

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Dict				):





  '''simple docstring'''







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

  a__    =pt_attn_layer.out_proj.weight
  a__    =pt_attn_layer.out_proj.bias

  a__    =q_proj
  a__    =q_proj_bias

  a__    =k_proj
  a__    =k_proj_bias

  a__    =v_proj
  a__    =v_proj_bias

  a__    =out_proj_weights
  a__    =out_proj_bias




def      UpperCAmelCase__							(     _A :					str      , _A :					int				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Any				):





  '''simple docstring'''







  a__    =pt_linear.weight
  a__    =pt_linear.bias




def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					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      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					List[str]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Dict				):





  '''simple docstring'''







  a__    =pt_model.token_embedding.weight
  a__    =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      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Dict				):





  '''simple docstring'''







  a__    =pt_model.text_projection.data.T

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




def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					Any				):





  '''simple docstring'''







  a__    =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
  a__    =pt_model.visual.conva.weight.data
  a__    =pt_model.visual.class_embedding
  a__    =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      UpperCAmelCase__							(     _A :					List[str]      , _A :					Dict      , _A :					str=None				):





  '''simple docstring'''







  if config_path is not None:
    a__    =CLIPConfig.from_pretrained(lowerCAmelCase__				)
  else:
    a__    =CLIPConfig(projection_dim=5_12      , text_config={}      , vision_config={}				)

  a__    =CLIPModel(lowerCAmelCase__				).eval()

  a__    =load(lowerCAmelCase__      , device='''cpu'''      , jit=lowerCAmelCase__				)
  a__    =pt_model.eval()

  copy_text_model_and_projection(lowerCAmelCase__      , lowerCAmelCase__				)
  copy_vison_model_and_projection(lowerCAmelCase__      , lowerCAmelCase__				)
  a__    =pt_model.logit_scale

  a__    =torch.arange(0      , 77				).unsqueeze(0				)
  a__    =torch.randn(1      , 3      , 2_24      , 2_24				)

  a__    =hf_model(input_ids=lowerCAmelCase__      , pixel_values=lowerCAmelCase__      , return_dict=lowerCAmelCase__				)
  a__    =hf_outputs.logits_per_image
  a__    =hf_outputs.logits_per_text
  a__    =pt_model(lowerCAmelCase__      , lowerCAmelCase__				)

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

  hf_model.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
       lowerCamelCase 	=   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''')
       lowerCamelCase 	=   parser.parse_args()

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





"""simple docstring"""







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None




def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Optional[Any]=0.999       ,  lowerCAmelCase__ :							Optional[int]="cosine"       ,  )		->  Optional[Any]:

  """simple docstring"""




  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCAmelCase__ :							Optional[Any]		):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCAmelCase__ :							Union[str, Any]		):
      return math.exp(t * -12.0		)

  else:
    raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

  lowerCAmelCase_  :							List[str]								=					[]
  for i in range(lowerCAmelCase__		):
    lowerCAmelCase_  :							str								=					i / num_diffusion_timesteps
    lowerCAmelCase_  :							Optional[int]								=					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__		) / alpha_bar_fn(lowerCAmelCase__		)       ,  lowerCAmelCase__		)		)
  return torch.tensor(lowerCAmelCase__       ,  dtype=torch.floataa		)







class  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	[e.name for e in KarrasDiffusionSchedulers]
      _SCREAMING_SNAKE_CASE    							=	1



      @register_to_config
      def __init__( self							: str       ,     SCREAMING_SNAKE_CASE_							: int = 1_0_0_0       ,     SCREAMING_SNAKE_CASE_							: float = 0.00_01       ,     SCREAMING_SNAKE_CASE_							: float = 0.02       ,     SCREAMING_SNAKE_CASE_							: str = "linear"       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[np.ndarray, List[float]]] = None       ,     SCREAMING_SNAKE_CASE_							: str = "epsilon"       ,     SCREAMING_SNAKE_CASE_							: str = "linspace"       ,     SCREAMING_SNAKE_CASE_							: int = 0       ,     ):
        if trained_betas is not None:
          lowerCAmelCase_  :							int								=					torch.tensor(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "linear":
          lowerCAmelCase_  :							Union[str, Any]								=					torch.linspace(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowerCAmelCase_  :							Dict								=					(
              torch.linspace(beta_start**0.5       ,     beta_end**0.5       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowerCAmelCase_  :							Optional[int]								=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE_			)
        else:
          raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

        lowerCAmelCase_  :							str								=					1.0 - self.betas
        lowerCAmelCase_  :							List[Any]								=					torch.cumprod(self.alphas       ,     dim=0			)

        lowerCAmelCase_  :							Dict								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							Optional[int]								=					np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							Optional[int]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)

        # setable values
        lowerCAmelCase_  :							Union[str, Any]								=					None
        lowerCAmelCase_  :							List[Any]								=					np.linspace(0       ,     num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[::-1].copy()
        lowerCAmelCase_  :							int								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[str]								=					False



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]			):
        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							List[Any]								=					timestep.to(self.timesteps.device			)
        lowerCAmelCase_  :							Any								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							List[str]								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Optional[int]								=					sample / ((sigma**2 + 1) ** 0.5)
        lowerCAmelCase_  :							Dict								=					True
        return sample



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Union[str, torch.device] = None			):
        lowerCAmelCase_  :							Optional[int]								=					num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
          lowerCAmelCase_  :							Any								=					np.linspace(0       ,     self.config.num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowerCAmelCase_  :							int								=					self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							List[Any]								=					(np.arange(0       ,     SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          lowerCAmelCase_  :							List[Any]								=					self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							int								=					(np.arange(self.config.num_train_timesteps       ,     0       ,     -step_ratio			)).round().copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps -= 1
        else:
          raise ValueError(
              F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

        lowerCAmelCase_  :							Optional[int]								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							List[str]								=					np.interp(SCREAMING_SNAKE_CASE_       ,     np.arange(0       ,     len(SCREAMING_SNAKE_CASE_			)			)       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Union[str, Any]								=					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
          # mps does not support float64
          lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							List[Any]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):

        if (
            isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.IntTensor			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.LongTensor			)
        ):
          raise ValueError(
              (
                  'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                  ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                  ' one of the `scheduler.timesteps` as a timestep.'
              )       ,     )

        if not self.is_scale_input_called:
          logger.warning(
              'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
              'See `StableDiffusionPipeline` for a usage example.'			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							Union[str, Any]								=					timestep.to(self.timesteps.device			)

        lowerCAmelCase_  :							Optional[int]								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							Dict								=					self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowerCAmelCase_  :							List[str]								=					sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowerCAmelCase_  :							Tuple								=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
          raise NotImplementedError('prediction_type not implemented yet: sample'			)
        else:
          raise ValueError(
              F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

        lowerCAmelCase_  :							Any								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Union[str, Any]								=					self.sigmas[step_index + 1]
        lowerCAmelCase_  :							List[str]								=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase_  :							Dict								=					(sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowerCAmelCase_  :							Dict								=					(sample - pred_original_sample) / sigma

        lowerCAmelCase_  :							Tuple								=					sigma_down - sigma

        lowerCAmelCase_  :							List[str]								=					sample + derivative * dt

        lowerCAmelCase_  :							Any								=					model_output.device
        lowerCAmelCase_  :							Optional[int]								=					randn_tensor(model_output.shape       ,     dtype=model_output.dtype       ,     device=SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=SCREAMING_SNAKE_CASE_       ,     pred_original_sample=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     ):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase_  :							int								=					self.sigmas.to(device=original_samples.device       ,     dtype=original_samples.dtype			)
        if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_			):
          # mps does not support float64
          lowerCAmelCase_  :							Any								=					self.timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
          lowerCAmelCase_  :							str								=					timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							Dict								=					self.timesteps.to(original_samples.device			)
          lowerCAmelCase_  :							List[str]								=					timesteps.to(original_samples.device			)

        lowerCAmelCase_  :							Any								=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase_  :							Dict								=					sigmas[step_indices].flatten()
        while len(sigma.shape			) < len(original_samples.shape			):
          lowerCAmelCase_  :							Optional[int]								=					sigma.unsqueeze(-1			)

        lowerCAmelCase_  :							List[str]								=					original_samples + noise * sigma
        return noisy_samples





      def __len__( self							: List[Any]			):
        return self.config.num_train_timesteps





