feature_extractor=lowercase_						,	)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Optional[Union[str, int]] = "auto"							)->							Dict:
     if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         A__    =      self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							str:
     self.enable_attention_slicing(lowercase_							)



 @torch.no_grad()
 def __call__(    self				:Tuple						,	lowercase_				:Union[str, List[str]]						,	lowercase_				:int = 5_12						,	lowercase_				:int = 5_12						,	lowercase_				:int = 50						,	lowercase_				:float = 7.5						,	lowercase_				:Optional[Union[str, List[str]]] = None						,	lowercase_				:Optional[int] = 1						,	lowercase_				:float = 0.0						,	lowercase_				:Optional[torch.Generator] = None						,	lowercase_				:Optional[torch.FloatTensor] = None						,	lowercase_				:Optional[str] = "pil"						,	lowercase_				:bool = True						,	lowercase_				:Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,	lowercase_				:int = 1						,	lowercase_				:Optional[torch.FloatTensor] = None						,	**lowercase_				:int						,	)->							List[str]:

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

     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(lowercase_						,	lowercase_							) or callback_steps <= 0)
     ):
         raise ValueError(
             F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
             F" {type(lowercase_							)}."							)

     # get prompt text embeddings
     A__    =      self.tokenizer(
         lowercase_						,	padding="max_length"						,	max_length=self.tokenizer.model_max_length						,	return_tensors="pt"						,	)
     A__    =      text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         A__    =      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}"							)
         A__    =      text_input_ids[:, : self.tokenizer.model_max_length]

     if text_embeddings is None:
         A__    =      self.text_encoder(text_input_ids.to(self.device							)							)[0]

     # duplicate text embeddings for each generation per prompt, using mps friendly method
     A__,     A__,     A__    =      text_embeddings.shape
     A__    =      text_embeddings.repeat(1						,	lowercase_						,	1							)
     A__    =      text_embeddings.view(bs_embed * num_images_per_prompt						,	lowercase_						,	-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.
     A__    =      guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
         A__    =      42
         if negative_prompt is None:
             A__    =      [""]
         elif type(lowercase_							) is not type(lowercase_							):
             raise TypeError(
                 F"`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_							)} !="
                 F" {type(lowercase_							)}."							)
         elif isinstance(lowercase_						,	lowercase_							):
             A__    =      [negative_prompt]
         elif batch_size != len(lowercase_							):
             raise ValueError(
                 F"`negative_prompt`: {negative_prompt} has batch size {len(lowercase_							)}, but `prompt`:"
                 F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                 " the batch size of `prompt`."							)
         else:
             A__    =      negative_prompt

         A__    =      text_input_ids.shape[-1]
         A__    =      self.tokenizer(
             lowercase_						,	padding="max_length"						,	max_length=lowercase_						,	truncation=lowercase_						,	return_tensors="pt"						,	)
         A__    =      self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         A__    =      uncond_embeddings.shape[1]
         A__    =      uncond_embeddings.repeat(lowercase_						,	lowercase_						,	1							)
         A__    =      uncond_embeddings.view(batch_size * num_images_per_prompt						,	lowercase_						,	-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
         A__    =      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`.
     A__    =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     A__    =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
     A__    =      text_embeddings.dtype
     if latents is None:
         if self.device.type == "mps":
             # randn does not exist on mps
             A__    =      torch.randn(
                 lowercase_						,	generator=lowercase_						,	device="cpu"						,	dtype=lowercase_							).to(self.device							)
             A__    =      torch.randn(lowercase_						,	generator=lowercase_						,	device="cpu"						,	dtype=lowercase_							).to(
                 self.device							)
         else:
             A__    =      torch.randn(
                 lowercase_						,	generator=lowercase_						,	device=self.device						,	dtype=lowercase_							)
             A__    =      torch.randn(lowercase_						,	generator=lowercase_						,	device=self.device						,	dtype=lowercase_							)
     else:
         if latents_reference.shape != latents_shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"							)
         A__    =      latents_reference.to(self.device							)
         A__    =      latents.to(self.device							)

     # This is the key part of the pipeline where we
     # try to ensure that the generated images w/ the same seed
     # but different sizes actually result in similar images
     A__    =      (latents_shape[3] - latents_shape_reference[3]) // 2
     A__    =      (latents_shape[2] - latents_shape_reference[2]) // 2
     A__    =      latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
     A__    =      latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
     A__    =      0 if dx < 0 else dx
     A__    =      0 if dy < 0 else dy
     A__    =      max(-dx						,	0							)
     A__    =      max(-dy						,	0							)
     # import pdb
     # pdb.set_trace()
     A__    =      latents_reference[:, :, dy : dy + h, dx : dx + w]

     # set timesteps
     self.scheduler.set_timesteps(lowercase_							)

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

     # scale the initial noise by the standard deviation required by the scheduler
     A__    =      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]
     A__    =      "eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
     A__    =      {}
     if accepts_eta:
         A__    =      eta

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

         # predict the noise residual
         A__    =      self.unet(lowercase_						,	lowercase_						,	encoder_hidden_states=lowercase_							).sample

         # perform guidance
         if do_classifier_free_guidance:
             A__,     A__    =      noise_pred.chunk(2							)
             A__    =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         # compute the previous noisy sample x_t -> x_t-1
         A__    =      self.scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

     A__    =      1 / 0.1_8_2_1_5 * latents
     A__    =      self.vae.decode(lowercase_							).sample

     A__    =      (image / 2 + 0.5).clamp(0						,	1							)

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

     if self.safety_checker is not None:
         A__    =      self.feature_extractor(self.numpy_to_pil(lowercase_							)						,	return_tensors="pt"							).to(
             self.device							)
         A__,     A__    =      self.safety_checker(
             images=lowercase_						,	clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
     else:
         A__    =      None

     if output_type == "pil":
         A__    =      self.numpy_to_pil(lowercase_							)

     if not return_dict:
         return (image, has_nsfw_concept)

     return StableDiffusionPipelineOutput(images=lowercase_						,	nsfw_content_detected=lowercase_							)










'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__    =      rename_state_dict_key(_lowerCamelCase	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            A__    =      v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



