

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(args.tf_model_dir  ,      "parameters.json" )
				_UpperCAmelCase       :		List[str] 		=    json.loads(open(lowerCAmelCase ).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" ):
								_UpperCAmelCase       :		Union[str, Any] 		=    args.output + ".pt"
				_UpperCAmelCase       :		Dict 		=    OrderedDict()
				with tf.device("/CPU:0" ):
								_UpperCAmelCase       :		int 		=    tf.train.load_checkpoint(args.tf_model_dir )
								_UpperCAmelCase       :		int 		=    reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_UpperCAmelCase       :		Dict 		=    reader.get_tensor(lowerCAmelCase ).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" ):
																				_UpperCAmelCase       :		int 		=    int(key_name[9] )
																elif key_name.startswith("pasts/out" ):
																				_UpperCAmelCase       :		Any 		=    8
																_UpperCAmelCase       :		str 		=    "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/moe" ):
																_UpperCAmelCase       :		List[str] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/switch_gating/kernel" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/softmlp/kernel" ):
																				_UpperCAmelCase       :		List[str] 		=    "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
																				_UpperCAmelCase       :		str 		=    key_name[-9:-7]
																				for i in range(16 ):
																								_UpperCAmelCase       :		Optional[int] 		=    "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_UpperCAmelCase       :		Optional[int] 		=    (
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/mlp" ):
																_UpperCAmelCase       :		Tuple 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/p1/kernel" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p1/bias" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_UpperCAmelCase       :		Tuple 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/kernel" ):
																				_UpperCAmelCase       :		str 		=    "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/bias" ):
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/ln" ):
																_UpperCAmelCase       :		Union[str, Any] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.norm.bias" % player
																				_UpperCAmelCase       :		int 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.norm.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/att" ):
																_UpperCAmelCase       :		Optional[Any] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/qkv/kernel" ):
																				_UpperCAmelCase       :		List[Any] 		=    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_UpperCAmelCase       :		Dict 		=    state[:, 0, :, :]
																				_UpperCAmelCase       :		List[Any] 		=    state[:, 1, :, :]
																				_UpperCAmelCase       :		Tuple 		=    state[:, 2, :, :]
																				_UpperCAmelCase       :		List[str] 		=    (
																				    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
																				_UpperCAmelCase       :		int 		=    (
																				    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
																				_UpperCAmelCase       :		Tuple 		=    (
																				    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
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		List[Any] 		=    "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/o/kernel" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_UpperCAmelCase       :		Tuple 		=    (
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/an" ):
																_UpperCAmelCase       :		Optional[int] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.self_attn.norm.bias" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.norm.weight" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
												elif (
												    key_name.startswith("model/wte" )
												    or key_name.startswith("model/wpe" )
												    or key_name.startswith("model/ete" )
												):
																_UpperCAmelCase       :		Optional[Any] 		=    {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_UpperCAmelCase       :		Dict 		=    "model.%s.weight" % nlayer
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																if key_name.startswith("model/wte" ):
																				_UpperCAmelCase       :		List[str] 		=    "lm_head.weight"
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same in embedded
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/wob" ):
																_UpperCAmelCase       :		Dict 		=    "final_logits_bias"
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		str 		=    state.reshape((1, -1) )
																_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense/kernel":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.weight"
																_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense_1/bias":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.bias"
																_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
				torch.save(lowerCAmelCase  ,      args.output )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      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')
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_tf_gptsan_to_pt(args)


