



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def      __lowercase       (			a__							,       a__=0.999							,       a__="cosine"							,       )    ->			Optional[Any]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(a__       ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(a__       ):
																					return math.exp(t * -12.0       )

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

							__SCREAMING_SNAKE_CASE									= []
							for i in range(a__       ):
														__SCREAMING_SNAKE_CASE									= i / num_diffusion_timesteps
														__SCREAMING_SNAKE_CASE									= (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(a__       ) / alpha_bar_fn(a__       )							,       a__       )       )
							return torch.tensor(a__							,       dtype=torch.floataa       )

class        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			str      =  [e.name for e in KarrasDiffusionSchedulers]
						UpperCamelCase__      :			Dict      =  2






						@register_to_config
						def __init__(   self				,							_A = 1_000				,							_A = 0.0_0_0_8_5				,							_A = 0.0_1_2				,							_A = "linear"				,							_A = None				,							_A = "epsilon"				,							_A = "linspace"				,							_A = 0				,							):







													'''simple docstring'''

													if trained_betas is not None:
																				__SCREAMING_SNAKE_CASE									= torch.tensor(_A				,							dtype=torch.floataa		)
													elif beta_schedule == "linear":
																				__SCREAMING_SNAKE_CASE									= torch.linspace(_A				,							_A				,							_A				,							dtype=torch.floataa		)
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				__SCREAMING_SNAKE_CASE									= (
																				    torch.linspace(beta_start**0.5				,							beta_end**0.5				,							_A				,							dtype=torch.floataa		) ** 2
																				)
													elif beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				__SCREAMING_SNAKE_CASE									= betas_for_alpha_bar(_A		)
													else:
																				raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

													__SCREAMING_SNAKE_CASE									= 1.0 - self.betas
													__SCREAMING_SNAKE_CASE									= torch.cumprod(self.alphas				,							dim=0		)

													#  set all values
													self.set_timesteps(_A				,							_A				,							_A		)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if schedule_timesteps is None:
																				__SCREAMING_SNAKE_CASE									= self.timesteps

													__SCREAMING_SNAKE_CASE									= (schedule_timesteps == timestep).nonzero()

													# The sigma index that is taken for the **very** first `step`
													# is always the second index (or the last index if there is only 1)
													# This way we can ensure we don't accidentally skip a sigma in
													# case we start in the middle of the denoising schedule (e.g. for image-to-image)
													if len(self._index_counter		) == 0:
																				__SCREAMING_SNAKE_CASE									= 1 if len(_A		) > 1 else 0
													else:
																				__SCREAMING_SNAKE_CASE									= timestep.cpu().item() if torch.is_tensor(_A		) else timestep
																				__SCREAMING_SNAKE_CASE									= self._index_counter[timestep_int]

													return indices[pos].item()






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													if self.config.timestep_spacing in ["linspace", "trailing"]:
																				return self.sigmas.max()

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






						def 	_A     (   self				,							_A				,							_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.index_for_timestep(_A		)

													if self.state_in_first_order:
																				__SCREAMING_SNAKE_CASE									= self.sigmas[step_index]
													else:
																				__SCREAMING_SNAKE_CASE									= self.sigmas_interpol[step_index]

													__SCREAMING_SNAKE_CASE									= sample / ((sigma**2 + 1) ** 0.5)
													return sample






						def 	_A     (   self				,							_A				,							_A = None				,							_A = None				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= num_inference_steps

													__SCREAMING_SNAKE_CASE									= num_train_timesteps or self.config.num_train_timesteps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																				__SCREAMING_SNAKE_CASE									= np.linspace(0				,							num_train_timesteps - 1				,							_A				,							dtype=_A		)[::-1].copy()
													elif self.config.timestep_spacing == "leading":
																				__SCREAMING_SNAKE_CASE									= 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
																				__SCREAMING_SNAKE_CASE									= (np.arange(0				,							_A		) * step_ratio).round()[::-1].copy().astype(_A		)
																				timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																				__SCREAMING_SNAKE_CASE									= 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
																				__SCREAMING_SNAKE_CASE									= (np.arange(_A				,							0				,							-step_ratio		)).round().copy().astype(_A		)
																				timesteps -= 1
													else:
																				raise ValueError(
																				    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""		)

													__SCREAMING_SNAKE_CASE									= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(np.log(_A		)		).to(_A		)

													__SCREAMING_SNAKE_CASE									= np.interp(_A				,							np.arange(0				,							len(_A		)		)				,							_A		)
													__SCREAMING_SNAKE_CASE									= np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(_A		).to(device=_A		)

													# interpolate sigmas
													__SCREAMING_SNAKE_CASE									= sigmas.log().lerp(sigmas.roll(1		).log()				,							0.5		).exp()

													__SCREAMING_SNAKE_CASE									= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
													__SCREAMING_SNAKE_CASE									= torch.cat(
													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

													if str(_A		).startswith('mps'		):
																				# mps does not support float64
																				__SCREAMING_SNAKE_CASE									= torch.from_numpy(_A		).to(_A				,							dtype=torch.floataa		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.from_numpy(_A		).to(_A		)

													# interpolate timesteps
													__SCREAMING_SNAKE_CASE									= self.sigma_to_t(_A		).to(_A				,							dtype=timesteps.dtype		)
													__SCREAMING_SNAKE_CASE									= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,							dim=-1		).flatten()

													__SCREAMING_SNAKE_CASE									= torch.cat([timesteps[:1], interleaved_timesteps]		)

													__SCREAMING_SNAKE_CASE									= None

													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
													# we need an index counter
													__SCREAMING_SNAKE_CASE									= defaultdict(_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= sigma.log()

													# get distribution
													__SCREAMING_SNAKE_CASE									= log_sigma - self.log_sigmas[:, None]

													# get sigmas range
													__SCREAMING_SNAKE_CASE									= dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
													__SCREAMING_SNAKE_CASE									= low_idx + 1

													__SCREAMING_SNAKE_CASE									= self.log_sigmas[low_idx]
													__SCREAMING_SNAKE_CASE									= self.log_sigmas[high_idx]

													# interpolate sigmas
													__SCREAMING_SNAKE_CASE									= (low - log_sigma) / (low - high)
													__SCREAMING_SNAKE_CASE									= w.clamp(0				,							1		)

													# transform interpolation to time range
													__SCREAMING_SNAKE_CASE									= (1 - w) * low_idx + w * high_idx
													__SCREAMING_SNAKE_CASE									= t.view(sigma.shape		)
													return t






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.sample is None






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A = True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.index_for_timestep(_A		)

													# advance index counter by 1
													__SCREAMING_SNAKE_CASE									= timestep.cpu().item() if torch.is_tensor(_A		) else timestep
													self._index_counter[timestep_int] += 1

													if self.state_in_first_order:
																				__SCREAMING_SNAKE_CASE									= self.sigmas[step_index]
																				__SCREAMING_SNAKE_CASE									= self.sigmas_interpol[step_index + 1]
																				__SCREAMING_SNAKE_CASE									= self.sigmas[step_index + 1]
													else:
																				# 2nd order / KDPM2's method
																				__SCREAMING_SNAKE_CASE									= self.sigmas[step_index - 1]
																				__SCREAMING_SNAKE_CASE									= self.sigmas_interpol[step_index]
																				__SCREAMING_SNAKE_CASE									= self.sigmas[step_index]

													# currently only gamma=0 is supported. This usually works best anyways.
													# We can support gamma in the future but then need to scale the timestep before
													# passing it to the model which requires a change in API
													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				__SCREAMING_SNAKE_CASE									= sigma_hat if self.state_in_first_order else sigma_interpol
																				__SCREAMING_SNAKE_CASE									= sample - sigma_input * model_output
													elif self.config.prediction_type == "v_prediction":
																				__SCREAMING_SNAKE_CASE									= sigma_hat if self.state_in_first_order else sigma_interpol
																				__SCREAMING_SNAKE_CASE									= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																				    sample / (sigma_input**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`"""		)

													if self.state_in_first_order:
																				# 2. Convert to an ODE derivative for 1st order
																				__SCREAMING_SNAKE_CASE									= (sample - pred_original_sample) / sigma_hat
																				# 3. delta timestep
																				__SCREAMING_SNAKE_CASE									= sigma_interpol - sigma_hat

																				# store for 2nd order step
																				__SCREAMING_SNAKE_CASE									= sample
													else:
																				# DPM-Solver-2
																				# 2. Convert to an ODE derivative for 2nd order
																				__SCREAMING_SNAKE_CASE									= (sample - pred_original_sample) / sigma_interpol

																				# 3. delta timestep
																				__SCREAMING_SNAKE_CASE									= sigma_next - sigma_hat

																				__SCREAMING_SNAKE_CASE									= self.sample
																				__SCREAMING_SNAKE_CASE									= None

													__SCREAMING_SNAKE_CASE									= sample + derivative * dt

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.sigmas.to(device=original_samples.device				,							dtype=original_samples.dtype		)
													if original_samples.device.type == "mps" and torch.is_floating_point(_A		):
																				# mps does not support float64
																				__SCREAMING_SNAKE_CASE									= self.timesteps.to(original_samples.device				,							dtype=torch.floataa		)
																				__SCREAMING_SNAKE_CASE									= timesteps.to(original_samples.device				,							dtype=torch.floataa		)
													else:
																				__SCREAMING_SNAKE_CASE									= self.timesteps.to(original_samples.device		)
																				__SCREAMING_SNAKE_CASE									= timesteps.to(original_samples.device		)

													__SCREAMING_SNAKE_CASE									= [self.index_for_timestep(_A				,							_A		) for t in timesteps]

													__SCREAMING_SNAKE_CASE									= sigmas[step_indices].flatten()
													while len(sigma.shape		) < len(original_samples.shape		):
																				__SCREAMING_SNAKE_CASE									= sigma.unsqueeze(-1		)

													__SCREAMING_SNAKE_CASE									= original_samples + noise * sigma
													return noisy_samples






						def __len__(   self		):







													'''simple docstring'''

													return self.config.num_train_timesteps











import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__		:					Optional[int]							=logging.get_logger(__name__)






def      __lowercase       (			a__							,       a__=False       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= []
							for i in range(config.num_hidden_layers       ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")       )
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")       )
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")       )
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")       )
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")       )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('cls_token', 'deit.embeddings.cls_token'),
							        ('dist_token', 'deit.embeddings.distillation_token'),
							        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
							        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
							        ('pos_embed', 'deit.embeddings.position_embeddings'),
							    ]       )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														        ('pre_logits.fc.weight', 'pooler.dense.weight'),
														        ('pre_logits.fc.bias', 'pooler.dense.bias'),
														    ]       )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														__SCREAMING_SNAKE_CASE									= [(pair[0], pair[1][4:]) if pair[1].startswith('deit'       ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('norm.weight', 'deit.layernorm.weight'),
														        ('norm.bias', 'deit.layernorm.bias'),
														        ('head.weight', 'cls_classifier.weight'),
														        ('head.bias', 'cls_classifier.bias'),
														        ('head_dist.weight', 'distillation_classifier.weight'),
														        ('head_dist.bias', 'distillation_classifier.bias'),
														    ]       )

							return rename_keys






def      __lowercase       (			a__							,       a__							,       a__=False       )    ->			Tuple:
							for i in range(config.num_hidden_layers       ):
														if base_model:
																					__SCREAMING_SNAKE_CASE									= ''
														else:
																					__SCREAMING_SNAKE_CASE									= 'deit.'
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""       )
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""       )
														# next, add query, keys and values (in that order) to the state dict
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    : config.hidden_size, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[: config.hidden_size]
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    -config.hidden_size :, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[-config.hidden_size :]






def      __lowercase       (			a__							,       a__							,       a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= dct.pop(a__       )
							__SCREAMING_SNAKE_CASE									= val






def      __lowercase       (			)    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )
							return im






@torch.no_grad()
def      __lowercase       (			a__							,       a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= DeiTConfig()
							# all deit models have fine-tuned heads
							__SCREAMING_SNAKE_CASE									= False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__SCREAMING_SNAKE_CASE									= 10_00
							__SCREAMING_SNAKE_CASE									= 'huggingface/label-files'
							__SCREAMING_SNAKE_CASE									= 'imagenet-1k-id2label.json'
							__SCREAMING_SNAKE_CASE									= json.load(open(hf_hub_download(a__							,       a__							,       repo_type='dataset'       )							,       'r'       )       )
							__SCREAMING_SNAKE_CASE									= {int(a__       ): v for k, v in idalabel.items()}
							__SCREAMING_SNAKE_CASE									= idalabel
							__SCREAMING_SNAKE_CASE									= {v: k for k, v in idalabel.items()}
							__SCREAMING_SNAKE_CASE									= int(deit_name[-6:-4]       )
							__SCREAMING_SNAKE_CASE									= int(deit_name[-3:]       )
							# size of the architecture
							if deit_name[9:].startswith('tiny'       ):
														__SCREAMING_SNAKE_CASE									= 1_92
														__SCREAMING_SNAKE_CASE									= 7_68
														__SCREAMING_SNAKE_CASE									= 12
														__SCREAMING_SNAKE_CASE									= 3
							elif deit_name[9:].startswith('small'       ):
														__SCREAMING_SNAKE_CASE									= 3_84
														__SCREAMING_SNAKE_CASE									= 15_36
														__SCREAMING_SNAKE_CASE									= 12
														__SCREAMING_SNAKE_CASE									= 6
							if deit_name[9:].startswith('base'       ):
														pass
							elif deit_name[4:].startswith('large'       ):
														__SCREAMING_SNAKE_CASE									= 10_24
														__SCREAMING_SNAKE_CASE									= 40_96
														__SCREAMING_SNAKE_CASE									= 24
														__SCREAMING_SNAKE_CASE									= 16

							# load original model from timm
							__SCREAMING_SNAKE_CASE									= timm.create_model(a__							,       pretrained=a__       )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__SCREAMING_SNAKE_CASE									= timm_model.state_dict()
							__SCREAMING_SNAKE_CASE									= create_rename_keys(a__							,       a__       )
							for src, dest in rename_keys:
														rename_key(a__							,       a__							,       a__       )
							read_in_q_k_v(a__							,       a__							,       a__       )

							# load HuggingFace model
							__SCREAMING_SNAKE_CASE									= DeiTForImageClassificationWithTeacher(a__       ).eval()
							model.load_state_dict(a__       )

							# Check outputs on an image, prepared by DeiTImageProcessor
							__SCREAMING_SNAKE_CASE									= int(
							    (2_56 / 2_24) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							__SCREAMING_SNAKE_CASE									= DeiTImageProcessor(size=a__							,       crop_size=config.image_size       )
							__SCREAMING_SNAKE_CASE									= image_processor(images=prepare_img()							,       return_tensors='pt'       )
							__SCREAMING_SNAKE_CASE									= encoding['pixel_values']
							__SCREAMING_SNAKE_CASE									= model(a__       )

							__SCREAMING_SNAKE_CASE									= timm_model(a__       )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(a__							,       outputs.logits							,       atol=1E-3       )

							Path(a__       ).mkdir(exist_ok=a__       )
							print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""       )
							model.save_pretrained(a__       )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
							image_processor.save_pretrained(a__       )


if __name__ == "__main__":
			lowerCAmelCase__		:					Union[str, Any]							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--deit_name''',
			    default='''vit_deit_base_distilled_patch16_224''',
			    type=str,
			    help='''Name of the DeiT timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			lowerCAmelCase__		:					str							=parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






