

# 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 						lowercase     :


		"""simple docstring"""




		a__       							=	42
		a__       							=	42

		# setable values
		a__       							=	42
		a__       							=	42
		a__       							=	None


		@classmethod
		def 		A__       (					cls						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							):
			return cls(
			    common=__snake_case						,							final_alpha_cumprod=__snake_case						,							init_noise_sigma=__snake_case						,							timesteps=__snake_case						,							)







@dataclass
class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		a__       							=	42







class 						lowercase     (					_lowercase     ,				_lowercase				):


		"""simple docstring"""




		a__       							=	[e.name for e in FlaxKarrasDiffusionSchedulers]

		a__       							=	42


		@property
		def 		A__       (					self):
			return True


		@register_to_config
		def __init__(					self						,							__snake_case = 10_00						,							__snake_case = 0.0_0_0_1						,							__snake_case = 0.0_2						,							__snake_case = "linear"						,							__snake_case = None						,							__snake_case = True						,							__snake_case = 0						,							__snake_case = "epsilon"						,							__snake_case = jnp.floataa						,							):
			_UpperCamelCase :		Dict								=    dtype


		def 		A__       (					self						,							__snake_case = None):
			if common is None:
				_UpperCamelCase :		Tuple								=    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.
			_UpperCamelCase :		Union[str, Any]								=    (
			    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
			_UpperCamelCase :		Optional[int]								=    jnp.array(1.0						,							dtype=self.dtype)

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

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


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case = None):
			return sample


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case = ()):
			_UpperCamelCase :		Dict								=    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
			_UpperCamelCase :		Optional[int]								=    (jnp.arange(0						,							__snake_case) * step_ratio).round()[::-1] + self.config.steps_offset

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


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Optional[Any]								=    state.common.alphas_cumprod[timestep]
			_UpperCamelCase :		Any								=    jnp.where(
			    prev_timestep >= 0						,							state.common.alphas_cumprod[prev_timestep]						,							state.final_alpha_cumprod)
			_UpperCamelCase :		Optional[int]								=    1 - alpha_prod_t
			_UpperCamelCase :		Optional[int]								=    1 - alpha_prod_t_prev

			_UpperCamelCase :		Tuple								=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

			return variance


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case = 0.0						,							__snake_case = 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)
			_UpperCamelCase :		Tuple								=    timestep - self.config.num_train_timesteps // state.num_inference_steps

			_UpperCamelCase :		int								=    state.common.alphas_cumprod
			_UpperCamelCase :		int								=    state.final_alpha_cumprod

			# 2. compute alphas, betas
			_UpperCamelCase :		int								=    alphas_cumprod[timestep]
			_UpperCamelCase :		Union[str, Any]								=    jnp.where(prev_timestep >= 0						,							alphas_cumprod[prev_timestep]						,							__snake_case)

			_UpperCamelCase :		Dict								=    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":
				_UpperCamelCase :		Union[str, Any]								=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				_UpperCamelCase :		Any								=    model_output
			elif self.config.prediction_type == "sample":
				_UpperCamelCase :		List[str]								=    model_output
				_UpperCamelCase :		List[Any]								=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
			elif self.config.prediction_type == "v_prediction":
				_UpperCamelCase :		List[str]								=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
				_UpperCamelCase :		Union[str, Any]								=    (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)
			_UpperCamelCase :		str								=    self._get_variance(__snake_case						,							__snake_case						,							__snake_case)
			_UpperCamelCase :		Dict								=    eta * variance ** 0.5

			# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			_UpperCamelCase :		Dict								=    (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
			_UpperCamelCase :		Optional[Any]								=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

			if not return_dict:
				return (prev_sample, state)

			return FlaxDDIMSchedulerOutput(prev_sample=__snake_case						,							state=__snake_case)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							):
			return add_noise_common(state.common						,							__snake_case						,							__snake_case						,							__snake_case)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							):
			return get_velocity_common(state.common						,							__snake_case						,							__snake_case						,							__snake_case)






		def __len__(					self):
			return self.config.num_train_timesteps







import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

	_UpperCamelCase :		Any								=    out_proj_weights
	_UpperCamelCase :		Optional[int]								=    out_proj_bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''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        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    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
	_UpperCamelCase :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		str								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase :		Dict								=    CLIPConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    CLIPConfig(projection_dim=5_1_2				,							text_config={}				,							vision_config={}						)

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)
	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)

	hf_model.save_pretrained(UpperCAmelCase_						)


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)




