

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel
def        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      Tuple					)				->							Optional[Any]:
 __A   : List[str]								= pt_attn_layer.in_proj_weight.chunk(3 ,dim=0					)
 __A   : List[Any]								= pt_attn_layer.in_proj_bias.chunk(3 ,dim=0					)

 __A   : Union[str, Any]								= pt_attn_layer.out_proj.weight
 __A   : Optional[int]								= pt_attn_layer.out_proj.bias

 __A   : Dict								= q_proj
 __A   : Tuple								= q_proj_bias

 __A   : Optional[Any]								= k_proj
 __A   : str								= k_proj_bias

 __A   : List[Any]								= v_proj
 __A   : Any								= v_proj_bias

 __A   : Dict								= out_proj_weights
 __A   : Optional[Any]								= out_proj_bias
def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict ,a__						:      List[Any]					)				->							Union[str, Any]:
 copy_linear(hf_mlp.fca ,pt_mlp.c_fc					)
 copy_linear(hf_mlp.fca ,pt_mlp.c_proj					)
def        __SCREAMING_SNAKE_CASE    (      a__						:      Any ,a__						:      List[Any]					)				->							int:
 __A   : str								= pt_linear.weight
 __A   : Dict								= pt_linear.bias
def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Optional[int]					)				->							Optional[int]:
 # copy layer norms
 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        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      Any					)				->							List[Any]:
 for hf_layer, pt_layer in zip(a__ ,a__					):
  copy_layer(a__ ,a__					)
def        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any] ,a__						:      Tuple					)				->							Dict:
 # copy  embeds
 __A   : Any								= pt_model.token_embedding.weight
 __A   : List[str]								= 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        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      Tuple					)				->							List[str]:
 # copy projection
 __A   : List[Any]								= pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model ,a__					)
def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      Any					)				->							Any:
 # copy projection
 __A   : Dict								= 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   : List[str]								= pt_model.visual.conva.weight.data
 __A   : Optional[Any]								= pt_model.visual.class_embedding
 __A   : Dict								= 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        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      List[Any] ,a__						:      List[str]=None					)				->							Tuple:
 if config_path is not None:
  __A   : int								= CLIPConfig.from_pretrained(a__					)
 else:
  __A   : Optional[Any]								= CLIPConfig(projection_dim=512 ,text_config={} ,vision_config={}					)

 __A   : Union[str, Any]								= CLIPModel(a__					).eval()

 __A   : Any								= load(a__ ,device="""cpu""" ,jit=a__					)
 __A   : Tuple								= pt_model.eval()

 copy_text_model_and_projection(a__ ,a__					)
 copy_vison_model_and_projection(a__ ,a__					)
 __A   : int								= pt_model.logit_scale

 __A   : List[Any]								= torch.arange(0 ,77					).unsqueeze(0					)
 __A   : str								= torch.randn(1 ,3 ,224 ,224					)

 __A   : Optional[int]								= hf_model(input_ids=a__ ,pixel_values=a__ ,return_dict=a__					)
 __A   : int								= hf_outputs.logits_per_image
 __A   : str								= hf_outputs.logits_per_text
 __A   : List[Any]								= pt_model(a__ ,a__					)

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

 hf_model.save_pretrained(a__					)


if __name__ == "__main__":
   UpperCAmelCase_       :				Union[str, Any]		   =      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_       :				Optional[int]		   =      parser.parse_args()

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








# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

	# setable values
	_lowercase		:      jnp.ndarray
	_lowercase		:      jnp.ndarray
	_lowercase		:      Optional[int]    				=							None




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= CommonSchedulerState.create(self       )

		# At every step in ddim, we are looking into the previous alphas_cumprod
		# For the final step, there is no previous alphas_cumprod because we are already at 0
		# `set_alpha_to_one` decides whether we set this parameter simply to one or
		# whether we use the final alpha of the "non-previous" one.
		__A   : Tuple								= (
		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
		)

		# standard deviation of the initial noise distribution
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]

		return DDIMSchedulerState.create(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
		# creates integer timesteps by multiplying by ratio
		# rounding to avoid issues when num_inference_step is power of 3
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

		return state.replace(
		    num_inference_steps=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		if state.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		if self.config.prediction_type == "epsilon":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
			    """ `v_prediction`"""       )

		# 4. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps





