






"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers

def 							snake_case (  A__		):
			random.seed(A__		)
			np.random.seed(A__		)
			torch.manual_seed(A__		)
			torch.cuda.manual_seed_all(A__		)
			# ^^ safe to call this function even if cuda is not available







class      UpperCamelCase_			:





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Iterable[torch.nn.Parameter]      ,      lowerCAmelCase_   :					float = 0.9_9_9_9      ,      lowerCAmelCase_   :					float = 0.0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Union[float, int] = 1.0      ,      lowerCAmelCase_   :					Union[float, int] = 2 / 3      ,      lowerCAmelCase_   :					Optional[Any] = None      ,      lowerCAmelCase_   :					Dict[str, Any] = None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     int:

				if isinstance(lowerCAmelCase_      ,      torch.nn.Module		):
							UpperCAmelCase_	:					Optional[Any]     	=   (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"      ,      "1.0.0"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Optional[Any]     	=   parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							UpperCAmelCase_	:					List[Any]     	=   True

				if kwargs.get("max_value"      ,      lowerCAmelCase_		) is not None:
							UpperCAmelCase_	:					Dict     	=   "The `max_value` argument is deprecated. Please use `decay` instead."
							deprecate("max_value"      ,      "1.0.0"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_		)
							UpperCAmelCase_	:					str     	=   kwargs["max_value"]

				if kwargs.get("min_value"      ,      lowerCAmelCase_		) is not None:
							UpperCAmelCase_	:					Dict     	=   "The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"      ,      "1.0.0"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_		)
							UpperCAmelCase_	:					Tuple     	=   kwargs["min_value"]

				UpperCAmelCase_	:					Union[str, Any]     	=   list(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   [p.clone().detach() for p in parameters]

				if kwargs.get("device"      ,      lowerCAmelCase_		) is not None:
							UpperCAmelCase_	:					Optional[int]     	=   "The `device` argument is deprecated. Please use `to` instead."
							deprecate("device"      ,      "1.0.0"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_		)
							self.to(device=kwargs["device"]		)

				UpperCAmelCase_	:					Dict     	=   None

				UpperCAmelCase_	:					Tuple     	=   decay
				UpperCAmelCase_	:					Tuple     	=   min_decay
				UpperCAmelCase_	:					Optional[int]     	=   update_after_step
				UpperCAmelCase_	:					int     	=   use_ema_warmup
				UpperCAmelCase_	:					Union[str, Any]     	=   inv_gamma
				UpperCAmelCase_	:					str     	=   power
				UpperCAmelCase_	:					Dict     	=   0
				UpperCAmelCase_	:					Union[str, Any]     	=   None  # set in `step()`

				UpperCAmelCase_	:					List[str]     	=   model_cls
				UpperCAmelCase_	:					Optional[Any]     	=   model_config





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Any		)		->     "EMAModel":
				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   model_cls.load_config(lowerCAmelCase_      ,      return_unused_kwargs=lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   model_cls.from_pretrained(lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   cls(model.parameters()      ,      model_cls=lowerCAmelCase_      ,      model_config=model.config		)

				ema_model.load_state_dict(lowerCAmelCase_		)
				return ema_model





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Optional[Any]:
				if self.model_cls is None:
							raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."		)

				if self.model_config is None:
							raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."		)

				UpperCAmelCase_	:					List[Any]     	=   self.model_cls.from_config(self.model_config		)
				UpperCAmelCase_	:					str     	=   self.state_dict()
				state_dict.pop("shadow_params"      ,      lowerCAmelCase_		)

				model.register_to_config(**lowerCAmelCase_		)
				self.copy_to(model.parameters()		)
				model.save_pretrained(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					int		)		->     float:
				UpperCAmelCase_	:					str     	=   max(0      ,      optimization_step - self.update_after_step - 1		)

				if step <= 0:
							return 0.0

				if self.use_ema_warmup:
							UpperCAmelCase_	:					str     	=   1 - (1 + step / self.inv_gamma) ** -self.power
				else:
							UpperCAmelCase_	:					List[Any]     	=   (1 + step) / (10 + step)

				UpperCAmelCase_	:					int     	=   min(lowerCAmelCase_      ,      self.decay		)
				# make sure decay is not smaller than min_decay
				UpperCAmelCase_	:					Union[str, Any]     	=   max(lowerCAmelCase_      ,      self.min_decay		)
				return cur_decay_value





	@torch.no_grad()
	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Iterable[torch.nn.Parameter]		)		->     Optional[Any]:
				if isinstance(lowerCAmelCase_      ,      torch.nn.Module		):
							UpperCAmelCase_	:					Tuple     	=   (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"      ,      "1.0.0"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Any     	=   parameters.parameters()

				UpperCAmelCase_	:					Dict     	=   list(lowerCAmelCase_		)

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				UpperCAmelCase_	:					Dict     	=   self.get_decay(self.optimization_step		)
				UpperCAmelCase_	:					Optional[Any]     	=   decay
				UpperCAmelCase_	:					Optional[int]     	=   1 - decay

				UpperCAmelCase_	:					Dict     	=   contextlib.nullcontext
				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

				for s_param, param in zip(self.shadow_params      ,      lowerCAmelCase_		):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
										UpperCAmelCase_	:					List[Any]     	=   deepspeed.zero.GatheredParameters(lowerCAmelCase_      ,      modifier_rank=lowerCAmelCase_		)

							with context_manager():
										if param.requires_grad:
													s_param.sub_(one_minus_decay * (s_param - param)		)
										else:
													s_param.copy_(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Iterable[torch.nn.Parameter]		)		->     None:
				UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)
				for s_param, param in zip(self.shadow_params      ,      lowerCAmelCase_		):
							param.data.copy_(s_param.to(param.device		).data		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					Optional[int]=None		)		->     None:
				UpperCAmelCase_	:					List[Any]     	=   [
				    p.to(device=lowerCAmelCase_      ,      dtype=lowerCAmelCase_		) if p.is_floating_point() else p.to(device=lowerCAmelCase_		)
				    for p in self.shadow_params
				]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     dict:
				return {
				    "decay": self.decay,
				    "min_decay": self.min_decay,
				    "optimization_step": self.optimization_step,
				    "update_after_step": self.update_after_step,
				    "use_ema_warmup": self.use_ema_warmup,
				    "inv_gamma": self.inv_gamma,
				    "power": self.power,
				    "shadow_params": self.shadow_params,
				}





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Iterable[torch.nn.Parameter]		)		->     None:
				UpperCAmelCase_	:					int     	=   [param.detach().cpu().clone() for param in parameters]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Iterable[torch.nn.Parameter]		)		->     None:
				if self.temp_stored_params is None:
							raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"		)
				for c_param, param in zip(self.temp_stored_params      ,      lowerCAmelCase_		):
							param.data.copy_(c_param.data		)

				# Better memory-wise.
				UpperCAmelCase_	:					str     	=   None





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					dict		)		->     None:
				UpperCAmelCase_	:					List[str]     	=   copy.deepcopy(lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   state_dict.get("decay"      ,      self.decay		)
				if self.decay < 0.0 or self.decay > 1.0:
							raise ValueError("Decay must be between 0 and 1"		)

				UpperCAmelCase_	:					Optional[Any]     	=   state_dict.get("min_decay"      ,      self.min_decay		)
				if not isinstance(self.min_decay      ,      lowerCAmelCase_		):
							raise ValueError("Invalid min_decay"		)

				UpperCAmelCase_	:					Optional[Any]     	=   state_dict.get("optimization_step"      ,      self.optimization_step		)
				if not isinstance(self.optimization_step      ,      lowerCAmelCase_		):
							raise ValueError("Invalid optimization_step"		)

				UpperCAmelCase_	:					Any     	=   state_dict.get("update_after_step"      ,      self.update_after_step		)
				if not isinstance(self.update_after_step      ,      lowerCAmelCase_		):
							raise ValueError("Invalid update_after_step"		)

				UpperCAmelCase_	:					List[Any]     	=   state_dict.get("use_ema_warmup"      ,      self.use_ema_warmup		)
				if not isinstance(self.use_ema_warmup      ,      lowerCAmelCase_		):
							raise ValueError("Invalid use_ema_warmup"		)

				UpperCAmelCase_	:					Optional[int]     	=   state_dict.get("inv_gamma"      ,      self.inv_gamma		)
				if not isinstance(self.inv_gamma      ,      (float, int)		):
							raise ValueError("Invalid inv_gamma"		)

				UpperCAmelCase_	:					Tuple     	=   state_dict.get("power"      ,      self.power		)
				if not isinstance(self.power      ,      (float, int)		):
							raise ValueError("Invalid power"		)

				UpperCAmelCase_	:					Tuple     	=   state_dict.get("shadow_params"      ,      lowerCAmelCase_		)
				if shadow_params is not None:
							UpperCAmelCase_	:					Union[str, Any]     	=   shadow_params
							if not isinstance(self.shadow_params      ,      lowerCAmelCase_		):
										raise ValueError("shadow_params must be a list"		)
							if not all(isinstance(lowerCAmelCase_      ,      torch.Tensor		) for p in self.shadow_params		):
										raise ValueError("shadow_params must all be Tensors"		)











"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							snake_case (  A__		):
			return ConvertCommand(
			    args.model_type      ,args.tf_checkpoint      ,args.pytorch_dump_output      ,args.config      ,args.finetuning_task_name		)


lowerCamelCase_							      = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class      UpperCamelCase_			(__A	):





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser		)		->     Optional[Any]:
				UpperCAmelCase_	:					Any     	=   parser.add_parser(
				    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
				train_parser.add_argument("--model_type"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Model's type."		)
				train_parser.add_argument(
				    "--tf_checkpoint"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="TensorFlow checkpoint path or folder."		)
				train_parser.add_argument(
				    "--pytorch_dump_output"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Path to the PyTorch saved model output."		)
				train_parser.add_argument("--config"      ,      type=lowerCAmelCase_      ,      default=""      ,      help="Configuration file path or folder."		)
				train_parser.add_argument(
				    "--finetuning_task_name"      ,      type=lowerCAmelCase_      ,      default=lowerCAmelCase_      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
				train_parser.set_defaults(func=lowerCAmelCase_		)





	def __init__( self   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      *lowerCAmelCase_   :					Tuple      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   logging.get_logger("transformers-cli/converting"		)

				self._logger.info(f"""Loading model {model_type}"""		)
				UpperCAmelCase_	:					Optional[int]     	=   model_type
				UpperCAmelCase_	:					Union[str, Any]     	=   tf_checkpoint
				UpperCAmelCase_	:					Tuple     	=   pytorch_dump_output
				UpperCAmelCase_	:					Tuple     	=   config
				UpperCAmelCase_	:					List[str]     	=   finetuning_task_name





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				if self._model_type == "albert":
							try:
										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "bert":
							try:
										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "funnel":
							try:
										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "t5":
							try:
										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "transfo_xl":
							try:
										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
										    convert_transfo_xl_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							if "ckpt" in self._tf_checkpoint.lower():
										UpperCAmelCase_	:					Union[str, Any]     	=   self._tf_checkpoint
										UpperCAmelCase_	:					List[str]     	=   ""
							else:
										UpperCAmelCase_	:					Any     	=   self._tf_checkpoint
										UpperCAmelCase_	:					Any     	=   ""
							convert_transfo_xl_checkpoint_to_pytorch(
							    lowerCAmelCase_      ,      self._config      ,      self._pytorch_dump_output      ,      lowerCAmelCase_		)
				elif self._model_type == "gpt2":
							try:
										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
										    convert_gpta_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "xlnet":
							try:
										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
										    convert_xlnet_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name		)
				elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				else:
							raise ValueError(
							    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"		)



