'''simple docstring'''

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 							UpperCamelCase__			(			a__			,      a__=0.999			,      a__="cosine"			,      ):


						'''simple docstring'''






						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}'''      )

						_lowerCAmelCase										=[]
						for i in range(a__      ):
												_lowerCAmelCase										=i / num_diffusion_timesteps
												_lowerCAmelCase										=(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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]	       =							[e.name for e in KarrasDiffusionSchedulers]
		lowercase   :      Any	       =							2






		@register_to_config
		def __init__(   self		,						__A = 1000		,						__A = 0.00_085		,						__A = 0.012		,						__A = "linear"		,						__A = None		,						__A = "epsilon"		,						__A = "linspace"		,						__A = 0		,						) ->     str:
								if trained_betas is not None:
														_lowerCAmelCase										=torch.tensor(__A		,						dtype=torch.floataa				)
								elif beta_schedule == "linear":
														_lowerCAmelCase										=torch.linspace(__A		,						__A		,						__A		,						dtype=torch.floataa				)
								elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_lowerCAmelCase										=(
														    torch.linspace(beta_start**0.5		,						beta_end**0.5		,						__A		,						dtype=torch.floataa				) ** 2
														)
								elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_lowerCAmelCase										=betas_for_alpha_bar(__A				)
								else:
														raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''				)

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     Optional[Any]:
								if schedule_timesteps is None:
														_lowerCAmelCase										=self.timesteps

								_lowerCAmelCase										=(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:
														_lowerCAmelCase										=1 if len(__A				) > 1 else 0
								else:
														_lowerCAmelCase										=timestep.cpu().item() if torch.is_tensor(__A				) else timestep
														_lowerCAmelCase										=self._index_counter[timestep_int]

								return indices[pos].item()






		@property
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								# 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 		UpperCamelCase__       (   self		,						__A		,						__A		,						) ->     torch.FloatTensor:
								_lowerCAmelCase										=self.index_for_timestep(__A				)

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						) ->     List[Any]:
								_lowerCAmelCase										=num_inference_steps

								_lowerCAmelCase										=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":
														_lowerCAmelCase										=np.linspace(0		,						num_train_timesteps - 1		,						__A		,						dtype=__A				)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
														_lowerCAmelCase										=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										=(np.arange(0		,						__A				) * step_ratio).round()[::-1].copy().astype(__A				)
														timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
														_lowerCAmelCase										=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										=(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\'.'''				)

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

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

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

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

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

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

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

								_lowerCAmelCase										=None

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								# get log sigma
								_lowerCAmelCase										=sigma.log()

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

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

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

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

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






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								return self.sample is None






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = True		,						) ->     Union[SchedulerOutput, Tuple]:
								_lowerCAmelCase										=self.index_for_timestep(__A				)

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

								if self.state_in_first_order:
														_lowerCAmelCase										=self.sigmas[step_index]
														_lowerCAmelCase										=self.sigmas_interpol[step_index + 1]
														_lowerCAmelCase										=self.sigmas[step_index + 1]
								else:
														# 2nd order / KDPM2's method
														_lowerCAmelCase										=self.sigmas[step_index - 1]
														_lowerCAmelCase										=self.sigmas_interpol[step_index]
														_lowerCAmelCase										=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
								_lowerCAmelCase										=0
								_lowerCAmelCase										=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":
														_lowerCAmelCase										=sigma_hat if self.state_in_first_order else sigma_interpol
														_lowerCAmelCase										=sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
														_lowerCAmelCase										=sigma_hat if self.state_in_first_order else sigma_interpol
														_lowerCAmelCase										=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
														_lowerCAmelCase										=(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														_lowerCAmelCase										=sigma_interpol - sigma_hat

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

														# 3. delta timestep
														_lowerCAmelCase										=sigma_next - sigma_hat

														_lowerCAmelCase										=self.sample
														_lowerCAmelCase										=None

								_lowerCAmelCase										=sample + derivative * dt

								if not return_dict:
														return (prev_sample,)

								return SchedulerOutput(prev_sample=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						) ->     torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								_lowerCAmelCase										=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
														_lowerCAmelCase										=self.timesteps.to(original_samples.device		,						dtype=torch.floataa				)
														_lowerCAmelCase										=timesteps.to(original_samples.device		,						dtype=torch.floataa				)
								else:
														_lowerCAmelCase										=self.timesteps.to(original_samples.device				)
														_lowerCAmelCase										=timesteps.to(original_samples.device				)

								_lowerCAmelCase										=[self.index_for_timestep(__A		,						__A				) for t in timesteps]

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

								_lowerCAmelCase										=original_samples + noise * sigma
								return noisy_samples






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






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).read()      )
						if not params:
												raise ValueError(
												    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
						if not args.output.endswith('.pt'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).astype(np.floataa      )
																		if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
																								continue
																		if key_name.startswith('pasts/'      ):
																								if key_name.startswith('pasts/mlp'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser(
							    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
							parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





