






"""simple docstring"""



import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


A__	:    Any			 = {
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}

def 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     int:
							lowerCamelCase_	:							List[str]									={}
							state_dict.pop("pixel_mean"    ,						lowerCamelCase__  )
							state_dict.pop("pixel_std"    ,						lowerCamelCase__  )

							lowerCamelCase_	:							List[Any]									=r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

							for key, value in state_dict.items():
														for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																					if key_to_modify in key:
																												lowerCamelCase_	:							str									=key.replace(lowerCamelCase__    ,						lowerCamelCase__  )

														if re.match(lowerCamelCase__    ,						lowerCamelCase__  ):
																					lowerCamelCase_	:							Optional[Any]									=int(re.match(lowerCamelCase__    ,						lowerCamelCase__  ).group(2  )  )
																					if layer_nb == 0:
																												lowerCamelCase_	:							List[Any]									=key.replace("layers.0"    ,						"proj_in"  )
																					elif layer_nb == 1:
																												lowerCamelCase_	:							Any									=key.replace("layers.1"    ,						"layers.0"  )
																					elif layer_nb == 2:
																												lowerCamelCase_	:							Optional[Any]									=key.replace("layers.2"    ,						"proj_out"  )

														lowerCamelCase_	:							str									=value

							lowerCamelCase_	:							Any									=model_state_dict[
							    "prompt_encoder.shared_embedding.positional_embedding"
							]

							return model_state_dict

def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   Optional[int]    ,						lowerCamelCase__     :   str="ybelkada/segment-anything"  )    ->     Any:
							lowerCamelCase_	:							int									=hf_hub_download(lowerCamelCase__    ,						F"""checkpoints/{model_name}.pth"""  )

							if "sam_vit_b" in model_name:
														lowerCamelCase_	:							Optional[Any]									=SamConfig()
							elif "sam_vit_l" in model_name:
														lowerCamelCase_	:							Union[str, Any]									=SamVisionConfig(
														    hidden_size=1_024    ,						num_hidden_layers=24    ,						num_attention_heads=16    ,						global_attn_indexes=[5, 11, 17, 23]    ,						)

														lowerCamelCase_	:							Optional[Any]									=SamConfig(
														    vision_config=lowerCamelCase__    ,						)
							elif "sam_vit_h" in model_name:
														lowerCamelCase_	:							List[str]									=SamVisionConfig(
														    hidden_size=1_280    ,						num_hidden_layers=32    ,						num_attention_heads=16    ,						global_attn_indexes=[7, 15, 23, 31]    ,						)

														lowerCamelCase_	:							Tuple									=SamConfig(
														    vision_config=lowerCamelCase__    ,						)

							lowerCamelCase_	:							Optional[Any]									=torch.load(lowerCamelCase__    ,						map_location="cpu"  )
							lowerCamelCase_	:							Optional[Any]									=replace_keys(lowerCamelCase__  )

							lowerCamelCase_	:							Any									=SamImageProcessor()

							lowerCamelCase_	:							Optional[int]									=SamProcessor(image_processor=lowerCamelCase__  )
							lowerCamelCase_	:							Dict									=SamModel(lowerCamelCase__  )

							hf_model.load_state_dict(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[Any]									=hf_model.to("cuda"  )

							lowerCamelCase_	:							int									="https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
							lowerCamelCase_	:							int									=Image.open(requests.get(lowerCamelCase__    ,						stream=lowerCamelCase__  ).raw  ).convert("RGB"  )

							lowerCamelCase_	:							int									=[[[400, 650]]]
							lowerCamelCase_	:							Any									=[[1]]

							lowerCamelCase_	:							int									=processor(images=np.array(lowerCamelCase__  )    ,						return_tensors="pt"  ).to("cuda"  )

							with torch.no_grad():
														lowerCamelCase_	:							Optional[Any]									=hf_model(**lowerCamelCase__  )
							lowerCamelCase_	:							Dict									=output.iou_scores.squeeze()

							if model_name == "sam_vit_h_4b8939":
														assert scores[-1].item() == 0.579_8902_5115_9668

														lowerCamelCase_	:							Optional[Any]									=processor(
														    images=np.array(lowerCamelCase__  )    ,						input_points=lowerCamelCase__    ,						input_labels=lowerCamelCase__    ,						return_tensors="pt"  ).to("cuda"  )

														with torch.no_grad():
																					lowerCamelCase_	:							Tuple									=hf_model(**lowerCamelCase__  )
														lowerCamelCase_	:							List[str]									=output.iou_scores.squeeze()

														assert scores[-1].item() == 0.9712_6030_9219_3604

														lowerCamelCase_	:							int									=((75, 275, 1_725, 850),)

														lowerCamelCase_	:							Dict									=processor(images=np.array(lowerCamelCase__  )    ,						input_boxes=lowerCamelCase__    ,						return_tensors="pt"  ).to("cuda"  )

														with torch.no_grad():
																					lowerCamelCase_	:							Union[str, Any]									=hf_model(**lowerCamelCase__  )
														lowerCamelCase_	:							List[str]									=output.iou_scores.squeeze()

														assert scores[-1].item() == 0.8686_0156_0592_6514

														# Test with 2 points and 1 image.
														lowerCamelCase_	:							Optional[int]									=[[[400, 650], [800, 650]]]
														lowerCamelCase_	:							Dict									=[[1, 1]]

														lowerCamelCase_	:							Optional[int]									=processor(
														    images=np.array(lowerCamelCase__  )    ,						input_points=lowerCamelCase__    ,						input_labels=lowerCamelCase__    ,						return_tensors="pt"  ).to("cuda"  )

														with torch.no_grad():
																					lowerCamelCase_	:							List[str]									=hf_model(**lowerCamelCase__  )
														lowerCamelCase_	:							Dict									=output.iou_scores.squeeze()

														assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
						A__	:    Optional[Any]			 = argparse.ArgumentParser()
						A__	:    Any			 = ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
						parser.add_argument(
						    '--model_name',
						    default='sam_vit_h_4b8939',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub after converting',
						)
						parser.add_argument(
						    '--model_hub_id',
						    default='ybelkada/segment-anything',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)

						A__	:    Dict			 = parser.parse_args()

						convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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__      :
			_UpperCAmelCase		:CommonSchedulerState

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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=jnp.array(1.0       ,	dtype=self.dtype )

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

										return DDPMSchedulerState.create(
										    common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=jnp.split(snake_case__       ,	sample.shape[1]       ,	axis=1 )
										else:
																	lowerCamelCase_	:							List[str]									=None

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																	    " for the FlaxDDPMScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(snake_case__       ,	-1       ,	1 )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							Optional[Any]									=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																	lowerCamelCase_	:							Union[str, Any]									=jax.random.split(snake_case__       ,	num=1 )
																	lowerCamelCase_	:							List[Any]									=jax.random.normal(snake_case__       ,	shape=model_output.shape       ,	dtype=self.dtype )
																	return (self._get_variance(snake_case__       ,	snake_case__       ,	predicted_variance=snake_case__ ) ** 0.5) * noise

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


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



