


'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__snake_case							      =     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''


def 			a      (			__a ,					__a ,					__a=8   )  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCamelCase__   :Any												= width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def 			a      (			__a ,					__a=512 ,					__a=512   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= pil_image.resize((w, h) ,					resample=Image.BICUBIC ,					reducing_gap=1   )
					UpperCamelCase__   :int												= np.array(pil_image.convert('''RGB'''   )   )
					UpperCamelCase__   :Tuple												= arr.astype(np.floataa   ) / 127.5 - 1
					UpperCamelCase__   :str												= np.transpose(snake_case_ ,					[2, 0, 1]   )
					UpperCamelCase__   :List[str]												= torch.from_numpy(snake_case_   ).unsqueeze(0   )
					return image







class    lowercase	(							UpperCAmelCase__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__()

							self.register_modules(
							    unet=SCREAMING_SNAKE_CASE__    ,   scheduler=SCREAMING_SNAKE_CASE__    ,   movq=SCREAMING_SNAKE_CASE__    ,   )
							UpperCamelCase__   :Optional[int]												= 2 ** (len(self.movq.config.block_out_channels							) - 1)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :str												= min(int(num_inference_steps * strength							)    ,   SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :List[Any]												= max(num_inference_steps - init_timestep    ,   0							)
							UpperCamelCase__   :str												= self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if not isinstance(SCREAMING_SNAKE_CASE__    ,   (torch.Tensor, PIL.Image.Image, list)							):
												raise ValueError(
												    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE__							)}'''							)

							UpperCamelCase__   :List[Any]												= image.to(device=SCREAMING_SNAKE_CASE__    ,   dtype=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :int												= batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												UpperCamelCase__   :List[Any]												= image

							else:
												if isinstance(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							) and len(SCREAMING_SNAKE_CASE__							) != batch_size:
																	raise ValueError(
																	    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__							)}, but requested an effective batch'''
																	    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

												elif isinstance(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							):
																	UpperCamelCase__   :Tuple												= [
																	    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(SCREAMING_SNAKE_CASE__							)
																	]
																	UpperCamelCase__   :Tuple												= torch.cat(SCREAMING_SNAKE_CASE__    ,   dim=0							)
												else:
																	UpperCamelCase__   :int												= self.movq.encode(SCREAMING_SNAKE_CASE__							).latent_dist.sample(SCREAMING_SNAKE_CASE__							)

												UpperCamelCase__   :Tuple												= self.movq.config.scaling_factor * init_latents

							UpperCamelCase__   :Tuple												= torch.cat([init_latents]    ,   dim=0							)

							UpperCamelCase__   :List[Any]												= init_latents.shape
							UpperCamelCase__   :List[Any]												= randn_tensor(SCREAMING_SNAKE_CASE__    ,   generator=SCREAMING_SNAKE_CASE__    ,   device=SCREAMING_SNAKE_CASE__    ,   dtype=SCREAMING_SNAKE_CASE__							)

							# get latents
							UpperCamelCase__   :Union[str, Any]												= self.scheduler.add_noise(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :Union[str, Any]												= init_latents

							return latents




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=0							):

							'''simple docstring'''


							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

							UpperCamelCase__   :Any												= torch.device(F'''cuda:{gpu_id}'''							)

							UpperCamelCase__   :int												= [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=0							):

							'''simple docstring'''


							if is_accelerate_available() and is_accelerate_version('''>='''    ,   '''0.17.0.dev0'''							):
												from accelerate import cpu_offload_with_hook
							else:
												raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

							UpperCamelCase__   :int												= torch.device(F'''cuda:{gpu_id}'''							)

							if self.device.type != "cpu":
												self.to('''cpu'''    ,   silence_dtype_warnings=SCREAMING_SNAKE_CASE__							)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							UpperCamelCase__   :List[str]												= None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= cpu_offload_with_hook(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   prev_module_hook=SCREAMING_SNAKE_CASE__							)

							# We'll offload the last model manually.
							UpperCamelCase__   :List[Any]												= hook




		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if not hasattr(self.unet    ,   '''_hf_hook'''							):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(SCREAMING_SNAKE_CASE__    ,   '''_hf_hook'''							)
												    and hasattr(module._hf_hook    ,   '''execution_device'''							)
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device							)
							return self.device




		@torch.no_grad()
		@replace_example_docstring(SCREAMING_SNAKE_CASE__							)
		def __call__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 512    ,   UpperCamelCase_ = 512    ,   UpperCamelCase_ = 100    ,   UpperCamelCase_ = 4.0    ,   UpperCamelCase_ = 0.3    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "pil"    ,   UpperCamelCase_ = True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self._execution_device

							UpperCamelCase__   :int												= guidance_scale > 1.0

							if isinstance(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							):
												UpperCamelCase__   :Optional[int]												= torch.cat(SCREAMING_SNAKE_CASE__    ,   dim=0							)
							UpperCamelCase__   :str												= image_embeds.shape[0]
							if isinstance(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							):
												UpperCamelCase__   :Dict												= torch.cat(SCREAMING_SNAKE_CASE__    ,   dim=0							)

							if do_classifier_free_guidance:
												UpperCamelCase__   :Tuple												= image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__    ,   dim=0							)
												UpperCamelCase__   :Union[str, Any]												= negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__    ,   dim=0							)

							UpperCamelCase__   :Optional[Any]												= torch.cat([negative_image_embeds, image_embeds]    ,   dim=0							).to(dtype=self.unet.dtype    ,   device=SCREAMING_SNAKE_CASE__							)

							if not isinstance(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							):
												UpperCamelCase__   :Tuple												= [image]
							if not all(isinstance(SCREAMING_SNAKE_CASE__    ,   (PIL.Image.Image, torch.Tensor)							) for i in image							):
												raise ValueError(
												    F'''Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE__							) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''							)

							UpperCamelCase__   :int												= torch.cat([prepare_image(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							) for i in image]    ,   dim=0							)
							UpperCamelCase__   :Union[str, Any]												= image.to(dtype=image_embeds.dtype    ,   device=SCREAMING_SNAKE_CASE__							)

							UpperCamelCase__   :Optional[int]												= self.movq.encode(SCREAMING_SNAKE_CASE__							)['''latents''']
							UpperCamelCase__   :List[str]												= latents.repeat_interleave(SCREAMING_SNAKE_CASE__    ,   dim=0							)
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__    ,   device=SCREAMING_SNAKE_CASE__							)
							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self.get_timesteps(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)
							UpperCamelCase__   :Tuple												= timesteps[:1].repeat(batch_size * num_images_per_prompt							)
							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= downscale_height_and_width(SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   self.movq_scale_factor							)
							UpperCamelCase__   :Optional[Any]												= self.prepare_latents(
							    SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   image_embeds.dtype    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__							)
							for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__							)							):
												# expand the latents if we are doing classifier free guidance
												UpperCamelCase__   :Any												= torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

												UpperCamelCase__   :Optional[int]												= {'''image_embeds''': image_embeds}
												UpperCamelCase__   :int												= self.unet(
												    sample=SCREAMING_SNAKE_CASE__    ,   timestep=SCREAMING_SNAKE_CASE__    ,   encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,   added_cond_kwargs=SCREAMING_SNAKE_CASE__    ,   return_dict=SCREAMING_SNAKE_CASE__    ,   )[0]

												if do_classifier_free_guidance:
																	UpperCamelCase__					,				UpperCamelCase__   :Any												= noise_pred.split(latents.shape[1]    ,   dim=1							)
																	UpperCamelCase__					,				UpperCamelCase__   :Any												= noise_pred.chunk(2							)
																	UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= variance_pred.chunk(2							)
																	UpperCamelCase__   :str												= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCamelCase__   :Tuple												= torch.cat([noise_pred, variance_pred_text]    ,   dim=1							)

												if not (
												    hasattr(self.scheduler.config    ,   '''variance_type'''							)
												    and self.scheduler.config.variance_type in ["learned", "learned_range"]
												):
																	UpperCamelCase__					,				UpperCamelCase__   :str												= noise_pred.split(latents.shape[1]    ,   dim=1							)

												# compute the previous noisy sample x_t -> x_t-1
												UpperCamelCase__   :str												= self.scheduler.step(
												    SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   SCREAMING_SNAKE_CASE__    ,   generator=SCREAMING_SNAKE_CASE__    ,   )[0]

							# post-processing
							UpperCamelCase__   :str												= self.movq.decode(SCREAMING_SNAKE_CASE__    ,   force_not_quantize=SCREAMING_SNAKE_CASE__							)['''sample''']

							if output_type not in ["pt", "np", "pil"]:
												raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''							)

							if output_type in ["np", "pil"]:
												UpperCamelCase__   :List[str]												= image * 0.5 + 0.5
												UpperCamelCase__   :Tuple												= image.clamp(0    ,   1							)
												UpperCamelCase__   :List[str]												= image.cpu().permute(0    ,   2    ,   3    ,   1							).float().numpy()

							if output_type == "pil":
												UpperCamelCase__   :Tuple												= self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__							)
'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''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_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(snake_case_						,		snake_case_      )

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







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

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


if __name__ == "__main__":
 _A			:      Optional[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'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






