
import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		pt_attn_layer.in_proj_weight.chunk(3		,				dim=0    )
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		pt_attn_layer.in_proj_bias.chunk(3		,				dim=0    )

  _lowerCAmelCase						=		pt_attn_layer.out_proj.weight
  _lowerCAmelCase						=		pt_attn_layer.out_proj.bias

  _lowerCAmelCase						=		q_proj
  _lowerCAmelCase						=		q_proj_bias

  _lowerCAmelCase						=		k_proj
  _lowerCAmelCase						=		k_proj_bias

  _lowerCAmelCase						=		v_proj
  _lowerCAmelCase						=		v_proj_bias

  _lowerCAmelCase						=		out_proj_weights
  _lowerCAmelCase						=		out_proj_bias



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  copy_linear(hf_mlp.fca		,				pt_mlp.c_fc    )
  copy_linear(hf_mlp.fca		,				pt_mlp.c_proj    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_linear.weight
  _lowerCAmelCase						=		pt_linear.bias



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






  """simple docstring"""




  copy_linear(hf_layer.layer_norma		,				pt_layer.ln_a    )
  copy_linear(hf_layer.layer_norma		,				pt_layer.ln_a    )

  # copy MLP
  copy_mlp(hf_layer.mlp		,				pt_layer.mlp    )

  # copy attn
  copy_attn_layer(hf_layer.self_attn		,				pt_layer.attn    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  for hf_layer, pt_layer in zip(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    copy_layer(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_model.token_embedding.weight
  _lowerCAmelCase						=		pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm		,				pt_model.ln_final    )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers		,				pt_model.transformer.resblocks    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model		,				__SCREAMING_SNAKE_CASE    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  _lowerCAmelCase						=		pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm		,				pt_model.visual.ln_pre    )
  copy_linear(hf_model.vision_model.post_layernorm		,				pt_model.visual.ln_post    )

  # copy embeds
  _lowerCAmelCase						=		pt_model.visual.conva.weight.data
  _lowerCAmelCase						=		pt_model.visual.class_embedding
  _lowerCAmelCase						=		pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers		,				pt_model.visual.transformer.resblocks    )



@torch.no_grad()
def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  Dict=None    ):






  """simple docstring"""




  if config_path is not None:
    _lowerCAmelCase						=		CLIPConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
  else:
    _lowerCAmelCase						=		CLIPConfig(projection_dim=512		,				text_config={}		,				vision_config={}    )

  _lowerCAmelCase						=		CLIPModel(__SCREAMING_SNAKE_CASE    ).eval()

  _lowerCAmelCase	,       _lowerCAmelCase						=		load(__SCREAMING_SNAKE_CASE		,				device='cpu'		,				jit=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		pt_model.eval()

  copy_text_model_and_projection(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  copy_vison_model_and_projection(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		pt_model.logit_scale

  _lowerCAmelCase						=		torch.arange(0		,				77    ).unsqueeze(0    )
  _lowerCAmelCase						=		torch.randn(1		,				3		,				224		,				224    )

  _lowerCAmelCase						=		hf_model(input_ids=__SCREAMING_SNAKE_CASE		,				pixel_values=__SCREAMING_SNAKE_CASE		,				return_dict=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		hf_outputs.logits_per_image
  _lowerCAmelCase						=		hf_outputs.logits_per_text
  _lowerCAmelCase	,       _lowerCAmelCase						=		pt_model(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  assert torch.allclose(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				atol=1E-3    )
  assert torch.allclose(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				atol=1E-3    )

  hf_model.save_pretrained(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       _UpperCamelCase:					Optional[int]     =argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       _UpperCamelCase:					Union[str, Any]     =parser.parse_args()

       convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

snake_case__   :				Any						=	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'





@dataclass
class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					42





class A_   (     _lowerCamelCase  ):




   def __init__(self      :List[str]       ,  _UpperCamelCase      :PriorTransformer       ,  _UpperCamelCase      :CLIPTextModelWithProjection       ,  _UpperCamelCase      :CLIPTokenizer       ,  _UpperCamelCase      :HeunDiscreteScheduler       ,  _UpperCamelCase      :ShapERenderer       ,  )->   Tuple:
         super().__init__()

         self.register_modules(
             prior=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  renderer=_UpperCamelCase       ,  )




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Union[str, Any]					)->   Any:
         if latents is None:
               __A			  =							randn_tensor(_UpperCamelCase       ,  generator=_UpperCamelCase       ,  device=_UpperCamelCase       ,  dtype=_UpperCamelCase					)
         else:
               if latents.shape != shape:
                     raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
               __A			  =							latents.to(_UpperCamelCase					)

         __A			  =							latents * scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Dict=0					)->   Optional[Any]:
         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

         __A			  =							torch.device(f"""cuda:{gpu_id}"""					)

         __A			  =							[self.text_encoder, self.prior]
         for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                     cpu_offload(_UpperCamelCase       ,  _UpperCamelCase					)




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Dict=0					)->   Tuple:
         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.'''					)

         __A			  =							torch.device(f"""cuda:{gpu_id}"""					)

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

         __A			  =							None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               __A  ,		__A			  =							cpu_offload_with_hook(_UpperCamelCase       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         if self.safety_checker is not None:
               __A  ,		__A			  =							cpu_offload_with_hook(self.safety_checker       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         # We'll offload the last model manually.
         __A			  =							hook




   @property
   def _lowerCAmelCase (self      :int					)->   int:
         if self.device != torch.device('''meta'''					) or not hasattr(self.text_encoder       ,  '''_hf_hook'''					):
               return self.device
         for module in self.text_encoder.modules():
               if (
                   hasattr(_UpperCamelCase       ,  '''_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




   def _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :int       ,  )->   Dict:
         len(_UpperCamelCase					) if isinstance(_UpperCamelCase       ,  _UpperCamelCase					) else 1

         # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
         __A			  =							0
         # get prompt text embeddings
         __A			  =							self.tokenizer(
             _UpperCamelCase       ,  padding='''max_length'''       ,  max_length=self.tokenizer.model_max_length       ,  truncation=_UpperCamelCase       ,  return_tensors='''pt'''       ,  )
         __A			  =							text_inputs.input_ids
         __A			  =							self.tokenizer(_UpperCamelCase       ,  padding='''longest'''       ,  return_tensors='''pt'''					).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
               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			  =							self.text_encoder(text_input_ids.to(_UpperCamelCase					)					)
         __A			  =							text_encoder_output.text_embeds

         __A			  =							prompt_embeds.repeat_interleave(_UpperCamelCase       ,  dim=0					)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         __A			  =							prompt_embeds / torch.linalg.norm(_UpperCamelCase       ,  dim=-1       ,  keepdim=_UpperCamelCase					)

         if do_classifier_free_guidance:
               __A			  =							torch.zeros_like(_UpperCamelCase					)

               # 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([negative_prompt_embeds, prompt_embeds]					)

         # Rescale the features to have unit variance
         __A			  =							math.sqrt(prompt_embeds.shape[1]					) * prompt_embeds

         return prompt_embeds






   @torch.no_grad()
   @replace_example_docstring(_UpperCamelCase					)
   def __call__(self      :Optional[Any]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :int = 1       ,  _UpperCamelCase      :int = 25       ,  _UpperCamelCase      :Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  _UpperCamelCase      :Optional[torch.FloatTensor] = None       ,  _UpperCamelCase      :float = 4.0       ,  _UpperCamelCase      :int = 64       ,  _UpperCamelCase      :Optional[str] = "pil"       ,  _UpperCamelCase      :bool = True       ,  )->   Optional[Any]:

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

         __A			  =							self._execution_device

         __A			  =							batch_size * num_images_per_prompt

         __A			  =							guidance_scale > 1.0
         __A			  =							self._encode_prompt(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         # prior

         self.scheduler.set_timesteps(_UpperCamelCase       ,  device=_UpperCamelCase					)
         __A			  =							self.scheduler.timesteps

         __A			  =							self.prior.config.num_embeddings
         __A			  =							self.prior.config.embedding_dim

         __A			  =							self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)       ,  prompt_embeds.dtype       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  self.scheduler       ,  )

         # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
         __A			  =							latents.reshape(latents.shape[0]       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         for i, t in enumerate(self.progress_bar(_UpperCamelCase					)					):
               # 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(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							self.prior(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  proj_embedding=_UpperCamelCase       ,  ).predicted_image_embedding

               # remove the variance
               __A  ,		__A			  =							noise_pred.split(
                   scaled_model_input.shape[2]       ,  dim=2					)  # batch_size, num_embeddings, embedding_dim

               if do_classifier_free_guidance is not None:
                     __A  ,		__A			  =							noise_pred.chunk(2					)
                     __A			  =							noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               __A			  =							self.scheduler.step(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  sample=_UpperCamelCase       ,  ).prev_sample

         if output_type == "latent":
               return ShapEPipelineOutput(images=_UpperCamelCase					)

         __A			  =							[]
         for i, latent in enumerate(_UpperCamelCase					):
               __A			  =							self.renderer.decode(
                   latent[None, :]       ,  _UpperCamelCase       ,  size=_UpperCamelCase       ,  ray_batch_size=4096       ,  n_coarse_samples=64       ,  n_fine_samples=128       ,  )
               images.append(_UpperCamelCase					)

         __A			  =							torch.stack(_UpperCamelCase					)

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

         __A			  =							images.cpu().numpy()

         if output_type == "pil":
               __A			  =							[self.numpy_to_pil(_UpperCamelCase					) for image in images]

         # Offload last model to CPU
         if hasattr(self       ,  '''final_offload_hook'''					) and self.final_offload_hook is not None:
               self.final_offload_hook.offload()

         if not return_dict:
               return (images,)

         return ShapEPipelineOutput(images=_UpperCamelCase					)





