

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 			a					(		UpperCAmelCase							,	UpperCAmelCase			):
						_lowercase        =     1


						@register_to_config
						def __init__(    self  ,   A_=2000  ,   A_=0.1  ,   A_=20  ,   A_=1e-3		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										_UpperCAmelCase    :						Any							=      None
										_UpperCAmelCase    :						List[str]							=      None


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      torch.linspace(1  ,   self.config.sampling_eps  ,   A_  ,   device=A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_=None		):







										'''simple docstring'''


										if self.timesteps is None:
														raise ValueError(
														    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										_UpperCAmelCase    :						Tuple							=      (
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										_UpperCAmelCase    :						Any							=      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
										_UpperCAmelCase    :						Optional[int]							=      std.flatten()
										while len(std.shape		) < len(score.shape		):
														_UpperCAmelCase    :						Tuple							=      std.unsqueeze(-1		)
										_UpperCAmelCase    :						str							=      -score / std

										# compute
										_UpperCAmelCase    :						Optional[int]							=      -1.0 / len(self.timesteps		)

										_UpperCAmelCase    :						Dict							=      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										_UpperCAmelCase    :						Any							=      beta_t.flatten()
										while len(beta_t.shape		) < len(x.shape		):
														_UpperCAmelCase    :						Dict							=      beta_t.unsqueeze(-1		)
										_UpperCAmelCase    :						Tuple							=      -0.5 * beta_t * x

										_UpperCAmelCase    :						int							=      torch.sqrt(A_		)
										_UpperCAmelCase    :						int							=      drift - diffusion**2 * score
										_UpperCAmelCase    :						Dict							=      x + drift * dt

										# add noise
										_UpperCAmelCase    :						Dict							=      randn_tensor(x.shape  ,   layout=x.layout  ,   generator=A_  ,   device=x.device  ,   dtype=x.dtype		)
										_UpperCAmelCase    :						Optional[int]							=      x_mean + diffusion * math.sqrt(-dt		) * noise

										return x, x_mean





						def __len__(    self		):







										'''simple docstring'''


										return self.config.num_train_timesteps





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


